Observing System Research Articles

The Aerodynamic Assessment and Shot Point Position Accuracy of UAV Seismic Source

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of the shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Prediction of atmospheric temperature in Sacramento area based on ARIMA and ETS models

Abstract. As global temperatures increased by 1.1 Celsius degrees, there has unprecedented shifts in climate systems. With the rising impact of global warming, exploring the warming trend helps to better understand and maintain the local environment and economy. This study focuses on predicting atmospheric temperature in the Sacramento area using ARIMA and ETS models. The research uses the temperature data from Sacramento Airport's Automated Surface Observing System (ASOS) and explores the prediction performance of ARIMA, ETS, and ARIMAX models in predicting daily average temperatures. The results indicate the ARIMAX (1,1,1) model is the most suitable for forecasting this temperature data with the lowest AIC and RMSE values. However, there are still challenges, in particular the dependence of the ARIMAX model on future exogenous variables, which leads to the forecast outcomes less accurate. To enable this ARIMAX models have better prediction performance, incorporating more exogenous variables is a potential solution. Therefore, the methods discussed in this paper provides ideas for the atmospheric temperature forecasting and points out the direction for further research.

Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites

With the growing popularity of leisure activities such as camping and glamping, the incidence of fires at camping sites has increased. This study focuses on improving the effectiveness of photoelectric fire alarm devices by incorporating temperature and humidity data for early fire detection in confined spaces, such as campsites. This study proposes a novel multi-sensor fire alarm system that dynamically adjusts fire detection threshold values based on temperature and humidity data collected by unmanned automatic weather observation systems. The prototype, which was implemented using Raspberry Pi and multiple sensors, demonstrated approximately 20% faster fire detection speed than existing photoelectric fire alarm systems, as verified through experiments in a simulated camping environment. The proposed approach is expected to advance fire alarm systems, enabling faster and more accurate fire detection in diverse environments, particularly at campsites.

Towards modelling instructional quality for music class-rooms: Exploring subject-specific application of the TA-LIS/GTI generic framework

Arts subjects have so far been absent from large-scale studies on instructional quality as well as from related methodological development. The purpose of this article is to explore whether an observation scheme that builds on generic theoretical models and aims to capture instructional quality across school subjects might be meaningfully employed in general music education so that it could serve as a basis for empirical research and reflective practice. We evaluated instructional quality criteria proposed in the video observation system from the study Global Teaching InSights, the third cycle of OECD’s large-scale Teaching and Learning International Survey (TALIS/GTI) by exploring the system’s observation codes for analysis of music lessons on video and comparing the criteria with quality criteria from general music education. We found that the generic models are helpful and may have more to offer both research and practice in music education than we initially expected. The risk when attempting to apply quality criteria that are intended to be generic is that those aspects may be overemphasised while criteria that are essential for music education are pushed out of sight. However, we argue that modelling instructional quality in music classrooms is worthwhile in itself and that the friction points where generic criteria do not fit our subject are particularly interesting. A systematic discussion of quality criteria can sharpen our views and understandings of general music education and connect music education research with interdisciplinary discourse and with a much-needed, broad dialogue within the educational sciences.

The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results

“Synergistic Observing Network for Ocean Prediction (SynObs)” was launched in 2022 as a project of the United Nations Decade of Ocean Science for Sustainable Development to evaluate the importance of ocean observation systems and co-design the future evolution of the ocean observing network. SynObs is currently leading the flagship OSEs/OSSEs, an internationally coordinated activity in which observing system experiments (OSEs) and observing system simulation experiments (OSSEs) are conducted using a variety of ocean and coupled atmosphere–ocean prediction systems to evaluate ocean observation impacts consistent across most prediction systems. The flagship OSEs/OSSEs comprises the ocean prediction (OP) OSEs for high-resolution ocean predictions, the subseasonal-to-seasonal (S2S) OSEs for long-term lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems. SynObs plans to use the results of the flagship OSEs to contribute to the reports on the ocean observing network design made by international organizations and projects. Here, we introduce this initiative, and we report on some initial results. Some observation impacts consistent across four ocean prediction systems are found by a preliminary analysis of the analysis runs for the OP OSEs. For example, impacts of the altimetry data on the assimilated sea surface height (SSH) field are generally large in the westerly boundary current regions and around Antarctic Circumpolar Currents where SSH has large variability but are small in the tropical regions, despite the relatively large SSH variability there. The analysis also indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. Although OSE outputs of only four ocean prediction systems are available now, we will make further investigation, adding OSE outputs of other prediction systems that will be submitted in the near future.

