esa-sceva/satcom-chunk-collection huggingface dataset:
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
###### Abstract  
The Formosa Satellite-7/Constellation Observing System for Meteorology. Ionosphere, and Climate-2 (Satellite-7/COSMIC-2), which was successfully launched on 25 June 2019, provides dense radio occultation (RO) observations over the tropics and subtropics. This study examines the RO-observed lowest altitude and its possible relationship to refractivity gradients and planetary boundary layer (PBL) heights. COSMIC-2 RO data over the Southeast Pacific region (SEP) and the South-Central Pacific (SCP) from August 2020 are employed to determine their RO-observed lowest altitudes, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data are used to obtain the gradients of refractivity. Results show that there are no ray perigees below the PBL top when the vertical gradient of \(N-N(r)\) is strong (\(<\)\(-\)65 N-unit km\({}^{-1}\)), where \(N(r)\) represents the vertical profile of the spherically symmetric refractivity. Significantly strong local vertical gradients due to atmospheric ducting occur more frequently over the SEP than the SCP areas. For some cases, a strong local horizontal gradient of refractivity in the tangent direction of a ray near its perigee point can also limit the RO profile from going further below even when the vertical gradient of \(N-N(r)\) is relatively weak. Fortunately, only about 0.6% COSMIC-2 RO profiles are unaffected by the above factors but cannot observe below 2-km altitude.  
radio occultation; penetration; vertical gradient of refractivity +
Footnote †: journal: Remote Sensing  
2022  
14
# A Method for Detecting the Yarm Roll's Margin Based on VGG-UNet
###### Abstract  
The identification of the yarn roll's margin represents a critical phase in the automated production of textiles. At present, conventional visual detection techniques are inadequate for accurately measuring, filtering out background noise, and generalizing the margin of the yarn roll. To address this issue, this study constructed a semantic segmentation dataset for the yarn roll and proposed a new method for detecting the margin of the yarn roll based on deep learning. By replacing the encoder component of the U-Net with the initial 13 convolutional layers of VGG16 and incorporating pre-trained weights, we constructed a VGG-UNet model that is well suited for yarn roll segmentation. A comparison of the results obtained on the test set revealed that the model achieved an average Intersection over Union (IoU) of 98.70%. Subsequently, the contour edge point set was obtained through the application of traditional image processing techniques, and contour fitting was performed. Finally, the actual yarn roll margin was calculated based on the relationship between pixel dimensions and actual dimensions. The experiments demonstrate that the margin of the yarn roll can be accurately measured with an error of less than 3 mm. This is particularly important in situations where the margin is narrow, as the detection accuracy remains high. This study provides significant technical support and a theoretical foundation for the automation of the textile industry.  
yarm margin detection; deep learning; semantic segmentation; image processing: VGG-UNe +
Footnote †: journal: Applied  
0000-0001-9980-3888-2  
the remaining yarn regions. Subsequently, edge detection algorithms are used to extract the edge contours, which are then fitted into circles using the least squares method. Finally, the relationship between pixel dimensions and actual dimensions is established to calculate the actual yarn roll's margin.
## 10 Conclusions  
The connection design of the SCBF system evolved in the last decade to improve the seismic performance of the braced frame structure. In that process, a new connection that helps the brace member to buckle in the in-plane direction was proposed. This new connection provides solutions to some of the demerits associated with the OOPB braced system and helps in improving the performance of the structure. The work presented in this paper highlights recent advances in the design of the connection and some of the shortcomings that need further evaluation to have the desired seismic performance for the IPB braced system. The recent advances and challenges in connection design and detailing that have been highlighted in this text are as follows:  
* The mode of buckling of the IPB braced system is very closely spaced and highly dependent on the uncertainties of the brace member and the braced frame system.
* To prevent out-of-plane buckling of the braced frame system, a design criterion was developed considering the worst load case scenario along the out-of-plane buckling direction.
* The quantification of the ductility of the OOPB and IPB braced system shows that the IPB braced system has higher ductility as compared to that of the OOPB system, provided the undesirable failure mode does not govern. The rotational rigidity of the end connection influences the ductility of the system.
* In order to have optimum ductility, the clearance of the knife plate was proposed to lie between 4 tp to 6 tp.
* The absence of efficient design guidelines to prevent the failure of the interface weld is one of the major shortcomings of the newly developed connection, which requires further study.
