esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Reliability Analysis Method for Excavation Settlement  
Among the reliability analysis method, FORM, based on the gradient approach, estimates the probability of failure by identifying the most likely failure point in the normalized space. Its main drawback is that the derivative of the function is required to be computed to obtain the basic random variable, but it is currently impractical to quantify its derivative [62]. Comparatively, the MCS employs a random sampling method based on statistical theory to extract a set of values that satisfy the probability distribution of variables for the performance function. Multiple values of evaluation indicators under the sampling values and the probability distribution of evaluation indicators were obtained for reliability analysis. Although the results are typically considered more reliable, it has a shortcoming in terms of computational efficiency and cost [63]. Given that, this study adopted the MCS-FORM hybrid method to carry out the pit deformation reliability analysis. It employs MCS to derive the dimensionless moments of the functional function and then estimates the failure probability using the derived moments using FORM. This method takes full advantage of the effectiveness of MCS in a random sampling of geotechnical parameters and the efficiency of FORM in estimating reliability [61].  
Firstly, the performance function \(G\) for the ground settlement reliability analysis of the excavation was defined as:  
\[G=H_{\mathit{con}}-H_{\mathit{sim}} \tag{12}\]  
where, \(H_{\mathit{con}}\) is the subjective defined the controlled standard value of surface settlement of excavation, which is governed by the grade of the construction and the area where the construction is located, and \(H_{\mathit{sim}}\) is the simulated surface settlement. If \(G>0\), the surface settlement on the outside of the pit is considered to be less than the control limit thus it is stable; otherwise, it is considered to be unstable if \(G<0\).  
Based on the results of MCS, the statistics and dimensionless central moments of the function \(G\) are computed as follows:  
\[\mu_{G}=\frac{1}{N}\sum\limits_{i=1}^{N}G_{i} \tag{13}\]  
\[\sigma_{G}=\left[\frac{1}{N}\sum\limits_{i=1}^{N}(G_{i}-\mu_{G})^{2}\right]^{ \frac{1}{2}} \tag{14}\]  
\[m_{Gk}=\frac{\frac{1}{N}\sum\limits_{i=1}^{N}(G_{i}-\mu_{G})^{k}}{\sigma_{G}^{ k}},(k=3,4) \tag{15}\]  
where, \(\mu_{G}\) and \(\sigma_{G}\) denote the mean and standard deviation of \(G\), \(G_{i}\) denotes the \(G\) value of the \(i\)th sample (\(i=1,2,3,\ldots,N\)), and \(m_{Gk}\) is \(k\)th order dimensionless central moment of \(G\).  
For the performance function \(G\) herein, if the first two moments are obtained and assumed to be normally distributed, the reliability index \(\beta\) can be computed as  
\[\beta_{G}=\sigma_{G}/\mu_{G} \tag{16}\]
# Emerging Trends and Management for Sjogren Syndrome-Related Dry Eye Corneal Alterations
## 3. **Glandular Ocular Involvement: Dry Eye Disease (DED) or Keratoconjunctivitis Sicca (KCS)**  
### Histology, Biochemistry, Anatomy, and Physiology  
Several studies performed using different methods have demonstrated that patients affected by SS-related DED or KCS show various abnormalities of all ocular surface components (Table 1). The results of these studies are frequently non-univocal because of the lack of a clear distinction between pSS and sSS patients, the inclusion of patients with different disease severity degrees, and the comparison against different control groups (healthy subjects or other non-SS dry eye patients). However, they may still help in understanding the pathogenesis and clinical manifestations of the SS.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Tissue/Organ** & **Diagnostic Method** & **Findings** \\ \hline
**Salivary and lacimal glands** & **Histology**[24; 25; 26] & Infiltration of lymphocytes CD4+ T helper cells (Th1 and Th17), CD8+ T cytotoxic cells, B lymphocytes, plasma cells, macrophages, fibroblasts, and dendritic and mast cells; reduced levels of T regulatory cells \\ \hline  & & Destruction of the gland tubuloacinr architecture and atrophy in late stages \\ \hline  & & High levels of pro-inflammatory cytokines, especially interferon (IFN)-\(\gamma\) and interleukin (IL)-17 \\ \hline  & & Presence of autoantibodies, such as anti-SSA/Ro and anti-SSB/La \\ \hline  & & Presence of Epstein–Barr virus antigens \\ \hline  & & Ectopic lymphoid germinal center-like structures containing autoreactive B lymphocytes \\ \hline
**Tear film** & **Chemical and biochemical analyses**[27] & Tear film hyperosmolarity \\ \hline  & & Enhanced levels of proinflammatory cytokines (e.g., IL-1\(\alpha\), IL-1\(\beta\), IL-6, IL-8, IL-10, IL-17, IL-33, TNF-\(\alpha\), and IFN-\(\gamma\)), matrix metalloproteinase (MMP-9), and defensins \\ \hline  & & Over-expression of HLA-DR antigens \\ \hline  & & Decreased levels of lysozyme, lactoferrin, and IgA \\ \hline  & & Presence of autoantibodies, including anti-Ro/SSA, anti-La/SSB, ANA, and rheumatoid factor \\ \hline  & & Decreased levels of epidermal growth factor secreted by the lacimal gland \\ \hline  & & Decreased levels of nerve growth factor secreted by the ocular surface nerves \\ \hline  & & Decreased levels of a mucin and transforming growth factor (TGF)-\(\beta\) produced by the conjunctival goblet cells \\ \hline  & & Upregulation of several proteins related to the oxidative stress, such as the APEX1 and S100 \\ \hline
**Microbiological analyses** & & Tear film microbiome alterations, with predominant pro-inflammatory bacteria \\ \hline \end{tabular}
\end{table}
Table 1. Ocular surface histological, anatomical, and physiological features in [PERSON]’s syndrome-related dry eye disease.  