Data Delivery Indicators in EIDA: Designing a Consistent Metrics System in a Distributed Services Environment

The European Integrated Data Archive (EIDA) is a data service within Observatories and Research Facilities for European Seismology (ORFEUS) which distributes seismological waveform data across, presently, 12 federated data centres, so-called EIDA nodes. EIDA is also integrated into the European Research Infrastructure EPOS (European Plate Observing System). The increasing need of quantifying the scientific impact of such Research Infrastructures at all levels of operations has led EIDA to develop a unified system able to provide global indicators for data delivery across all EIDA nodes. To achieve this goal, EIDA implemented a system where the numbers are collected and aggregated by each EIDA node and ingested in a centralised database, accessible through an online API. The successful implementation of the system stems from carefully identifying the relevant indicators for well-defined groups of users. The implementation combines simplicity and robustness whilst respecting the distributed design of EIDA. The architecture and implementation choices may be of interest to other multi-site data distribution research infrastructures that face the challenge of providing meaningful key performance indicators.

The Pyrenean Platform for Observation of the Atmosphere: site, long-term dataset, and science

Abstract. The Pyrenean Platform for Observation of the Atmosphere (P2OA) is a coupled plain–mountain instrumented platform in southwestern France. It is composed of two physical sites: the “Pic du Midi” mountaintop observatory (2877 m a.s.l.) and the “Centre de Recherches Atmosphériques” (600 m a.s.l). Both sites are complementarily instrumented for the monitoring of climate-relevant variables and the study of meteorological processes in a mountainous region. The scientific topics covered by P2OA include surface–atmosphere interactions in heterogeneous landscapes and complex terrain, the physics and chemistry of atmospheric trace species at a large scale, the influence of local- and regional-scale emissions and transport on the atmospheric composition, and transient luminous events above thunderstorms. With a large number of instruments and a high hosting capacity, P2OA contributes to atmospheric sciences through (i) building long-term series of atmospheric observations, (ii) hosting experimental field campaigns and instrumental tests, and (iii) educational training in atmospheric observation techniques. In this context, P2OA is part of the French component of the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS-Fr) and also contributes to the Integrated Carbon Observation System (ICOS) research infrastructure and to several European or international networks. Here, we present the complete instrumentation of P2OA and the associated datasets, give a meteorological characterization of the platform, and illustrate the potential of P2OA and its dataset with past or ongoing studies and projects.

A global adoption of cryptocurrency and blockchain technology into the healthcare system

Abstract Background/Objectives Blockchain technology is revolutionizing various industries by enhancing security, transparency, and efficiency. In healthcare, its small-scale application demonstrates the potential to transform healthcare. This study explores how cryptocurrency and blockchain technology enhance healthcare financial stability, secure medical information exchange, increase payment recovery and interoperability. Methods Guided by the Diffusion of Innovations (DOI) Theory, this study reviews 34 articles on blockchain and cryptocurrency adoption in healthcare. We performed a thematic analysis focusing on empirical data, theoretical analyses, and existing implementations. Data were categorized by DOI elements: relative advantage, compatibility, complexity, trialability, observability, and social system. The findings were synthesized into a detailed framework for adopting these technologies in healthcare. Results Our analysis shows that cryptocurrency offers a secure, decentralized payment platform, eliminating the need for third-party intermediaries. Blockchain adoption enhances payment and patient record protection. Cryptographic methods ensure data integrity, transparency, and security, preventing modifications once recorded. Altering data requires consensus from the network majority, protecting healthcare providers from ransomware attacks. Conclusions Evidence suggests that cryptocurrency and blockchain will disrupt healthcare payments, data management, and accommodate interoperability within the next decade. These technologies promise improved security, improved public health research, and increased trust in the healthcare system. Key messages • Our research will be assessing the challenges of integrating digital coins and blockchain technology into the healthcare system. • Examine the potential benefits and risks of implementing blockchain technology in healthcare settings.

