Data Acquisition Time Research Articles

High-Throughput SFC-MS/MS Method to Measure EPSA and Predict Human Permeability.

Permeability is a key factor driving the absorption of orally administered drugs. In early discovery, the efficient evaluation of permeability, particularly for compounds violating Lipinski's Rule of 5, remains challenging. Addressing this, we established a high-throughput method to measure the experimental polar surface area (HT-EPSA) as an in vitro surrogate to measure permeability. Compared to earlier methods, HT-EPSA significantly reduces data acquisition time with enhanced sensitivity, selectivity, and data quality. In the effort of translating EPSA to human in vitro and in vivo passive permeability, we demonstrated the application of EPSA for predicting Caco-2 cell and human intestinal permeability, showing improvements over topological polar surface area and the parallel artificial membrane permeability assay for rank-ordering permeability in a proteolysis targeting chimera case study. The HT-EPSA method is expected to be highly beneficial in guiding early stage compound rank-ordering, faster decision-making, and in predicting in vitro and/or in vivo human intestinal permeability.

Optimizing Low-Dose [18F]FDG-PET/CT Scans: Ensuring Quality Amid Radiotracer Availability Challenges – Insights from a Peripheral Tertiary Care Center

Background: The introduction of positron emission tomography/computed tomography (PET/CT) has significantly advanced medical imaging. In oncology, 18F-fluorodeoxyglucose (18F-FDG) PET/CT is particularly crucial for staging, evaluating treatment response, monitoring follow-up, and planning radiotherapy. However, in resource limiting hospitals, the availability of fluorine-labeled 18F-FDG limits optimal scan acquisition. This study aims to determine the optimal dosage and acquisition time to maximize patient throughput during shortages. Aim and Objective: To optimize low-dose 18F-FDG scan protocols while maintaining high image quality despite radiotracer availability challenges. Materials and Methods: PET/CT scans were performed using GE’s Discovery IQ 5-ring, 16-slice system within 40–60 minutes of intravenous 18F-FDG injection. The protocol was adjusted to a low-dose (0.05 mCi/kg of 18F-FDG), and the PET data acquisition time was increased to 3 min per bed position to ensure image quality. Results: Notable differences were observed in image quality scores based on varying acquisition times, with the extended acquisition time helping maintain diagnostic standards despite reduced tracer doses. Conclusion: The high sensitivity and long axial length of the PET/CT system (with five rings spanning 26 cm AFOV) can significantly alleviate the challenges faced by cyclotron-dependent centers. By leveraging the increased sensitivity, we successfully reduced the injected activity rather than the scan time to address the tracer shortage at our institute. This approach proved to be effective in maintaining image quality and patient care standards.

Reanalysis of DIA Data Demonstrates the Capabilities of MS/MS-Free Proteomics to Reveal New Biological Insights in Disease-Related Samples.

Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.

Composition Profiles at the Metal Substrate-Deposit Interface Produced in Laser-Assisted Additive Manufacturing Processes.

The cross-section of various substrate-deposit metal pairs obtained with a laser-assisted additive manufacturing process has been studied by observing the composition profile with energy-dispersive spectroscopy (EDS). The EDS composition profiles observed with a sufficiently high data acquisition time revealed that the composition profile is asymmetric. By scanning toward the growth direction, a sudden composition variation was observed, which was followed by a slow decay. The character of the composition profile was the same for a number of substrate-deposit pairs, and similar trends were found in various earlier publications as well. A mathematical model for the composition variation is suggested based on the assumption that a spontaneous homogenization process takes place in the intermixing (dilution) zone of the remelted top layer of the substrate. The equation obtained makes it possible to quantitatively describe the composition profile of each component that exhibits a concentration difference between the substrate and the deposit, provided that the mole fraction difference much exceeds the scattering of the data measured. The suggested model has also been applied successfully to composition profiles published in other works, hence exhibiting general relevance. Since the variation in some physical parameters (such as hardness) along the growth direction has been reported to follow the same pattern, it is assumed that the root cause in these cases may also be the composition variation.

Fast Three-Dimensional Profilometry with Large Depth of Field.

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time-domain Gaussian fitting method is proposed in this paper. The concept of a time-domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time-domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time-domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.

Smart Vehicle Accident Prevention and Road Safety System with Real Time Data Acquisition

Smart Vehicle Accident Prevention and Road Safety System with Real Time Data Acquisition

Quick Estimation of Minimum Hearing Levels Using a Binaural Multifrequency Stimulus Paradigm: Proof of Concept.