The most important findings include those discussed below.
# Delineation of the Flooded Areas in Urban Environments Based on a Simplified Approach
## 3 Case Study and Mathematical Model
### Conceptual Model  
As a first step, the urban catchments for the Tulcea municipality were generated, identifying four main basins and six secondary basins (Figure 10). The area of the main basins is 5.56 km\({}^{2}\), while the area of the secondary basins is 0.45 km\({}^{2}\). The most extended basin is BH3, having an area of 2.76 km\({}^{2}\). This basin contains most of the frequently flooded areas (most sensitive areas). The BH3 basin, due to its area, has the largest contribution to the generation of the volumes accumulated in the low area of the Tulcea municipality.  
The next step was to divide the main basins into sub-basins, having as support the sewage network layers exported from the.dwg files. The division was performed with GIS tools, imposing the condition that the sub-basins should not be larger than 1000 cells (0.65 km\({}^{2}\)). The division into sub-basins led to a realistic assessment of the flow direction imposed by the city's topography and the location of the main collectors (Figure 11).  
Figure 10: Urban catchments and frequently flooded areas.  
Figure 9: The 14 June 2016 rainfall event: (**a**) hyetograph; (**b**) rainfall mass curve.  
A detailed topographic analysis of the urban surface highlighted the natural direction of flow on the slope, represented by green arrows (Figure 12). If the carrying capacity of the collector corresponding to BH4 is exceeded, the overflow remaining on the ground surface will be directed to the sub-basins in BH3.  
In order to evaluate the volumes generated by overflow and accumulated on the land surface, four capacity curves (elevation-volume relations of the depression areas) were determined corresponding to the location of the low areas in the Danube meadow. The locations of the four flood cells are shown in Figure 13.  
Figure 11: Sub-basins and main sewage system.  
Figure 12: Detail of the flow direction in the eastern area of Tulcea city.  
Figure 14: Storage–elevation curves for depression areas: (**a**) “La Ceas” roundabout area; (**b**) Multi-purpose Hall area; (**c**) Summer cinema area; and (**d**) Paci-Isaccei-Unirii-Babadag streets area.  
Figure 13: Location of the flood areas (cells).
## 3 Analytical Methods
### Estimation of 'Main Resistance' from U-I Data  
In all our statistical representations of the voltage-current data \((U-I)\) we have identified a dominant contact resistance (in red) which we call the'main resistance'. To estimate this resistance, we have developed a method that consists in determining the equation of the straight line connecting the voltage-current pairs \((U,\ I)\) with the highest densities of occurrence. This technique involves examining the statistical maps to identify in two specific current ranges \(I_{1}\) and \(I_{2}\), one positive and the other negative, the pair \((U,\ I)\) located in each current interval and representing the highest density of occurrence. We then simply draw the straight line that passes through these two specific points on the map. The slope of this straight line corresponds to the main resistance.  
To standardize our approach independently of the currents applied, we have defined \(I_{1}=[-5\ \text{mA};-21\ \text{mA}]\) and \(I_{2}=[5\ \text{mA};\ 21\ \text{mA}]\), as shown in Figure 6.  
This method has made it possible to examine the influence of various parameters such as maximum current intensity, applied force and speed on this main contact resistance.  
