esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Innovation, Limitations, and Prospects  
The innovation of this study lies in coupling multiple evaluation indicators (linear regression parameters, satellite spatial characteristics, GPP influencing factor responsiveness, and GPP/SIF values under different weather conditions) to comprehensively compare and analyze the applicability of four commonly used SIF satellite products for GPP estimation in arid regions, and identify which SIF satellite product is most suitable for GPP estimation in arid regions. Additionally, it verifies the feasibility of canopy and linear accuracy improvement methods for SIF accuracy improvement in arid regions based on the most suitable SIF satellite product. By integrating land use data, it also reveals the spatial distribution patterns of SIF (GPP) in arid regions. These results have not been clearly demonstrated in previous studies, thus our research fills the gap in the coupling studies of SIF and GPP in arid regions and lays a theoretical foundation for achieving the \"carbon neutrality\" goal in these areas.  
The limitation of this study is that the climate change in Xinjiang is complex and there are some uncontrollable influencing factors, which may have a certain impact on the comprehensive assessment of applicability. Other methods for improving the accuracy of SIF satellite products in arid regions need to be further developed and validated. There are often biases and uncertainties in data measurement, which result in some abnormal and outlier data, as well as incomplete and nonstationary time series data. These data have a certain impact on the operability of statistical analysis, reasonable estimation of parameters, and effective analysis of dependent variables. Although we have comprehensively processed these data in detail and reasonably, there are still subtle impacts that are inevitable. Additionally, the research results may also be influenced by the limitations of data resolution and remote sensing technology, requiring careful consideration in the application of the results.  
Based on the current research results, we will further explore models suitable for GPP inversion in arid regions in the future, coupling the improved SIF with these models to achieve GPP inversion in arid regions, thus analyzing the spatial and temporal patterns of GPP in arid regions. Additionally, we will use measured data of influencing factors such as PAR, Tair, and Tsoil, along with satellite data, to comprehensively analyze the spatial and temporal characteristics of GPP influencing factors/mechanisms in arid regions.
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 5 Conclusions
Abbreviations  
The full name  
BDS  
BeiDou Navigation Satellite System  
SCB  
Satellite Clock Bias  
ES  
Exponential Smoothing  
TNTF  
Thick Near Thin Far  
SW  
Sliding Windows  
GM  
Gray Model  
ES1  
One Exponential Smoothing  
ES2  
Two Exponential Smoothing  
ES3  
Three Exponential Smoothing  
ES + SW  
Exponential Smoothing using Sliding Windows  
ES + GM  
the combination model of the ES model and the GM  
ES3 + GM  
the combination model of the ES3 model and the GM  
ES2 + GM + SW  
the combination of the ES2 model and the GM using sliding windows  
ES3 + GM + SW  
the combination of the ES3 model and the GM using sliding windows  
GNSS  
Global Navigation Satellite System  
GPS  
Global Positioning System  
GLONAS  
GLObal NAvigation Satellite System  
PNT  
Positioning, navigation, and timing  
IGS  
International GNSS Service  
PPP  
Precise point positioning  
ARMA  
Auto-Regressive Moving Average model  
SAM  
Spectrum Analysis Model  
LSSVM  
Least Squares Support Vector Machines  
WNN  
Wavelet Neural Network  
MEO  
Medium Earth Orbit  
GEO  
Geosynchronous Earth Orbit  
IGSO  
Inclined Geosynchronous Satellite Orbit
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 5 Ocular Ischemic Syndrome
### Role of OCTA in Ocular Ischemic Syndrome  
Presently, there are limited studies detailing the role of OCTA in OIS. OCTA has been shown to be comparable to dye-based angiography in imaging other retinal vascular diseases and ischemia of the optic nerve [74]. Similarly, in OIS, OCTA has a potential role in imaging areas of capillary hypoperfusion, retinal vascular abnormalities, intraretinal fluid, and neovascularization in both the anterior and posterior segment [67]. OCTA can also delineate areas of non-perfusion in the SCP, DCP, outer retina, and choriocapillary layers, and demonstrate the loss of capillary density in the SCP and DCP (Figure 8).  
