esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Using various high-quality data sets, namely, the GIMMS-3g 1/12\({}^{\circ}\) spatial resolution NDVI, CRU-TS climate (0.05\({}^{\circ}\)), and SPEI (0.05\({}^{\circ}\)), we analyzed the variation in drought characteristics, including the drought duration, intensity, and frequency, within the growing season in the northern hemisphere before and after the important time point of 1998, after which the global warming trend was stalled. Furthermore, on the basis of these analyses, we explored the impacts of the spatiotemporal variation in drought characteristics on various vegetation types. The results indicated that the annual growing season precipitation, temperature, and SPEI all showed significant (\(p>0.05\)) increases during the 1982-2015 period and that the increasing trend in temperature after 1998 was steadier and more significant than before. No obvious changes in drought characteristics were observed before 1998, but afterward, both the drought duration, drought frequency, and absolute values of drought intensity decreased significantly. Spatially, the areas with considerable wetting trend were located mainly at high latitudes, such as in northwestern Canada, the eastern United States, and a great part of central and eastern Russia. At the biome level, significant decreases in drought characteristics mainly occurred in the TUN, BF, and TCF biomes. In addition, to fully understand the potential damage to vegetation from drought, we extracted the highly drought-vulnerable areas by examining the correlation between GPP and the SPEI. We found that the highly drought-vulnerable areas were located mainly in the low latitudes and that they mainly occurred in the DXS, TGSS, and TBMF biomes, which is inconsistent with the distribution of obvious decrease in drought areas. Therefore, we suggested that although overall wetting trends were found in vegetation areas in the northern hemisphere, the low latitudes (especially the arid and semi-arid areas) still face a greater threat of drought.  
[PERSON] and [PERSON] designed the research and performed the analysis, and [PERSON] wrote the first draft. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] reviewed and edited the draft. [PERSON] and [PERSON] collected the data. All authors have read and agreed to the published version of the manuscript. This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040101, XDA19040304).  
The authors declare no conflict of interest.
## 3 Results
### SIF Data Accuracy Improvement Analysis  
A comparison between multisource SIF satellite products and site data revealed that RTSIF satellite products performed better in estimating GPP than other satellite products in arid regions, but there is still considerable room for improvement in the applicability of RTSIF across different ecosystems. In order to advance research on GPP in arid regions, it is necessary to further refine RTSIF data. Therefore, we applied two correction methods to improve the RTSIF data in arid regions, and evaluated their applicability of two methods across the three ecosystems. Ultimately, the optimal improvement method for different ecosystems is determined, revealing the overall spatial distribution characteristics of SIF in arid regions and indirectly elucidating the spatial characteristics of GPP.
## 5 Conclusions  
This study demonstrates delineated floodwater maps using optical and SAR imageries from the Landsat-7/8/9, Sentinel-2, and Sentinel-1 missions, respectively. Three popular automated algorithms, including Edge Otsu, Bmax Otsu, and Fuzzy Otsu, are compared for the 2022 Madagascar flooding event. All 30 m mNDWI maps produced by Edge Otsu, Bmax Otsu, and Fuzzy Otsu show more flooded areas of water bodies than those estimated from Sentinel-1 SAR imagery based on the same methods. Compared to Fuzzy Otsu, Edge Otsu and Bmax Otsu are more likely to generate similar results. The Fuzzy Otsu, as a more flexible method, needs additional model parameter settings that require more extensive tuning and calibration efforts to achieve desirable results.  
In addition, a DEM-based tool (FwDET) uses six different DEM data sources for water extent estimation for the same event. DEM-based maps show general consistency with the reference map and satellite-based mapping results. This study showcases that developing countries such as Madagascar will benefit from some of the advanced surface water mapping products demonstrated in this study for better flood risk assessment in the future.  
Conceptualization, Z.N.F., W.L. and D.L.; methodology, W.L. and D.L.; software, W.L.; validation, D.L. and W.L.; formal analysis, W.L., D.L. and Z.N.F.; investigation, Z.N.F., W.L. and D.L.; resources, W.L. and D.L.; data curation, W.L.; writing--original draft preparation, W.L. and D.L.; writing--review and editing, W.L. and D.L.; visualization, W.L.; supervision, Z.N.F.; project administration, Z.N.F. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study are freely accessible on Google Earth Engine ([[https://developers.google.com/earth-engine/datasets](https://developers.google.com/earth-engine/datasets)]([https://developers.google.com/earth-engine/datasets](https://developers.google.com/earth-engine/datasets)) (accessed on 1 May 2022)).  