Figure 6: Illustrative example of estimation of the main resistance from the \(U\)–\(I\) data, illustrated by the thick red line. In the intervals \(I_{1}\)= [\(-21\ \text{mA};-5\ \text{mA}\)] and \(I_{2}\) = [\(5\ \text{mA};\ 21\ \text{mA}\)], the most frequent \((U,\ I)\) pairs determine the main resistance, represented by the slope of this line.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 3 Preliminary  
### Federated Learning  
In FL, multiple distributed servers or devices with local data train a machine learning model without exchanging local data. Distributed servers or devices share only local model parameters obtained by training a global learning model delivered from a centralized server with local data, allowing them to participate in the training process without concern about data leakage. The centralized server aggregates locally trained model parameters to update the global model and delivers the updated global parameters to distributed servers or devices to perform the training process again. This procedure is repeated until convergence is achieved.  
According to the data distribution characteristics, FL can be categorized into horizontal federated learning, vertical federated learning, and federated transfer learning in [9]. Horizontal and vertical FL algorithms are applied when local datasets have the same feature space and different sample spaces and when local datasets have different feature spaces and the same sample space, respectively. Federated transfer learning is applied to a scenario where the local datasets have varying features and minimal overlapping samples. In this case, federated transfer learning utilizes the transfer learning techniques in [18] for FL-based systems. We consider horizontal FL in this paper. In other words, we assume that the local datasets have the same feature space and different sample spaces, and we consider an FL scenario in which many clients, including smartphones and IoT devices, participate in the training process.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Feature Fusion
#### 3.2.2 Positional Encoding  
We separate the binding relationship between input and positional encoding, and introduces relative positional bias, ensuring model flexibility while avoiding excessive computations.  
In the encoder, positional encoding represents the positional relationships between input sequences. Traditional encoders treat position information and feature sequences as a whole, performing self-attention calculations. However, this approach introduces redundant information and limiting tracking performance. This paper adopts the TUPE [33] structure, conducting correlation calculations separately for the feature sequence and positional information using parameter matrices. This reduces redundancy caused by binding. The calculation of the TUPE is illustrated in Formula (4):  
\[\alpha_{ij}=\frac{1}{\sqrt{2d}}(x_{i}^{l}W^{Q,l})(x_{j}^{l}W^{K,l})^{T}+\frac{ 1}{\sqrt{2d}}(p_{i}U^{Q})(p_{j}U^{K})^{T}+b_{j-i} \tag{4}\]  
\(\alpha_{ij}\) is a learnable parameter and can be viewed as the embedding of the relative position. \(x_{i}^{l}\), \(x_{j}^{l}\) are the input to the self-attention module in the l-th layer. \(W^{Q,l}\), \(W^{K,l}\) are their corresponding parameter matrices. \(U^{Q}\) and \(U^{K}\) map the positional sequences \(p_{i}\), \(p_{j}\) to the parameter matrices used for \(Q\) and \(K\). Dividing by \(\sqrt{2d}\) is carried out to maintain \(\mathbbm{a_{ij}}\) on the same scale, ensuring the importance of features is preserved. \(b_{j-i}\) represents the bias term related to relative position.
## 3 Results
### Content of Bioactive Compounds
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Fermentation Duration/Bioactive Compounds** & **Gallic** & **Chlorogenic** & _p-C_**oumaric** & **Ellagic** & **Benzoic** \\ \hline Control (not fermented) & 6.59 \(\pm\) 0.40 c1  & 9.26 \(\pm\)0.23 a & 121.08 \(\pm\) 5.47 e & 1507.40 \(\pm\) 73.92 b & 10.42 \(\pm\) 1.72 b \\ \hline \multicolumn{6}{c}{Aerobic fermentation method} \\ \hline Fermented 24 h & 13.57 \(\pm\) 0.38 b & 3.17 \(\pm\) 0.08 e & 237.11 \(\pm\) 7.30 c & 1630.68 \(\pm\) 57.14 a & 10.70 \(\pm\) 0.97 b \\ Fermented 48 h & 4.47 \(\pm\) 1.01 d & 9.05 \(\pm\) 0.15 a & 120.67 \(\pm\) 10.26 e & 1477.35 \(\pm\) 19.60 b & 3.81 \(\pm\) 1.13 c \\ Fermented 72 h & 5.01 \(\pm\) 0.30 d & 