* Quantification of the knife plate fracture should be incorporated to optimize the flexure rigidity and performance of the braced frame system.
* The critical buckling load of the knife plate also needs to be explored as the boundary condition of the knife plate is different than that of the OOPB braced frame system and expected for different effective length factors. The optimization of the shape of the knife plate can also be studied for minimization of cost.
* The associated complexity and confusion because of the Whitmore width extended inside the flange and web may be explored in future studies.  
Conceptualization, P.P. and D.R.S.; methodology, P.P.; formal analysis, P.P.; investigation, P.P.; writing--original draft preparation, P.P.; writing--review and editing, D.R.S., A.K.J. and P.P.; visualization, P.P.; supervision, D.R.S. and A.K.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.  
The financial assistance received from the Ministry of Education, Government of India (Grant no. 35-5/2017-TS-I) toward the Prime Minister Research Fellowship of the second author is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 The Improved Strategy of the ACO Algorithm
### The Heuristic Information  
The heuristic information \(\eta_{ij}\) is only related to the distance to the next hop node, which can be calculated by:  
\[\eta_{ij}=\frac{1}{d_{ij}} \tag{12}\]  
where \(d_{ij}\) denotes the distance between node \(i\) and the next hop node \(j\). Nevertheless, in UWSNs, the distance from node \(j\) to the SN also has an influence on the network energy consumption. If the next hop node \(j\) is closer to the SN, it tends to consume less energy to forward data. In addition, the energy of the next hop node also affects the balance of the energy consumption, which helps to prevent the node with low energy becoming the next hop node. Hence, this paper defines an improved strategy for heuristic information:  
\[\eta_{ij}=\frac{1}{\sigma d_{ij}+(1-\sigma)d_{js}}\times\frac{E_{\mathit{ pres}}}{E_{ini}} \tag{13}\]  
where \(\sigma\) is a constant ranging from 0 to 1, \(E_{\mathit{pres}}\) denotes the residual energy of the next hop node \(j\), \(E_{ini}\) indicates the initial energy of node \(j\), and \(d_{js}\) represents the distance from node \(j\) to the SN. From (13), we can see that the heuristic information is positively related to the residual energy of the next hop node \(j\), and is negatively correlated with the distance between node \(i\) and node \(j\) and the distance from node \(j\) to the SN. It is more likely for node \(j\) to become the next hop node if the value of the heuristic information is larger.
## 3 Results
### Post-iVRE Results for Pain Intensity and Pressure Pain Thresholds  
After six weeks of intervention, PI (NRS) showed a reduction (MD = \(-\)2.36; CI-MD [\(-\)4.15, \(-\)0.58]; Student's \(t\)-test \(p<0.05\); ES = 0.89, large). The MD exceeded the minimum clinically important difference (MCID) described for the NRS (2 points) [33], suggesting clinical relevance. For PPTs after six weeks of intervention, the results were as follows: upper trapezius (MD = \(-\)0.38, CI-MD [\(-\)0.92, 0.17]), lumbar spine (MD = \(-\)0.04, CI-MD [\(-\)0.62, 0.54]), and knee (MD = \(-\)0.13, CI-MD [\(-\)0.78, 0.52]). Repeated measures ANOVA was conducted to evaluate changes in PPTs at the upper trapezius, lumbar spine, and knee at three time points: before the intervention, after the first iVRE session (to assess the exercise-induced hypoalgesia mechanism), and at the end of the protocol. The analysis revealed no significant differences in PPTs for any of the assessed regions at any of the three time points (\(p>0.05\)). In summary, the iVRE intervention resulted in reductions in PI, surpassing the clinical importance threshold, while no changes were observed in PPTs in the assessed areas. Detailed results are presented in Table 2.
# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
###### Abstract
HBV-specific modelling studies, ranging from deterministic systems incorporating vaccination, treatment, and age structure to hybrid SARIMA-neural-network time-series predictors, demonstrate that simulation can quantify the long-term impact of scaling antiviral therapy or improving vaccine uptake [15; 16]. Given HBV's substantial morbidity, Ukraine's unique epidemiologic context, and the strategic imperative for anticipatory public health planning during the protracted conflict, there is an urgent need for reliable, locally calibrated forecasting tools. This study, therefore, develops and validates a deep recurrent neural network model to predict monthly trajectories of acute and chronic HBV in Ukraine, providing quantitative evidence to inform vaccination catch-up, diagnostic outreach, and antiviral procurement strategies. Building on these motivations, the present study makes two distinct contributions. First, it delivers the first conflict-calibrated, data-driven forecast of acute and chronic HBV for Ukraine. It shows that a mid-depth recurrent neural network can retain predictive value even when the national notification system is periodically interrupted by war-related shocks. Second, it introduces a dual-scale evaluation protocol that simultaneously assesses monthly and cumulative targets. It clarifies which forecast granularity is fit for tactical outbreak warning and which is adequate for longer-range procurement decisions, a distinction rarely examined in earlier HBV modelling work. The current research is part of a comprehensive information system for assessing the impact of emergencies on the spread of infectious diseases described in [17].
## 3 Results and Analysis
### LAI Validation for ZY-3 MUX, GF-1 WFV and HJ-1 CCD  
The LAI inversions for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD based on the proposed LAI method are shown in Figure 4. The LAI inversions from (a) to (c) were inversed from the NDVI-LAI exponential relationships in Table 5, and the LAI inversions from (d) to (f) were inversed from the NIRv-LAI exponential relationships in Table 5. The high LAI values were in the forest and cropland regions around the reservoir, whereas the low LAI values were in the wetland beach and grassland regions in the right corner and bottom of the images. Overall, the spatial variations in these three LAI inversions were similar. However, the values of the LAI inversions for the GF-1 WFV and HJ-1 CCD images were much lower, as is especially apparent in Figure 4c,e.f.  
The performances of the proposed LAI estimation method for the ZY-3 MUX, GF-1 WFV, and HJ-1 CCD data were validated based on the LAI field measurements from 17 maize samples (Figure 5). Although the R\({}^{2}\) values of the field measurements, compared with the NDVI-LAI inversionsfrom GF-1 WFV (R\({}^{2}\) = 0.66) and HJ-1 CCD (R\({}^{2}\) = 0.74), were higher than that of ZY-3 MUX (R\({}^{2}\) = 0.53), the NDVI-LAI inversions from GF-1 WFV and HJ-1 CCD tended to be systematically underestimated, especially for the higher LAI values. The RMSE and BIAS of the NDVI-LAI inversions from ZY-3 MUX (RMSE = 0.94 and BIAS = \(-\)0.73) were lower than those from GF-1 WFV (RMSE = 1.30 and BIAS = \(-\)1.19) and HJ-1 CCD (RMSE = 1.35 and BIAS = \(-\)1.22). Overall, the performance of the LAI inversed from the NDVI-LAI relationship for ZY-3 MUX was better than that from GF-1 WFV and HJ-1 CCD (Figure 5a-c). In addition, the performance of the method for LAI inversed from the NIRv-LAI relationships for GF-1 WFV and HJ-1 CCD was not sufficient, because the NIRv-LAI relationships largely depended on the reflectance of the NIR band, which was significantly underestimated in the NDVI-LAI inversions (Figure 5e,f). The performance of the method for LAI inversed from the NIRv-LAI relationships was better for ZY-3 MUX (R\({}^{2}\) = 0.62, RMSE = 0.54 and BIAS = \(-\)0.02) than for GF-1 WFV and HJ-1 CCD (Figure 5d). Because the LAI field measurements were all observed on the same day (31 July 2014), apart from one LAI value of 1.8, the other LAI values varied from 2.5 to 4.5. The concentrated LAI measurements led to small R\({}^{2}\) values between the LAI inversions and field measurements, as shown in Figure 5. Moreover, the RMSE and BIAS reflected the systematical underestimation of LAI estimations from GF-1 WFV and HJ-1 CCD.  
Figure 4: LAI inversion results for ZY-3 MUX (**a**,**d**), GF-1 WFV (**b**,**e**), and HJ-1 CCD (**c**,**f**) in Huailai, Hebei Province.