OCTA can also show the presence of collateral \"shunting\" vessels in OIS as demonstrated in the case study by [PERSON] et al. [75]. While FA gives useful information on the presence and activity of neovascularization, OCTA is effective in identifying the origin of new vessels, anatomical and topographical relationships of neovascularization with different retinal structures and how they change after intravitreal and laser treatment [75]. It should be noted, however, that outer retina findings on OCTA may be affected by artefact if inner retina ischemia is present [67].  
[PERSON] et al. have recently used OCTA to compare changes in the superficial and deep FAZ and vessel density before and after carotid artery stenting in a patient with OIS. They found that enhanced  
Figure 8: A case of ocular ischemic syndrome. **(A)**: Cross-sectional OCTA showing temporal thinning of the retina layer in the posterior pole (indicated by the yellow arrow) along with a reduced flow signal. **(B)**: Corresponding en face OCTA demonstrating reduced flow in the temporal region (blue arrows).  
blood flow after stenting was accompanied by a reduction in the FAZ area and increased vessel density in the superficial and deep layers. One of their hypotheses is that capillary blood flow slows toward the periphery of the FAZ and may fall below the threshold necessary for OCTA detection in patients with OIS. Thus, once capillary flow velocity is improved after carotid stenting, the FAZ can seem to decrease in size on OCTA in OIS. This study also shows the potential to use OCTA to track changes in individual auto segmented retinal layers for patients in order to study the impact of disease and treatment on retina vasculature [76].
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 5 Conclusions  
In this study, a detailed analysis of the PSC-FXLMS algorithm with phase error between the disturbance and command signals was conducted. The influence of the phase error on control effort, the convergence time constant and convergence behavior have been discussed in both stationary and nonstationary disturbance cases. It can be concluded, for both stationary and nonstationary disturbance, that the phase error may heavily increase the distance of the optimum vector from the initial value, leading to a large control effort, a large convergence time constant and poor convergence performance. An estimation ratio, in both stationary and nonstationary disturbance cases, of the pseudo-error MSE convergence performance has also been developed and discussed. The analytical findings and results are supported by extensive simulations.  
Conceptualization, Y.W. and F.L.; methodology, Y.W.; validation, F.L., Z.F. and L.Y.; formal analysis, L.Y.; investigation, Y.W.; resources, Y.W.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, P.W.; visualization, Y.W.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design of Novel Cooling Systems Based on Metal Plates with Channels of Shapes Inspired by Nature
## 4 Results and Discussion
### Heat Transfer
#### 4.2.2 Steady State Heat Transfer
In fact, the slope of the linear increase of the maximum variation in temperature with the thermal power input under steady state conditions for each configuration is related to the maximum distance to the channel. Depending on the application, it is possible that not only the high temperature but also the extension of the hot spots in which this high temperature occurs is relevant. To account for both the extension of the region of the plate with high temperature and the values of the high temperature, the Index of Uniform Temperature (IUT) proposed by [PERSON] et al. [49] was determined. The IUT is a measure of the average deviation of the temperature from the mean values, and is defined as follows:  
\[\text{IUT}=\frac{\iint|T-T_{\text{m}}|dA_{p}}{\iint dA_{p}} \tag{23}\]  
The variation of IUT with the heat rate input can be found in Figure 10b. The maximum values of IUT were obtained for the square configuration as a combined effect of the high temperatures and large extension of high temperature region in the plate. However, the values of IUT for the fork, crateriform, and salverform channels are comparable, even though different values for the highest temperatures and extensions of the zone of high temperature were found for these three configurations. The fork configuration is characterized by two hot spots of high temperature at the corners close to the inlet and outlet of the channel. In contrast, the crateriform configuration shows five zones of high temperature, characterized by lower values compared to the fork channel, whereas the salverform configuration presents a larger zone of high temperature at the plate center, with values also lower than the fork configuration. Regarding the cruciform configuration, the values obtained for the IUT were also lower than for the rest of cases studied, confirming again the potential of the cruciform channel to improve the cooling capability of the plate. In fact, the value of the IUT can be reduced by 34.5% using the cruciform configuration compared to the square configuration. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & \(\overline{T}\) (\(-\)) \\ \hline Square & 13.4 \\ Fork & 13.2 \\ Crateriform & 11.8 \\ Salverform & 12.0 \\ Cruciform & 10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dimensionless temperature for each configuration.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 2 Materials and Methods