6.17 \(\pm\) 0.29 c & 165.92 \(\pm\) 12.33 d & 1362.84 \(\pm\) 29.15 c & 4.22 \(\pm\) 0.81 c \\ \hline \multicolumn{6}{c}{Anaerobic fermentation method} \\ \hline Fermented 24 h & 4.68 \(\pm\) 0.54 d & 7.19 \(\pm\) 0.71 b & 345.85 \(\pm\) 20.03 a & 1445.82 \(\pm\) 45.08 b & 14.89 \(\pm\) 1.95 a \\ Fermented 48 h & 15.77 \(\pm\) 0.37 a & 4.37 \(\pm\) 0.05 d & 290.61 \(\pm\) 8.64 b & 1249.77 \(\pm\) 16.74 d & 12.07 \(\pm\) 0.50 b \\ Fermented 72 h & 16.42 \(\pm\) 0.92 a & 3.58 \(\pm\) 0.03 e & 283.76 \(\pm\) 0.88 b & 1441.32 \(\pm\) 158.83 b & 15.37 \(\pm\) 0.08 a \\ \hline \multicolumn{6}{c}{\(p\)-Value (SPF 2 duration x SPF method)} & \multicolumn{1}{c}{\(<\)0.00001} & \multicolumn{1}{c}{\(<\)0.00001} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The influence of SPF on the content of phenolic acids (mg 100 g\({}^{-1}\) DW) in organic willowherb leaves. Mean value \(\pm\) standard error, \(n=3\). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Fermentation Duration/Bioactive Compounds** & **Lutein** & **Zeaxanthin** & **Beta-Carotene** \\ \hline Control (not fermented) & 19.08 \(\pm\) 1.04 d1  & 11.16 \(\pm\) 0.06 c & 6.10 \(\pm\) 0.14 b \\ \hline \multicolumn{6}{c}{Aerobic fermentation method} \\ \hline Fermented 24 h & 18.16 \(\pm\) 1.49 d & 11.57 \(\pm\) 0.06 c & 5.07 \(\pm\) 0.02 d \\ Fermented 48 h & 22.30 \(\pm\) 0.07 c & 12.27 \(\pm\) 0.06 b & 5.08 \(\pm\) 0.02 d \\ Fermented 72 h & 18.50 \(\pm\) 0.53 d & 12.12 \(\pm\) 0.17 b & 4.09 \(\pm\) 0.13 e \\ \hline \multicolumn{6}{c}{Anaerobic fermentation method} \\ \hline Fermented 24 h & 27.83 \(\pm\) 0.63 a & 13.96 \(\pm\) 0.39 a & 6.31 \(\pm\) 0.07 a \\ Fermented 48 h & 24.61 \(\pm\) 2.15 b & 12.36 \(\pm\) 0.41 b & 5.68 \(\pm\) 0.03 c \\ Fermented 72 h & 29.75 \(\pm\) 0.87 a & 14.32 \(\pm\) 0.51 a & 6.30 \(\pm\) 0.07 a \\ \hline \multicolumn{6}{c}{\(p\)-Value(SPF 2 duration x SPF method)} & \multicolumn{1}{c}{\(<\)0.00001} & \multicolumn{1}{c}{\(<\)0.00001} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The influence of SPF on the content of carotenoids (mg 100 g\({}^{-1}\) DW) in organic willowherb leaves. Mean value \(\pm\) standard error, \(n=3\).
# A Data Transmission Method with Spectral Switches via Electroabsorption
## 3 Results
Let us discuss the absolute efficiency of this method further. If we assume the incident Gaussian spectrum with 1.0 mW at the center wavelength \(\lambda_{c}\)= 843 nm, the absolute efficiency is 0.12 mW at \(\lambda\)= 848.7 nm for Figure 7a and the absolute efficiency is 0.042 mW at \(\lambda\)= 840.5 nm for Figure 7b. The output power magnitudes fall in the range from mW to uW, which can be easily detected by an available optical spectrum analyzer, such as an Anritsu MS9740B. The power is not too weak, for two reasons. First, the total length is only 150 um. Second, the shifted spectrum peaks are not very near the center wavelength \(\lambda_{c}\)= 843 nm, which is at the dip of the first curve in \[I(\lambda,d)=I(\lambda,0)\ \exp[-\alpha(\lambda)\cdot d], \tag{1}\]2 Thus, the two shifted peak wavelengths can still have enough power to be detected. As it is claimed as being a data transmission method, it is important to estimate the modulation speed. Basically, the achievable speed of our scheme depends on how fast a semiconductor multiquantum-well (MQW) electroabsorption structure can be modulated. Since the referenced work [20] does not give such information, we found another work with the same GaAs/AlGaAs quantum-well configuration studying this issue in detail [24]. The  
\[I(\lambda,d)=I(\lambda,0)\ \exp[-\alpha(\lambda)\cdot d], \tag{1}\]3 The normalized spectral distribution \(S(\lambda,d)\) of a unit Gaussian spectrum in \[I(\lambda,d)=I(\lambda,0)\ \exp[-\alpha(\lambda)\cdot d], \tag{1}\]4 (a) Red shift for \(V\) = 1.6 \(\times\) 10\({}^{4}\) V/cm. (b) Blue shift for \(V\) = 10\({}^{5}\) V/cm. (i) 1.6 \(\times\) 10\({}^{4}\) V/cm and (ii) 10\({}^{5}\) V/cm. speed depends on the capacity and the load. As seen from Table 1 in [24], a resistive load with 47 k\(\Omega\) has a 30 ns switching time and 28 ns RC time constant for a 100 \(\upmu\)m-long sample, which is a good reference to our 150 \(\upmu\)m-long sample [20]. The corresponding modulation speed is about 30 Mb/s, taking 30 ns switching time as the example. Afterwards, much higher speeds of 40 Gb/s were achieved by [PERSON] et al. [25].