Consistency evaluation of tropospheric ozone from ozonesonde and IAGOS (In-service Aircraft for a Global Observing System) observations: vertical distribution, ozonesonde types, and station–airport distance

Abstract. The vertical distribution of tropospheric O3 from ozonesondes is compared with that from In-service Aircraft for a Global Observing System (IAGOS) measurements collected at 23 pairs of sites between about 30° S and 55° N from 1995 to 2021. Profiles of tropospheric O3 from IAGOS are generally in good agreement with ozonesonde observations from electrochemical concentration cells (ECCs), Brewer–Mast sondes, and carbon–iodine sensors, with average biases of 2.58, −0.28, and 0.67 ppb and correlation coefficients (R) of 0.72, 0.82, and 0.66, respectively. Agreement between aircraft and Indian-sonde observations is poor, with an average bias of 15.32 ppb and an R value of 0.44. The O3 concentration observed by ECC sondes is, on average, 5 %–10 % higher than that observed by IAGOS, and the relative bias increases modestly with altitude. For other sonde types, there are some seasonal and altitudinal variations in the relative bias with respect to the IAGOS measurements, but these appear to be caused by local differences. The distance between the station and airport, when within 4° (latitude and longitude), has little effect on the comparison results. For the ECC ozonesondes, the overall bias with respect to the IAGOS measurements varies from 5.7 to 9.8 ppb when the station pairs are grouped by station–airport distances of <1° (latitude and longitude), 1–2°, and 2–4°. Correlations for these groups correspond to R=0.8, 0.9, and 0.7. These comparison results provide important information for merging ozonesonde and IAGOS measurement datasets. They can also be used to evaluate the relative biases of different sonde types in the troposphere, using the aircraft as a transfer standard.

Dropsonde Data Impact on Rain Forecasts in Taiwan Under Southwesterly Flow Conditions with Observing System Simulation Experiments

Dropsonde Data Impact on Rain Forecasts in Taiwan Under Southwesterly Flow Conditions with Observing System Simulation Experiments

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

Utilization of IoT and database system of central warehouse project in the pharmaceutical industry

This study aims to develop an MRPII device that utilizes internet technology for inventory management control processes, using case studies from pharmacutical companies in Indonesia. The research was conducted using field observation methods and system development, then comparing the manual system and MRPII. An internet-based inventory management system was created by integrating the warehouse’s material database into each material code via a QR. code. The study found that implementing the MRP II system design increases work effectiveness and reduces the risk of errors that frequently occur in conventional systems. Moreover, using the MRPII can facilitate the audit process and establish transparency in the inventory management process. Data processed using IoT is generated faster and more accurately as company operations improve quality, reduce unnecessary activities, and make it easier to organize materials and run administrative and technical processes in inventory management; it is measured based on reach, workforce requirements, supply, and handling time. In the industrial world, the MRPII database system has the potential to evolve into several more complex and comprehensive systems. It has broad integration and can assist humans working across all lines, particularly in the data processing system.

Social and emotional learning in the cerebellum.

The posterior cerebellum has a critical role in human social and emotional learning. Three systems and related neural networks support this cerebellar function: a biological action observation system as part of an extended sensorimotor integration network, a mentalizing system for understanding a person's mental and emotional state subserved by a mentalizing network, and a limbic network supporting core emotional (dis)pleasure and arousal processes. In this Review, I describe how these systems and networks support social and emotional learning via functional reciprocal connections initiating and terminating in the posterior cerebellum and cerebral neocortex. It is hypothesized that a major function of the posterior cerebellum is to identify and encode temporal sequences of events, which might help to fine-tune and automatize social and emotional learning. I discuss research using neuroimaging and non-invasive stimulation that provides converging evidence for this hypothesized function of cerebellar sequencing, but also other potential functional accounts of the posterior cerebellum's role in these social and emotional processes.

Analysis of fly-around mission with spinning tether system for space station observation

This paper presents the mission scenarios of using spinning tether system to conduct space stations fly-around mission and validates its feasibility. The main challenge of fly-around mission lies in the difficulty of balancing low fuel consumption and long-term fly-around observation. To deal with this problem, a novel spinning tether system is proposed. Firstly, the fly-around process with spinning tether system is introduced, and the tether system is modeled based on Newton-Euler method with a novel description of spinning motion. Secondly, Given the unique structural limitations of space stations, two fly-around schemes and referenced fly-around trajectories are detailed. Thirdly, a backstepping controller is proposed for tracking the reference motion of fly-around satellites, and the fuel consumption among different fly-around schemes is compared and analyzed. In the end, numerical results validate that under the proposed control strategy, the spinning tether system can maintain a stable fly-around configuration in both the planar and vertical plane, the symmetrical formation configuration prevents the central space station from being affected by the motion of fly-around satellites. Moreover, energy consumption analysis indicates that tethered system can save 62.8 % of impulse compared to traditional schemes when flying in the planar plane, making it the most energy-efficient option.