### Candidate Boxes Enhanced Filtering (CEF)  
NMS is based on a greedy algorithm for selecting the best candidate boxes for objects. It filters multiple candidate boxes, retaining the one with the highest confidence while suppressing others that have a high overlap with it, thereby finding the optimal candidate box. In most existing works, NMS is applied during the detection stage, while the tracking stage relies on the detection boxes after NMS has been executed. [PERSON] [31] proposed the application of NMS in the tracking stage to assist in matching occluded object tracklets. We believe that NMS in the detection stage does not fully utilize the information generated during the tracking stage, leading to resource waste. Filtering out low-scoring and redundant boxes may hinder the effective use of information in the tracking stage, as detection aims to identify and locate all relevant objects in a single frame, while tracking involves continuously following the object's position and maintaining its identity in a video sequence. Transmitting more information to the tracking stage could potentially improve tracking performance. Therefore, we attempted to delay the NMS work until the tracking stage, using the information generated during tracking to filter detection candidate boxes, thereby enhancing information utilization and further improving tracking results.  
Figure 1: The workflow of our method: the video frames are input into a detector in which NMS does not work, and then the output is input along with the KF prediction results into the CEF module to obtain the filtered detection boxes. After that, these boxes are matched twice with the KF prediction results using DHIoU. Finally, based on the matching results, the tracklets are managed and KF-AMN updates are performed.
## 3 Results
### COLFs Inhibited the Secretion of NO and Inflammatory Factors  
LPS treatment notably increased the secretion of NO, TNF-\(\alpha\), IL-1\(\beta\), and IL-18 in RAW264.7 cells by 371.43%, 680.73%, 111.74%, and 117.15%, respectively (\(p\) < 0.01) (Figure 1B-E). COLF pretreatment significantly inhibited the LPS-mediated secretion of NO, TNF-\(\alpha\), IL-1\(\beta\), and IL-18 in RAW264.7 cells in a dose-dependent manner (\(p\) < 0.01). Compared to the LPS group, the levels of NO, TNF-\(\alpha\), IL-1\(\beta\), and IL-18 decreased by 77.27%, 40.46%, 51.98%, and 68.11%, respectively, when COLFs were administered at the highest dose. These results indicate that COLFs exhibit anti-inflammatory properties by regulating NO secretion and inflammatory factors in RAW264.7 cells.  
Figure 1: Effect of COLFs on the viability of RAW264.7 cells (**A**) and effects of COLFs on the release of NO (**B**), TNF-\(\alpha\) (**C**), IL-1\(\beta\) (**D**), and IL-18 (**E**) in LPS-stimulated RAW264.7 cells. Data are expressed as mean \(\pm\) SD (\(n\) = 3). Different symbols correspond to statistically significant differences between groups. * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001, compared with control; *** \(p\) < 0.01, *** \(p\) < 0.001, compared with LPS.
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
###### Abstract  
Diffusion is well known for its success in improving retrieval performance by exploiting the local structure of data distribution. Some recent works have focused on improving its efficiency by shifting the computing burden offline. However, we find that efficient offline diffusion handles continuously updating datasets with difficulty, which directly hinders its application in the real world. Unlike previous methods that apply diffusion to the entire gallery, we introduce an anchor graph to serve as an agent of the complete gallery graph. By doing that, we empower diffusion with the ability of retrieving newly added images at acceptable computational cost. We demonstrate that our proposed method is a good approximation of diffusion featuring fast online search speed and the ability of handling growing data. Moreover, experiments on benchmark datasets show that the proposed method outperforms the state of the art by a large margin with proper parameter settings.  