## 2 Materials and Methods
### Western Blot Analysis  
Hela and SiHa cell lines were treated with 2 \(\mu\)M concentration of selected ferrocenes, and half of them were subsequently irradiated with 4 Gy of ionizing radiation. All samples were then incubated for 24 h. Cells were washed twice with ice-cold PBS, scraped off with a rubber scraper, and then lysed in nonyl phenoxypolyethoxyylethanol (NP-40) lysis buffer (150 mM NaCl, 50 mM TrisHCl pH 8.0, 5 mM NaF, 5 mM EDTA, 1% NP-40, 1:100 phosphatase inhibitor cocktail, and 1:100 protease inhibitor cocktail, both cocktails from Sigma-Aldrich, St. Louis, USA). The protein concentrations were measured via Bradford protein assay (Bio-Rad, Hercules, California, USA). A quantity of 20 \(\mu\)g of protein lysate per sample was applied and separated on 10% SDS polyacrylamide gel, then transferred onto nitrocellulose blotting membrane (Pall Life Sciences, New York, USA). The accuracy of sample loading was verified with Ponceau staining. Membranes were blocked in 5% milk with 0.1% Tween 20 in PBS and probed overnight with the following antibodies: anti \(\beta\)-actin monoclonal antibody (Sigma-Aldrich, St. Louis, Missouri, USA) served as a loading control, SQSTM1 p62 antibody (A-6) (Santa Cruz Biotechnology, Dallas, Texas, USA), and LC3B antibody (Novus Biological, Littleton, USA). Membranes were washed with PBS containing 0.1% Tween and incubated with secondary IgG antibodies SWAR-Px (Swine Anti-Rabbit Immunoglobulins- horseradish peroxidase, #P0217) and RAM-Px (Rabbit Anti-Mouse Immunoglobulins- horseradish peroxidase, #P0161) (Dako, Glostrup, Denmark) for 1 h. Positive signals were visualized with enhanced chemiluminescence reagent (ECL; Amersham Pharmacia Biotech, UK) using a G:BOX Chemi XX6 System (Syngene, Cambridge, United Kingdom). Ordinarily used chemicals were obtained from Sigma-Aldrich (St. Louis, Missouri, USA).
## 1 Introduction
This approach enhanced the rapid and accurate detection of pine wilt disease in complex terrain environments. However, there remains room for optimization in terms of computational efficiency. Despite these advancements, existing methods often focus on specific tree species and still face challenges posed by unbalanced datasets, varying illumination, and complex climatic conditions, thus limiting their applicability in accurately extracting area palms. To address these challenges, this study puts forward a novel deep learning model designed to achieve high-precision area palm extraction. First, we build a thematic dataset on the area nut distribution using imagery obtained from UAV remote sensing,laying the groundwork for automated and efficient extraction. Next, we propose a deep learning network named UniU-Net, which integrates an auxiliary encoder and a feature fusion module, thus exhibiting clear advantages when handling complex environmental backgrounds. Finally, we introduce a feature fusion mechanism known as the UAFM, which employs spatial attention to weight the input features derived from both the main and auxiliary encoders. This approach maximizes the use of effective features and significantly improves the overall segmentation performance. The main contributions of this study are as follows. 1. This study introduces a well-annotated distribution dataset for area palm trees, which can provide data support for the rapid extraction of area palm trees and related research. 2. This study proposes a deep learning model named UniU-Net for the high-precision extraction of area palm trees. The model integrates a primary-auxiliary encoder structure and a feature fusion module, which reduces the risk of overfitting during training and enhances the ability to learn multi-scale features, thereby achieving the high-precision extraction of area palm trees. 3. This study introduces a feature fusion module called the unified attention fusion module (UAFM). This module utilizes spatial attention to weight the input features from both the primary and auxiliary encoders, thereby enhancing the model's capability for multi-feature learning.