Objective estimation of minimum hearing levels using auditory brainstem responses (ABRs) elicited by single frequency tone-bursts presented monaurally is currently considered the gold standard. However, the data acquisition time to estimate thresholds (for both ears across four audiometric frequencies) using this method usually exceeds the sleep time (ranging between 35 and 49 minutes) in infants below 4 months, thus providing incomplete information of hearing status which in turn delays timely clinical intervention. Alternate approaches using faster rate, or tone-burst trains have not been readily accepted due to additional hardware and software requirements. We propose here a novel binaural multifrequency stimulation paradigm wherein several stimuli of different frequencies are presented binaurally in an interleaved manner. The rationale here is that the proposed paradigm will increase acquisition efficiency, significantly reduce test time, and improve accuracy by incorporating an automatic wave V detection algorithm. It is important to note that this paradigm can be easily implemented in most commercial ABR systems currently used by most clinicians. Using this binaural multifrequency paradigm, ear specific ABRs were recorded in 30 normal-hearing young adults to both tone-bursts, and narrow-band (NB) iChirps at 500, 1000, 2000, and 4000 Hz. Comparison of ABRs elicited by tone-bursts and narrow-band chirps allowed us to determine if NB iChirps elicited a more robust wave V component compared with the tone-bursts. ABR data were characterized by measures of minimum hearing levels; wave V amplitude; and response detectability for two electrode configurations (high forehead-C7; and high forehead-linked mastoids). Consistent with the research literature, wave V response amplitudes were relatively more robust for NB iChirp stimuli compared with tone-burst stimuli. The easier identification and better detectability of wave V for the NB iChirps at lower stimulus levels contributed to their better thresholds compared with tone-burst elicited responses. It is important to note that binaural multifrequency hearing levels close to minimum hearing levels were determined in approximately 22 minutes using this paradigm-appreciably quicker than the 45 to 60 minutes or longer time required for threshold determination using the conventional single frequency method. Our novel and simple paradigm using either NB iChirps or tone-bursts provides a reliable method to rapidly estimate the minimum hearing levels across audiometric frequencies for both ears. Incorporation of an automatic wave V detection algorithm increases objectivity and further reduce test time and facilitate early hearing identification and intervention.

Investigation of Effective Fracture Height Using Strain Responses in a Vertical Monitoring Well during a Well Interference Test

Summary Understanding and characterizing the effective hydraulic fracture height are critical for optimizing completion design and well performance in unconventional reservoirs. Rayleigh frequency shift distributed strain sensing (RFS-DSS) data provide crucial information about the fracture geometry by measuring the strain change along the fiber. Recently, the strain data from the vertical monitoring well [VMW; Boxwood 5 Pilot Hole (B5PH)] at Hydraulic Fracture Test Site 2 (HFTS-2) was collected during a well interference test conducted on two neighboring horizontal wells [Boxwood 3H (B3H) and Boxwood 4H (B4H)] in September 2020. However, this data set was overlooked and not comprehensively analyzed. Consequently, the valuable information that this data may provide remains unexplored. The objective of this study is to determine how to interpret this vertical strain data and assess its potential to provide insights into the effective fracture height generated in the horizontal wells. To understand the measured strain data, we have developed a coupled fluid flow and geomechanics model to simulate the vertical strain responses observed during production and the well interference test. The characteristics of the strain responses are thoroughly analyzed to establish guidelines for identifying the upper and lower tips of the effective fracture in different fracture geometry scenarios. In addition, field-measured data are examined, and a history-matching process is conducted by comparing the numerical matching results with the observed data to identify the effective fracture height. Furthermore, the pressure gauge data are analyzed and compared with the strain data results to further validate the findings. The study has demonstrated that vertical strain measurements obtained from a VMW can be used to determine the effective fracture height. Large strain responses, characterized by both negative and positive peaks, are observed near the tips of the effective fractures. The upper and lower tips of the effective fracture can be identified by examining the locations of zero-strain change. The magnitude of strain response is affected by the distance between the fracture and the fiber. It is worth noting that the locations where the absolute value of the derivative of fracture conductivity changes abruptly correspond to the positions of the fracture tips. Furthermore, we study and history match the field-measured vertical strain change in HFTS-2 data sets. The pressure gauge data are analyzed to further validate our findings. The effective fracture height determined from the strain measurements falls within the range of 11,360–11,616 ft, which is consistent with the values obtained from pressure gauges (11,360–11,635 ft). Specifically, for B4H, the effective fracture height was determined to be 256 ft at a distance of 330 ft away at the time of data acquisition. Overall, the strain measurements provide valuable, time-dependent insights into the effective fracture height and the active drainage interval.