## 2 Related Work
The Chinese cryptographic algorithm uses SM2 and SM3, which are hash functions for random password operation and generation. Copyright information is embedded into the image using this encryption method. It is low cost and low risk compared with other frequency-based algorithms. [PERSON] et al. [30] proposed a block-based algorithm for tamper detection. Lifting wavelet transformation and check sum hashing algorithms are used together. The embedded pixels are selected from the least significant bits in the cover image. Several evaluation methods were used in experiments undertaken, such as the false positive, false negative and tamper-detection rates. The proposed algorithm was found to be very strong against copy paste, copy move, averaging, adding noise and image manipulations. The health sector uses electronic data frequently. Patient data can be collected, stored and shared with others using information technology. There are several medical-imaging technologies available in all hospitals. Patient data can be kept secret using watermarking and encryption algorithms for privacy purposes. [PERSON] et al. [31] discussed several spatial domain, frequency domain and cryptographic algorithms to compare their performance in both embedding and extraction. There are some limitations to using watermarking for medical images. For example, if the embedding process is applied to some of the critical parts of the image content, it might affect the doctor's decision. Discrete cosine transformation is one of the frequency-based embedding methods in image watermarking. Especially if the watermark is a pseudo-random number, the scaling factor is an important coefficient to provide balance between invisibility and robustness. [PERSON] et al. [32] proposed a flexible scaling-factor-based DCT watermarking algorithm in blocks. In this work, selected coefficients were compared against the average coefficients using flexible scaling. The PSNR and SSIM values were very high compared to the DCT algorithm. The watermarked image was resistant against several filtering, adding noise and compression attacks. [PERSON] et al. [33] proposed a reversible fragile watermarking methodology for tamper detection. In this method, two secrets are embedded into the cover image for a dual watermarking scheme. Experiments showed that the proposed method was more robust and had high capacity and transparency. The proposed methodology finds tampered regions and relocates efficiently. [PERSON] et al. [34] proposed an M-sequence and sliding window-based watermarking method for audio. This method was found to be robust and secure against large-scale cropping attacks. In this methodology, audio is converted into DWT, DCT, graph-based transform (GBT), and SVD. The secret data is applied to cover audio.
## 5 Discussion  
### Relationship between the NMR Fractal Dimension and Organic Geochemical Characteristics  
Figure 9 illustrates the relationship between the NMR fractal dimension and the organic geochemical properties of shale samples extracted from the Funing Formation in the Gaoyou sag. The results show that the two NMR fractal dimensions have different correlations with the TOC and Ro. TOC has a negative correlation with the fractal dimension \(\rm D_{Min}\) of bound-fluid pores and a positive correlation with the fractal dimension \(\rm D_{Max}\) of movable-fluid pores. During the hydrocarbon generation process from organic matter, kerogen undergoes a transformation, resulting in the production of liquid hydrocarbons and a certain quantity of gas. This transformation leads to the formation of pores within the organic matter, as well as contraction fractures that are oriented in a linear manner. These features contribute to the expansion of reservoir space and enhance connectivity to a certain degree. Therefore, TOC was positively correlated with the fractal dimension of movable-fluid pores. In [PERSON] et al.'s investigation of pore heterogeneity in the Funing Formation shale, it was observed that an increase in TOC content leads to a reduction in the number of pores and a simplification of the pore-structure complexity [7]. Consequently, there exists a negative correlation between TOC and the fractal dimension \(\rm D_{Min}\) of bound-fluid pores.  
The overall burial depth of the Funing Formation in Gaoyou Depression remains relatively constant, with \(\rm R_{O}\) values typically ranging from 0.8% to 0.9%. This consistency in burial depth and \(\rm R_{O}\) values does not exert a significant influence on the two NMR fractal dimensions.  
Figure 9: Relationship between shale NMR fractal dimension and geochemical parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{3}{c}{\(T_{2}<T_{2C}\)} & \multicolumn{3}{c}{\(T_{2}>T_{2C}\)} \\ \cline{2-7}  & **NMR Regression Equation** & \(\rm D_{Min}\) & **R-Squared** & **NMR Regression Equation** & \(\rm D_{Max}\) & **R-Squared** \\ \hline L-1 & y = 2.2906x + 3.445 & 0.7094 & 0.8933 & y = 0.0254x + 1.9318 & 2.9746 & 0.4193 \\ L-2 & y = 1.6187x + 2.4734 & 1.3813 & 0.8284 & y = 0.0207x + 1.9395
## 7 Biological Activity of Se-Polysaccharides
### Algal Se-Polysaccharides  
The antioxidant and antitumor properties of selenium polysaccharides were also investigated in algae, such as _Sargassum fusiforme_ (Harv) Setch. In mice with S\({}_{180}\) tumor, selenium-enriched polysaccharides reduced the level of malondialdehydedehyde, while increasing the activity of catalase, superoxide dismutase, and glutathione peroxidase in the blood, liver, and heart cells [385]. Additionally, Se-polysaccharides isolated from _Ulva fascita_ showed antiproliferative activity against human lung cancer cells A549 [386]. The selenium nanoparticles encapsulated in _Ulva lactuca_ polysaccharides have also been observed to reduce the symptoms of sodium dextran sulfate-induced colitis in mice [295].  