On-orbit Moving Target Detection Method Based on Dual Images from Taijing-IV 01 Satellite

Abstract. With the evolution of satellite video technology, the domain has garnered increasing attention. Concurrently, advancements in deep learning have yielded numerous outcomes in target detection. This paper introduces a novel method for detecting moving targets, offering a broader detection range compared to traditional satellite video techniques, facilitating orbital target recognition from dual panchromatic image strips. Our experimental setup on the Taijing-IV 01 satellite, launched on February 27, 2022, successfully acquired two image strips separated by one second. These strips contain speed and directional information of moving objects, extractable through the frame differencing technique. We propose combining frame differencing with lightweight deep learning for target detection, extracting regions of interest (ROIs) to focus on areas with potential moving targets. This approach reduces the workload of wholeimage target detection, decreasing data processing volume by 89%. By optimizing the YOLOv8 network and using techniques like feature map fusion of low-level and high-resolution features, we enhance sensitivity to small targets. Consequently, the model size is reduced by 79%, the mean Average Precision (mAP) increases by approximately 1.8% and 4.5%, and detection speed rises by 26%. This method introduces a new paradigm in remote sensing data services, facilitating rapid acquisition and real-time transmission of positions and image information of moving targets to the ground. This significantly reduces bandwidth requirements for transmitting remote sensing information, presenting a novel strategy for data acquisition and processing in large-scale Earth observation systems and geoscientific applications.

The Influence of the Gap Phenomenon on the Occurrence of Consecutive Discharges in WEDM Through High-Speed Video Camera Observation

The Wire Electrical Discharge Machining (WEDM) process is an accurate method for manufacturing high-added-value components for industry. Continuous developments in the process have resulted in specialized machines used in sectors such as aerospace and biomedical engineering. However, some fundamental aspects of the discharge process remain unresolved. This work aims to study the influence of discharge location and bubble expansion on the occurrence of subsequent discharges. A high-speed video camera observation system was constructed to capture images of each discharge. From the acquired images, an algorithm was devised to determine the discharge location based on grayscale analysis. Moreover, the voltage and current waveforms of the discharges and the framing signals of the high-speed video camera were then obtained using an oscilloscope. Synchronizing the observation images and signals allowed for calculating the delay time for each single discharge. The results indicate that most of the discharges occurred near the boundary of the bubble and during bubble expansion. This finding has been observed for a variety of machining conditions and can be explained by the effect of the debris particles concentrated at the bubble boundary. This study provides useful information for better understanding the discharge process in WEDM.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

A Multi‐Sensor Approach for the Characterization of Tropical Cyclone Induced Swell—Application to TC Larry, 2021

AbstractSea states are likely highly complex and variable under tropical cyclone (TC) conditions. Outrunning the inner core TC vortex, swell systems can radiate in different directions with different wavelengths. Jointly using several sources of wave observations, it is demonstrated that directional properties of wave fields induced by a TC can now be very well recovered. Directional wave information extracted from Sentinel‐1 wave mode and CFOSAT SWIM are combined with Sofar Spotter drifters and NDBC buoy network. Specific filtering is applied to deal with respective limitations of these observation systems. High consistency between the different data sources is generally obtained, to provide evolving directional distributions of radiating swell systems during the whole TC life time. TC wave properties can then trace particular events associated to extended fetch effects, quantifying the asymmetrical directional wavelength distributions, as function of the TC main characteristics evolving with time (intensity, size, translation).

Towards a sustained and fit-for-purpose European ocean observing and forecasting system

The EU funded project EuroSea brought together key actors of the European ocean observing and forecasting communities with key users of the ocean observing products and services in order to better integrate existing ocean observation systems and tools, and to improve the delivery of ocean information to users. EuroSea was constructed around the ocean observing value chain that connects observations to users of ocean information, and, just as intended, the value chain concept was a useful prism to improve the system. In this article, we summarize some of the main take-home messages from EuroSea on the needs for developing the European Ocean Observing System and its links with modeling and forecasting systems. During the project, the challenges and gaps in the design and coordination of the European ocean observing and forecasting system were identified and mapped. Many gaps and challenges related to the observations of physical, chemical and biological Essential Ocean Variables were identified. Some of these gaps are related to technological developments, while others are caused by insufficient and short-term funding leading to a not sustainable system, management, and cooperation between different entities, as well as limitations in foresight activities, policies and decisions. This article represents a compilation of the broader needs for advancing the observing and forecasting system, and is meant as a guide for the community, and to funders and investors to advance ocean observing and the delivery of ocean information in Europe. To enhance the sustainability of ocean observations, which is paramount for a reliable provision of quality oceanographic data and services, several recommendations were compiled for ocean observing networks, frameworks, initiatives, as well as the ocean observing funders within the European nations, and the European Commission.