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm.

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D; x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), rely on a tilting of the sample plane with respect to the light sheet of 30-45 degrees to ease sample preparation. Data from such tilted-sample-plane LSFMs require subsequent deskewing and rotation for proper visualization and analysis. Such transformations currently demand substantial memory allocation. This poses computational challenges, especially with large datasets. The consequence is long processing times compared to data acquisition times, which currently limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a novel GPU-accelerated memory-efficient algorithm that integrates deskewing and rotation into a single transformation, significantly reducing memory requirements and reducing the preprocessing run time by at least 10-fold for large image stacks. Benchmarked against conventional methods and existing software, our approach demonstrates linear scalability. Processing large 3D stacks of up to 15 GB is now possible within one minute using a single GPU with 24 GB of memory. Applied to 4D LLSM datasets of human hepatocytes, human lung organoid tissue, and human brain organoid tissue, our method outperforms alternatives, providing rapid, accurate preprocessing within seconds. Importantly, such processing speeds now allow visualization of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on-the-fly data processing, visualization, and analysis on standard workstations, thereby revolutionizing biological imaging applications for LLSM, SPIM and similar light microscopes.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics.

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Renormalizing the high-harmonic interference via double-slit and grating

A method to utilize high-harmonic interference in Young’s double-slit followed by a grating holds promise for precisely measuring the refractive-index spectrum in the extreme ultraviolet (EUV) region. The measurement is currently bottlenecked by the time it takes to fit the Fresnel interference patterns. The conventional fitting procedure, involving multiple numerical integrals for evaluating the EUV propagation through the slit and grating, takes ≳30min on a standard laptop computer, while the data acquisition time is ∼200s. Here, we apply an analytic Gaussian integral and bypass one of the numerical integrals associated with the grating. The analysis reduces to evaluating a renormalized EUV propagation in a simple double-slit interferometer, and the estimated fitting time on a laptop becomes as short as 112 s. Our study enables real-time analysis during measurements, facilitating mass optical-data collection of EUV lithography materials.

Enhancing online dose distribution reconstruction in radiotherapy through sparse sampled rotating fiber arrays and deep learning

A optical fiber array dosimeter based on radiation-induced luminescence can be used in two-dimensional dose distribution measurements. However, using rotating fiber arrays introduces challenges in terms of online monitoring and variations in detector performance due to the long data acquisition time. This study aims to propose and demonstrate a novel method for online fast reconstruction of two-dimensional dose distributions in radiation therapy using projection data obtained from sparsely sampled rotating fiber arrays. The projection data were processed in both the projection and image domains to achieve restoration and noise reduction by deep learning techniques. The performance of the proposed method was evaluated using simulation data obtained by Monte Carlo toolkit Geant4 and experimental data obtained from a medical accelerator. Results showed that the method can successfully reconstruct dose distribution through sparse sampling and reduce the measurement time to one-sixth of the original. The peak signal-to-noise ratio, root mean square error, and structural similarity index were 23.72, 2.68, and 0.95, respectively. Additionally, the 3%/3 mm gamma pass rate reached 98.4% under specific irradiation conditions with a dose of at least 10% of the maximum dose. The integration of sparse sampling techniques, rotating fiber arrays, and deep learning techniques offers new possibilities for the fast and efficient reconstruction of two-dimensional dose distributions, ensuring high-quality radiotherapy outcomes.

Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

In materials science, traditional techniques for analyzing layered structures are essential for obtaining information about local structure, electronic properties and chemical states. While valuable, these methods often require high vacuum environments and have limited depth profiling capabilities. The grazing exit x-ray absorption near-edge structure (GE-XANES) technique addresses these limitations by providing depth-resolved insight at ambient conditions, facilitating in situ material analysis without special sample preparation. However, GE-XANES is limited by long data acquisition times, which hinders its practicality for various applications. To overcome this, we have incorporated Bayesian optimization (BO) into the GE-XANES data acquisition process. This innovative approach potentially reduces measurement time by a factor of 50. We have used a standard GE-XANES experiment, which serve as reference, to validate the effectiveness and accuracy of the BO-informed experimental setup. Our results show that this optimized approach maintains data quality while significantly improving efficiency, making GE-XANES more accessible to a wider range of materials science applications.

Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation

Abstract. Rooftop photovoltaic is considered as a cost-effective and environmentally friendly solution to energy challenges in urban areas. To ensure photovoltaic efficiency, it is essential to accurately estimate rooftop solar potential and deploy solar panels wisely. During the past few years, deep learning-based estimation methods have emerged and mainly rely on inferring rooftop orientations from aerial imagery. However, we note that rooftops often appear diversely when images are taken at different solar azimuths, and this can lead to orientation misclassification. To address this, we propose a robust solar potential estimation framework, mainly composed of a rooftop orientation prediction network and a bilateral solar potential estimation module. Specifically, we first classify rooftops into five orientations, i.e., east, west, south, north towards, and flat with a semantic segmentation network. Afterward, opposing orientations are merged to alleviate misclassification caused by variant data acquisition time. Eventually, we compute solar potentials based on PVGIS and a weighting scheme. Experimental results on the RID dataset demonstrate the effectiveness of our approach in improving the accuracy of solar energy estimation.

Mid-infrared optical coherence tomography with MHz axial line rate for real-time non-destructive testing

Non-destructive testing (NDT) is crucial for ensuring product quality and safety across various industries. Conventional methods, such as ultrasonic, terahertz, and x-ray imaging, have limitations in terms of probe-contact requirement, depth resolution, or radiation risks. Optical coherence tomography (OCT) is a promising alternative to solve these limitations, but it suffers from strong scattering, limiting its penetration depth. Recently, OCT in the mid-infrared (MIR) spectral region has attracted attention with a significantly lower scattering rate than in the near-infrared region. However, the highest reported A-scan rate of MIR-OCT has been 3 kHz, which requires long data acquisition time to take an image, unsatisfying industrial demands for real-time diagnosis. Here, we present a high-speed MIR-OCT system operating in the 3–4 µm region that employs the frequency-swept spectrum detection in OCT technique based on time-stretch infrared spectroscopy. By integrating a broadband femtosecond MIR pulsed laser operating at a repetition rate of 50 MHz, we achieved an A-scan rate of 1 MHz with an axial resolution of 11.6 µm, a 10 dB roll-off depth of about 700 µm, and a sensitivity of 55 dB. As a proof-of-concept demonstration, we imaged the surface of substrates covered by highly scattering paint coatings. The demonstrated A-scan rate surpasses previous state of the art by more than two orders of magnitude, paving the way for real-time NDT of industrial products, cultural assets, and structures.

Thermoelastic wave generation and its longitudinal wave propagation measurement by a microscopic optical interferometer

Laser ultrasonics is a noncontact measurement method that uses a laser-induced elastic wave source in combination with an optical surface displacement-tracking system. This study compared the performances of two optical interferometers with different characteristics when applied to measurement of pulsed thermoelastic waves. The surface displacement-tracking system was designed to measure the center of the microscopic view. A pulsed laser beam irradiated a black ink layer to generate the thermoelastic waves. The out-of-plane displacement on the axially opposite side was then measured using either a Michelson interferometer or a Sagnac interferometer. The objective lens of the system was of a type commonly used in biological observations. The Michelson interferometer estimated a maximum displacement of 0.43 nm and a maximum sound pressure of 24.7 kPa. The signal-to-noise ratios from 16 averages were 14.9 dB (Michelson interferometer) and 19.2 dB (Sagnac interferometer). Furthermore, this paper compares the performance of the numerically estimated Sagnac interferometer outputs calculated from the measured Michelson interferometer outputs with the experimentally obtained Sagnac interferometer outputs. The numerically estimated Sagnac interferometer’s output was shown to be identical to the experimentally acquired output. The Michelson interferometer requires a higher average operating frequency (i.e., it needs a longer data acquisition time), although this interferometer does offer superior displacement output linearity. This property enables calculation of the sound pressure from the displacement amplitude. These findings indicated that combination of the measurement points of the Sagnac interferometer with those of the sparsely distributed Michelson interferometer reduced the measurement time when compared with a single use of the Michelson interferometer while also maintaining the data acquisition quality.

Online evolution of a phased array for ultrasonic imaging by a novel adaptive data acquisition method

Ultrasonic imaging, using ultrasonic phased arrays, has an enormous impact in science, medicine and society and is a widely used modality in many application fields. The maximum amount of information which can be captured by an array is provided by the data acquisition method capturing the complete data set of signals from all possible combinations of ultrasonic generation and detection elements of a dense array. However, capturing this complete data set requires long data acquisition time, large number of array elements and transmit channels and produces a large volume of data. All these reasons make such data acquisition unfeasible due to the existing phased array technology or non-applicable to cases requiring fast measurement time. This paper introduces the concept of an adaptive data acquisition process, the Selective Matrix Capture (SMC), which can adapt, dynamically, to specific imaging requirements for efficient ultrasonic imaging. SMC is realised experimentally using Laser Induced Phased Arrays (LIPAs), that use lasers to generate and detect ultrasound. The flexibility and reconfigurability of LIPAs enable the evolution of the array configuration, on-the-fly. The SMC methodology consists of two stages: a stage for detecting and localising regions of interest, by means of iteratively synthesising a sparse array, and a second stage for array optimisation to the region of interest. The delay-and-sum is used as the imaging algorithm and the experimental results are compared to images produced using the complete generation-detection data set. It is shown that SMC, without a priori knowledge of the test sample, is able to achieve comparable results, while preforming ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}10 times faster data acquisition and achieving ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 10 times reduction in data size.