Data on Se-polysaccharides in microalgae are very limited and concern mainly selenium metabolism in these organisms, as in the work cited below [387].
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 3 Results and Discussion
### Post-Spill Impacts of Oil Spill Volume and Time Gap on Vegetation Health
The sparse and mangrove vegetation appears not to show signs of degradation within 180 days and 90 days (Figure 10b and 10c, respectively). However, the dense vegetation tends to show non-significant signs of degradation. The possible reason for the lack of evidence of degradation could be that sparse vegetation and mangrove vegetation show signs of degradation a few days after an oil spill. Mangrove stress usually occurs within the first two weeks of an oil spill event, and these signs are visible in several ways, such as chlorosis and defoliation to tree death [65]. Mangrove are very sensitive to oil, partly because oil sediments thinly cover the highly sensitive fine-feeding roots of mangrove trees [66]. Additionally, visible oil stress symptoms of vegetation depend upon the plant species type and degree of stress [67]. Each vegetation type has different biophysical properties, so their levels of resistance vary. Figure 8: The relationship between post-spill NDVI and oil spill volumes of 401–1000 bbl for (**a**) dense vegetation, (**b**) sparse vegetation, and (**c**) mangrove vegetation. Figure 7: The relationship between post-spill NDVI and oil spill volumes of 225–400 bbl for (**a**) dense vegetation, (**b**) sparse vegetation, and (**c**) mangrove vegetation. Figure 9: Relationship between post-spill NDVI and oil spill volumes greater than 1000 bbl for (**a**) dense vegetation, (**b**) sparse vegetation, and (**c**) mangrove vegetation.
## 3 Methodology
### Calibration Methods
#### 3.2.5 Spline Calibration  
Spline Calibration combines the flexibility of non-parametric methods with the smoothness of parametric approaches by modeling the calibration function \(\phi_{\text{Spline}}\) as a piecewise polynomial. This method partitions the raw score range \([0,1]\) into \(K\) intervals (knots) and fits a polynomial of degree \(d\) within each interval, constrained for continuity and smoothness at knot boundaries. For cubic splines (\(d=3\)), the calibration function takes the form:  
\[\phi_{\text{Spline}}(\hat{p}_{i})=\sum_{k=1}^{K}\beta_{k}B_{k}(\hat{p}_{i}), \tag{12}\]  
where \(B_{k}(\cdot)\) are basis functions (e.g., B-splines) and \(\beta_{k}\) are coefficients learned from \(\mathcal{D}_{\text{cal}}\).  
The optimization objective minimizes a penalized squared error:  
\[\beta^{*}=\operatorname*{arg\,min}_{\beta}\sum_{(\mathbf{x}_{j},y_{j})\in \mathcal{D}_{\text{cal}}}\left(y_{j}-\sum_{k=1}^{K}\beta_{k}B_{k}(\hat{p}_{j}) \right)^{2}+\lambda\int_{0}^{1}\left[\phi_{\text{Spline}}^{\mu}(p)\right]^{2} dp, \tag{13}\]  
where \(\lambda\geq 0\) controls the trade-off between fit and smoothness, penalizing large curvature in \(\phi_{\text{Spline}}\).  
Spline calibration adapts to diverse miscalibration patterns while avoiding the staircase artifacts of Isotonic Regression. For example, raw scores clustered near \(\hat{p}_{i}=0.8\) with an empirical positive rate of 60% can be smoothly adjusted downward without abrupt binning. The number of knots \(K\) and penalty \(\lambda\) are tuned via cross-validation on \(\mathcal{D}_{\text{cal}}\), balancing underfitting and overfitting risks.