The authors would like to thank the data supported from Google Earth Engine.  
The authors declare no conflict of interest.
# Shear Lag Effect of Ultra-Wide Box Girder under Influence of Shear Deformation
## 3 Analysis of Flexural Displacement Function of Box Girder via Energy Variational Method
\tag{63}\]  
According to the principle of the variational method and Equation (63), the control differential equation for the flexural box girder can be obtained as follows:  
\[EI_{x}{\omega^{\prime\prime}}-EI_{y\eta}g^{\prime}+M=0 \tag{64}\]  
\[G\bigl{(}A_{x}g+A_{y}g\bigr{)}-EI_{\eta}g^{\prime\prime}+EI_{y\eta}{\omega^{ \prime\prime}}=0. \tag{65}\]  
The resulting boundary conditions are as follows:  
\[\bigl{(}EI_{\eta}g^{\prime}-EI_{y\eta}{\omega^{\prime\prime}}\bigr{)}\delta g \mid_{0}^{l}=0. \tag{66}\]  
By simplifying Equations (64) and (65), we obtain the following:  
\[g^{\prime\prime}-k^{2}g=-k_{0}Q(z) \tag{67}\]  
\[{\omega^{\prime\prime}}=-\frac{M}{EI_{x}}+Bg^{\prime}. \tag{68}\]  
Here, \(B=I_{y\eta}/I_{x}\), and \(k=\sqrt{\frac{G\bigl{(}A_{x}+A_{y}\bigr{)}I_{x}}{E\bigl{(}I_{x}I_{y}-I_{y\eta} ^{2}\bigr{)}}}\) and \(k_{0}=\frac{I_{y\eta}}{E\bigl{(}I_{x}I_{y}-I_{y\eta}^{2}\bigr{)}}\) are shear parameters. By solving Equation (63), the general solution for the shear displacement is obtained as follows:  
\[g(z)=k_{0}(C_{1}{\sinh}kz+C_{2}\cosh kz+g^{*}), \tag{69}\]  
where \(g^{*}\) is the particular solution related to the shear force \(Q(z)\), and \(C_{1}\) and \(C_{2}\) are the integration constants related to the boundary conditions. According to [PERSON]'s law and Equation (61), the stress at any point on the box beam section can be expressed as follows when the influence of the shear deformation of the wing plates and webs is considered. \[\sigma(x,y,z)=E\bigl{[}-y{\omega^{\prime\prime}}(z)+\eta(x,y)g^{\prime}(z) \bigr{]}=\frac{yM(z)}{I_{x}}+\biggl{[}\eta(x,y)-\frac{yI_{y\eta}}{I_{x}}\biggr{]} Eg^{\prime}(z) \tag{70}\]  
The equation for the shear lag coefficient \(\lambda\) is as follows:  
\[\lambda=\frac{\sigma}{\overline{\sigma}}, \tag{71}\]  
where \(\sigma\) represents the actual stress on the section, and \(\overline{\sigma}\) represents the stress calculated using the elementary beam theory. By substituting Equations (6) and (70) into Equation (71), the shear lag coefficients can be expressed as follows:  
\[\lambda(x,y,z)=1+\left[\frac{\eta(x,y)}{y}-\frac{I_{yy\eta}}{I_{x}}\right]\frac{ EI_{x}g^{\prime}(z)}{M(z)}. \tag{72}\]  
The bending curvature of the girder body is expressed as follows:  
\[\omega^{\prime\prime}=-\frac{M(z)}{EI_{x}}+\frac{I_{yy}}{I_{x}}g^{\prime}. \tag{73}\]  
According to the boundary conditions, the integral constants \(C_{1}\) and \(C_{2}\) were solved to determine the expression of the shear displacement, and the theoretical solution of the flexural displacement function of the box girder was obtained by substituting the expression of the shear displacement into Equations (71)-(73).