Keywords:image retrieval; online learning; computational efficiency; incremental diffusion
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 2 Measurement Principle and Simulation
### Calculation of the Direct Fit  
Traditional time constant methods typically employ exponential fitting for calculations, which involves nonlinear fitting and tends to be computationally complex. In this paper, a direct fitting approach is adopted, utilizing linear fitting of the proportionality coefficient to determine the capacitance. This method reduces the complexity of fitting and significantly improves computational efficiency. The difference of the response curve is proportional to the difference between the input and the output, so the capacitance can be calculated by simply fitting and calculating the scaling factor \(k\). The accuracy of the calculation of the proportionality coefficient \(k\) determines the accuracy of the measurement of the capacitance value of the capacitor when the time interval \(\Delta\) is constant.  
The difference between the sampled square wave function and the analog response function is made, and the difference \(y_{1}(n)\) is:  
\[y_{1}(n)=V_{i}(n)-V_{o}(n) \tag{14}\]  
A weighted average of the differences yields the processed data \(y_{2}(n)\) as:  
\[y_{2}(n)=[y_{1}(n+1)+y_{1}(n)]/2 \tag{15}\]  
The differential processing of the analog voltage signal with RC time constant yields the equation:  
\[z_{1}(n)=V_{o}(n+1)-V_{o}(n) \tag{16}\]  
Using the sampled n discrete data brought into the Kirchhoff's law equation, the following relationship can be recursively introduced:  
\[C\frac{z_{1}(n)}{\Delta}=\frac{y_{2}(n)}{R} \tag{17}\]  
\[z_{1}(n)\frac{CR}{\Delta}=y_{2}(n) \tag{18}\]  
\[k=\frac{z_{1}(n)}{y_{2}(n)} \tag{19}\]  
The connection between k and \(y_{2}(n)\) and \(z_{1}(n)\) can be expressed from the above equation, i.e., the proportional coefficient k is solved for by the sampled square wave signal and the analog voltage signal with RC time constant.  
A linear fit is made to k and \(\varepsilon_{n}\) is the error corresponding to each point.  
\[z_{1}(n)-ky_{2}(n)=\varepsilon_{n} \tag{20}\]  
According to the principle of linear fitting least squares [25; 26], the value of k after fitting can be obtained when the sum of squares of the errors at each point is minimized, as shown in Equation (21).  
\[k=\frac{\sum_{0}^{n}z_{1}(i)y_{2}(i)}{\sum_{0}^{n}y_{2}^{2}(i)} \tag{21}\]  
The value of k obtained in this way can be used to solve for capacitance using the relationship between k and capacitance \(C\).
## 2 Materials and Methods
### Materials Characterizations  
The crystallinity of the as-prepared photocatalysts were examined by X-ray diffraction (XRD), using an X-pert powder diffractometer (PanAnalytical, Almelo, Netherlands) furnished with Cu-K\(\alpha\) (X-ray wavelength of 1.5406 A), within 20 = 10-80\({}^{\circ}\) range, scan-speed of 200 seconds/step and a step-size of 0.02\({}^{\circ}\). Morphological analysis of the samples was performed by using scanning electron microscopy (SEM, TSCAN Vega LMU, Tescan, Kohoutvoice, Czech Republic) operating at 15 kV voltage. The size estimation of the nanoparticles employing SEM images was performed using the Image J 1.53 K software. Raman and photoluminescence (PL) spectra were obtained using an InVia Raman microscope (Renishaw, Wotton-under-Edge, England, UK) with a 514 nm excitation laser light source. Fourier transform infrared (FTIR) spectra were recorded for functional group analysis using a Spectrum II spectrometer by [PERSON] (Waltham, MA, USA) in the attenuated total reflectance (ATR) mode. Thermogravimetric analysis (TGA) of the samples were performed using a TGA-701 instrument (LECO, St. Joseph, MI, USA). Thermogravimetric analysis (TGA) of all samples was performed under continuous air flow (60 mL/min), heating up to 500 \({}^{\circ}\)C with a ramp of 20 \({}^{\circ}\)C/min.  