Comprehensive comparison of two models evaluating eco-environmental quality in Fangshan

It is crucial to employ scientifically sound models for assessing the quality of the ecological environment and revealing the strengths and weaknesses of ecosystems. This process is vital for identifying regional ecological and environmental issues and devising relevant protective measures. Among the widely acknowledged models for evaluating ecological quality, the ecological index (EI) and remote sensing ecological index (RSEI) stand out; however, there is a notable gap in the literature discussing their differences, characteristics, and reasons for selecting either model. In this study, we focused on Fangshan District, Beijing, China, to examine the differences between the two models from 2017 to 2021. We summarized the variations in evaluation indices, importance, quantitative methods, and data acquisition times, proposing application scenarios for both models. The results indicate that the ecological environment quality in Fangshan District, Beijing, remained favorable from 2017 to 2021. There was a discernible trend of initially declining quality followed by subsequent improvement. The variation in the calculation results is evident in the overall correlation between the RSEI and EI. Particularly noteworthy is the significantly smaller correlation between EI and the RSEI in 2021 than in the other two years. This discrepancy is attributed to shifts in the contribution of the evaluation indices within the RSEI model. The use of diverse quantitative methods for evaluating indicators has resulted in several variations. Notably, the evaluation outcomes of the EI model exhibit a stronger correlation with land cover types. This correlation contributes to a more pronounced fluctuation in RSEI levels from 2017 to 2021, with the EI model's evaluation results in 2019 notably surpassing those of the RSEI model. Ultimately, the most prominent disparities lie in the calculation results for water areas and construction land. The substantial difference in water areas is attributed to the distinct importance assigned to evaluation indicators between the two models. Moreover, the notable difference in construction land arises from the use of different quantification methods for evaluation indicators. In general, the EI model has suggested to be more comprehensive and effectively captures the annual comprehensive status of the ecological environment and the multiyear change characteristics of the administrative region. On the other hand, RSEI models exhibit greater flexibility and ease of implementation, independent of spatial and temporal scales. These findings contribute to a clearer understanding of the models' advantages and limitations, offering guidance for decision makers and valuable insights for the improvement and development of ecological environmental quality evaluation models.

Reflectance characterization of Starlink-like satellite with HELIOS brightness testing facility

The HELIOS (High Efficiency Light Intensity Observation System) project addresses the issue of excessive brightness from LEO satellite constellations. The system aims to develop a reflectance model of a satellite prior to launch, enabling the validation of compliance with astronomer guidelines, which suggest a minimum magnitude of 7. This ensures that the constellation is not visible to the naked eye and does not excessively interfere with astronomical observations. Additionally, the reflectance model obtained can be used for attitude determination by comparing the light curves with those obtained from observations. This application extends to Space Traffic Management, debris reentry prediction, and active debris removal. The system utilizes a light source consisting of 14 high-power LEDs with different wavelengths that replicate the solar spectrum. The light is positioned on an arc and can slide along a rail, allowing sampling at various angles. The arc itself can rotate on its axis, enabling the placement of the light source at any point on the hemisphere. At the center of the hemisphere, a scaled 3D printed model of the satellite under study is placed, with surface finishes resembling the actual satellite. Creating models with varying levels of detail permits to assess their impact on the reflectance model. Finally, the robotic arm Arm4NDO (Arm For Near Distance Observations) permits the movement of a high-efficiency quantum camera mounted at its end across the entire hemisphere’s surface for data acquisition. The camera records a video, stopping at points of interest for capturing frames. To strike a balance between continuous data acquisition and reasonable testing times, both the light source and capture points are positioned every 15° in elevation and azimuth. The initial test campaign aimed to determine optimal camera settings (exposure, aperture, and ISO) to achieve good model visibility and a dark background. Preliminary tests focused on Starlink’s chassis and the most significant observation configurations. Frames of interest were extracted from the videos, and their luminous flux (sum of pixel intensity values) was derived. With the obtained data, curves normalized relative to the maximum were plotted, allowing for a comparison of flux variations among different configurations.