# Deep Learning Based Airway Segmentation Using Key Point Prediction
## 3 Results
### Linear Regression Scatter Plots and Bland-Altman Plot for the Total Volume Data Set  
The total volume measured by deep learning was compared with the volume manually measured using regression analysis (Figure 5). The slopes of the two measurements were close to 1 and showed a linear regression correlation as \(r^{2}\) = 0.975, slope = 1.02, and \(p\) < 0.001. Bland-Altman plots and analyses were used to compare the total volume of the two methods, and the results are presented in Figure 6. The Bland-Altman plot comparing the level of agreement between manual and deep learning indicates an upper limit of agreement (0.261 cm\({}^{3}\)) and a lower limit of agreement (\(-\)0.207 cm\({}^{3}\)). The range of the 95% confidence interval was 0.468 cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**ICC**} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{3-4}  & & **Lower Limit** & **Upper Limit** \\ \hline Volume & & & \\ Nasopharynx & 0.912 & 0.858 & 0.946 \\ Oropharynx & 0.984 & 0.973 & 0.99 \\ Hypopharynx & 0.964 & 0.941 & 0.978 \\ Total volume & 0.986 & 0.977 & 0.992 \\ Coordinate & & & \\ PNS(x) & 0.908 & 0.852 & 0.944 \\ PNS(y) & 0.952 & 0.921 & 0.971 \\ Vp(x) & 0.908 & 0.842 & 0.946 \\ Vp(y) & 0.939 & 0.89 & 0.965 \\ CV1(x) & 0.929 & 0.885 & 0.957 \\ CV1(y) & 0.956 & 0.928 & 0.974 \\ CV2(x) & 0.963 & 0.939 & 0.978 \\ CV2(y) & 0.924 & 0.877 & 0.954 \\ CV4(x) & 0.953 & 0.924 & 0.972 \\ CV4(y) & 0.868 & 0.79 & 0.919 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Agreement analysis of the volume and point via intra-class correlation coefficient (ICC) (Two-way random effects, absolute agreement, single rater/measurement) (N = 61).  
Figure 5: Scatter plot of total volume measured between the manual of deep learning (r\({}^{2}\) = 0.975, slope = 1.02, \(p\) < 0.001). The line indicates a linear regression graph. There is a strong correlation between the two methods (N = 61).
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Architectural Overview  
The framework employs a client-server model where computational workload and security responsibilities are strategically distributed. The client is responsible for all key management, handling the generation and storage of both PQC and FHE secret keys. It also handles image preprocessing, encryption, and the final decryption of the results. The server performs the computationally intensive homomorphic operations without ever accessing plaintext data or secret keys. This design choice reflects practical deployment scenarios where untrusted cloud infrastructure must process sensitive visual data while maintaining strict privacy guarantees.  
In this model, the client transmits the encrypted image data to the server alongside a request specifying the operation to be performed (for example, \"brightness\") and any associated parameters (for example, the adjustment factor). This entire communicationis protected by the AES-GCM secure channel, safeguarding it from external network adversaries. However, the server itself receives the operation description. For applications where the nature of the computation must also be concealed from the server, more advanced FHE techniques, such as programmable bootstrapping, would be required. Such extensions are a potential avenue for future work.  
The architecture's novelty lies in its layered security approach. Rather than relying solely on homomorphic encryption for protection, we establish multiple defensive barriers: quantum-resistant key exchange prevents session key compromise, authenticated symmetric encryption ensures data integrity during transmission, and FHE provides computational privacy against the server. This redundancy ensures that, even if one layer experiences an unexpected vulnerability, the overall system security remains intact. The framework's quantum resistance is therefore twofold: CRYSTALS-Kyber protects data in transit, while the CKKS scheme, based on the hard Ring-LWE problem, protects data during computation.
## 4 Discussion
Lastly, the impact of MRI sequence variations on model generalizability was not explicitly tested. Different institutions may use different MRI acquisition protocols, which can affect image contrast, spatial resolution, and signal intensity, potentially influencing model performance on unseen data. However, our pipeline was trained using multiple MRI sequences from the same institution and included augmentations such as brightness and contrast variations, which can help improve robustness to different acquisition protocols. Additionally, all images were preprocessed using min-max normalization, ensuring a consistent intensity range across MRI scans, which can further aid in reducing variations between acquisition settings. A possible approach to further address this challenge is the use of domain adaptation techniques, such as feature normalization or style transfer methods, to align images from different protocols. Additionally, data standardization techniques, including intensity normalization and histogram matching, could help reduce variability. Future work should include validation across datasets from multiple institutions to assess generalizability and determine the most effective adaptation strategies for clinical deployment. Future research should aim to expand the dataset, particularly by increasing the representation of non-adenoma lesions and including normal patients to better assess real world performance. Additionally, external validation on an independent dataset would be valuable in confirming the pipeline's robustness across different imaging protocols and scanner manufacturers. Another important extension would be the inclusion of lipid-poor adenomas, which are more challenging to differentiate from malignant lesions and were underrepresented in our dataset. Future studies could explore integrating clinical and biochemical data alongside MRI features to enhance model interpretability and improve classification performance. Given that CT is the most widely used modality in adrenal lesion assessment, adapting this pipeline for multiphase CT images could also extend its clinical applicability. Exploring alternative deep learning architectures or fine-tuning hyperparameters through additional grid/random search comparisons could further improve classification and detection accuracy, particularly for non-adenoma lesions. Finally, future studies should include a comparative performance analysis between the pipeline and expert radiologists to further validate clinical applicability. This would involve reader studies, where radiologists classify adrenal lesions using the same dataset, allowing for a direct assessment of the model's strengths and limitations in comparison to human expertise. Such studies would help determine whether the pipeline can be a decision-support tool to enhance radiologists' diagnostic accuracy and efficiency.