UV-Vis diffuse reflectance spectra (DRS) of the samples were recorded using a V-770, UV-Visible/NIR spectrophotometer (Jasco, Mary's court, Easton, PA, USA) within a wavelength range of 200-800 nm. Surface area of the as prepared samples was examined using a Tristar II 3020 system (Micrometrics, Norcross, GA, USA). All the samples were degassed at 150 \({}^{\circ}\)C, before analysis, and the isotherms were controlled at \(-\)196 \({}^{\circ}\)C. The exposed surface area of the samples was examined using Brunauer-Emnet-Teller (BET) method. The BET equation employed for surface area measurement of the samples is as follows [41]:  
\[S=X_{m}L_{avg}A_{m}/M_{v} \tag{1}\]  
where \(S\) = Total surface area. \(X_{m}\) = Monolayer capacity. \(L_{avg}\) = Avogadro's number. \(A_{m}\) = cross sectional area of the adsorbate. \(M_{v}\) = Molar volume of the gas absorbed.  
The electronic states of Ti, O, N and C in pure TiO\({}_{2}\) and NT-3 samples were analyzed by X-ray photoelectron spectroscopy (XPS) using Al K\(\alpha\) line as X-ray source. Peaks fitting for the XPS spectrum was done by the Gaussian method.
## 4 Data Analysis and Results
### Development of BTS Classification
Compared to the specimens required for the UCS test, the BTS test is performed on smaller specimens with a diameter of 54 mm and a thickness-to-diameter ratio of 0.5-1.0, as suggested by ISRM [36]. On the other hand, performing the UCS test is more time-consuming and expensive than the BTS test. Considering the advantages of BTS from the perspectives of the specimen preparation and test performance, BTS classification can be a good alternative for UCS classification for the quantitative-qualitative assessment of stone strength. Some deterioration processes, such as freezing-thawing, salt crystallization, heating-cooling, and wetting-drying, lead to tensile stresses in the pore space and contact boundaries between the constituent components of the stone [13; 30; 101]. Schematic illustration of  
Figure 5: Proposed BTS classification for igneous building stones. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **UCS (MPa)** & **UCS Description** \\ \hline I & \textless{}15 & Weak \\ II & 15–50 & Moderately weak \\ III & 50–120 & Strong \\ IV & 120–230 & Very strong \\ V & 230 \textless{} & Extremely strong \\ \hline \hline \end{tabular}
\end{table} 
Table 5: UCS–based classification of rocks suggested by IAEG [87]. tensile stress induced by the freezing-thawing process is shown in Figure 6. Given that the various stones have different tensile strengths, their durability against deterioration processes will differ. Assuming that all the factors affecting the durability of two stone types are the same, the stone with a higher tensile strength will exhibit better durability against tensile stresses resulting from deterioration processes. The BTS classification developed for stones has two important advantages. One is that by measuring the BTS of various stones and determining their classes according to Figure 5, the BTS can be used as an indicator to select the most suitable stone from a durability perspective. In this context, stones of classes I and V are the least durable and most durable (the lowest and the highest BTS, respectively) against deterioration processes, respectively. Second, conducting the simulation tests of the deterioration processes under laboratory conditions is time-consuming, so it takes weeks or months. In addition, in some cases, the simulation tests require many test specimens, which is costly. Therefore, the BTS-based classification introduced in the present study can be used as a practical tool for a quick evaluation of the durability of building stones against deterioration processes.