## 4 Discussion
Although TAN exceeded 3.95 for a few days in the late stage, the pH was lower than 7, so the concentration of free ammonia was relatively low [28,29]. Therefore, there were no mortality events that could be attributed to high TAN values in the later stage of the culture. Studies have reported that the \"safe levels\" of nitrite nitrogen for juvenile _Litopeneus vammaei_ placed at salinities of 25% and 35% were 15.2 mg/L and 25.7 mg/L, respectively [38]. In the first 38 days of RAS culture, the concentration of nitrite nitrogen was within 1 mg/L, and it gradually increased with the increase in feed amount, reaching a maximum of 4.5 mg/L. By reducing the feed amount, nitrite nitrogen at the end of the culture process was finally reduced to approximately 2 mg/L. The system salinity fluctuated between 25 and 32 during the entire period, so nitrite nitrogen concentrations in this experiment were safely far below the limit. The procedures for _Litopeneus vammaei_ aquaculture in RASs are complex and highly controlled. In order to ensure effective water recirculation in the entire RAS, specific equipment including a device for the removal of shrimp shells, dead individuals, and sewage; microfilters; a foam fractionator; ultraviolet lamps; an ozone generator; and a liquid oxygen aerator were installed. Compared to open flow aquaculture technologies, the fluctuation of temperature, salinity, and pH during the whole recirculating aquaculture period was smaller [39]. Aeration facilities ensured the maintenance of the DO required for shrimp survival, and biofilters, UV lamps, and ozone effectively controlled the proliferation of pathogens. In particular, biofilters maintained TAN and nitrite nitrogen at safe levels.
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 1 Introduction  
Total hip arthroplasty (THA) is one of the most frequently performed orthopedic procedures worldwide, being used to treat hip osteoarthritis and hip fractures. The overall results of THAs are very good in the majority of cases, resulting in pain decrease, as well as improvement of mobility and hip function. There are two main types of THAs according to bone-implant fixation: cemented and cementless. There are still debates regarding the indications of the two methods of fixation. Generally, cemented THAs are chosen when poor bone quality is present, while cementless THAs are indicated in the case of good bone quality. Still, cementless fixation is gaining more indications in the last decade, thus becoming the most used method of fixation in the case of primary THAs. Nevertheless, there is still debate regarding the survivorship in cementless vs. cemented total hip replacements. A study on 170,413 THAs showed that cementless THAs had a higher risk of aseptic loosening compared to cemented THAs (RR = 1.5) [1].  
Complications of cementless THAs vary from mild to life-threatening ones. The most frequent late complication is represented by aseptic loosening, meaning a failure at the implant-bone interface, without the presence of infection. This complication leads to pain and functional impairment of the involved hip, severely decreasing the patient's quality of life. A key factor in preventing aseptic loosening is a good connection between the bone and the implant, a process called osseointegration.  
The treatment of aseptic loosening is generally represented by a revision total hip arthroplasty. This is a complex surgery performed in specialized centers, while the functional results are generally worse compared to a primary total hip replacement [2]. Moreover, revision surgery involves a huge cost to the healthcare system.  
Researchers and clinicians are constantly trying to discover new ways to prevent this complication and therefore increase the survivorship of total hip arthroplasties. This review aims to update both clinicians and researchers on the advances in the field of aseptic loosening prevention in the last decade.
## 2 HSI Procedure
### Hyperspectral Wavelength Selection
#### 2.3.4 Uninformative Variable Elimination (UVE)  
UVE is an effective technique for the selection of variables. It offers enhancements to improve the predictive modeling in the analysis of hyperspectral data by removing either redundant or otherwise irrelevant variables. PLS refines the regression models by evaluating the stability of the regression coefficients and eliminates unstable variables that will have low predictive power. This approach is very useful when one has to deal with a high volume of spectral information, as usually happens with HSI [89]. UVE has been widely followed to enhance the performance and increase the accuracy of the HSI models. In one of these, chestnut quality detection was made using UVE. The critical wavelengths were found near 1000, 1400, and 1600 nm. The choice of those critical target wavelengths gave a fabulous result in developing a deep learning model, FD-UVE-CNN, with a high accuracy of 97.33% that reduced recognition time massively and enhanced the performance of the model [90]. This demonstrates the capability of UVE to simplify spectral data for rapid, yet accurate qualitative analysis, especially in areas where speed is of the essence.  
Similarly, [PERSON] et al. (2020) combined UVE with the Successive Projections Algorithm (SPA) into analyzing fat and moisture content in salmon filets. The incorporation of UVE optimized the LS-SVM model through spectral refinement and improved the accuracy of the predictions at higher magnitude [91]. This case is an example of the synergism of UVE with other preprocessing methods, especially in the area of optimization and fine-tuning for models related to complex food matrices. Another case where UVE is used is in the starch content detection in maize kernels, in combination with MSC and texture features. Given the robustness for rapid detection with complications arising due to water interference, UVE definitely plays an important role in enhancing robustness when the sample conditions are adverse [92]. This makes UVE an important tool for enhanced reliability and precision in predictive models, across diverse food quality assessments.
## 5 Comparative Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Track Number** & \begin{tabular}{c} **Mask R-CNN** \\ **Restoration Rate** \\ \end{tabular} & \begin{tabular}{c} **YOLOv5-seg** \\ **Restoration Rate** \\ \end{tabular} & \
\begin{tabular}{c} **YOLACT** \\ **Restoration Rate** \\ \end{tabular} &
\begin{tabular}{c} **Improved Model** \\ **Restoration Rate** \\ \end{tabular} \\ \hline
1–1000 & 6.77\% & 5.25\% & 3.21\% & 0.51\% \\
1001–2000 & 6.81\% & 1.43\% & 4.48\% & 2.51\% \\
2001–3000 & 11.09\% & 6.48\% & 7.68\% & 3.92\% \\
3001–4000 & 5.52\% & 2.07\% & 3.28\% & 2.76\% \\
4001–5000 & 4.22\% & 1.69\% & 3.55\% & 3.72\% \\ Average Restoration Rate & 6.88\% & 3.38\% & 4.44\% & 2.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental models’ thickness estimation and restoration rate of recognition results. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Track No. Length (m)** & \begin{tabular}{c} **Signal-** \\ **Blackness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **SD of** \\ **Signal-** \\ **Based** \\ **Based** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Mask** \\ **Based** \\ **Based** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **SD of** \\ **Mask** \\ **R-CNN** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **SD of** \\ **YOLOv5-** \\ **seg (mm)** \\ \end{tabular} & \begin{tabular}{c} **SD of** \\ **YOLOv5-** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **YOLACT** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **SD of** \\ **YOLACT** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Improved** \\ **Model** \\ **(mm)** \\ \end{tabular} \\ \hline
1–1000 & 10 & 591 & 44.3 & 551 & 43.2 & 560 & 85.2 & 572 & 107.3 & 588 & 31.5 \\
1001–2000 & 10 & 558 & 48.2 & 520 & 35.7 & 550 & 20.9 & 583 & 90.8 & 544 & 38.5 \\
2001–3000 & 10 & 586 & 40.8 & 521 & 38.7 & 548 & 60.4 & 541 & 114.9 & 563 & 35.2 \\
3001–4000 & 10 & 580 & 43.3 & 548 & 41.1 & 568 & 105.1 & 599 & 116.7 & 596 & 32.6 \\
4001–5000 & 10 & 592 & 22.3 & 567 & 37.5 & 582 & 102.3 & 613 & 104.4 & 614 & 19.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thickness estimation of recognition results from comparison experiment models. Figure 7: Output results of comparative experiment models. From left to right: input GPR image, output results of Mask R-CNN, YOLOv5-seg, YOLACT, and the proposed improved model.