esa-sceva/satcom-chunk-collection huggingface dataset:
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
## 3 Definition of Noise Reduction Ratio for PAM Regenerators  
To evaluate all-optical regenerative performance we defined the noise reduction ratio (NRR) at each level:  
\[NRR_{l}=\sigma_{in,l}^{2}/\sigma_{out,l}^{2} \tag{11}\]  
where the normalized input noise is \(\sigma_{in,l}^{2}=E\bigg{[}\big{(}\sqrt{x}-\sqrt{x_{l}}\big{)}^{2}\bigg{]}\), and the normalized output noise is \(\sigma_{out,l}^{2}=E\bigg{[}\big{(}\sqrt{y}-\sqrt{y_{l}}\big{)}^{2}\bigg{]}\), in which \(E[\cdot]\) represents the averaging operation. Therefore, for a multi-level amplitude signal the overall NRR can be obtained:  
\[\frac{1}{NRR}=\sum_{l}^{M}\big{(}\sigma_{out,l}^{2}/M\big{)}/\sum_{l}^{M}\big{(} \sigma_{in,l}^{2}/M\big{)}=\sum_{l}^{M}\frac{w_{n,l}}{NRR_{l}} \tag{12}\]  
where \(M\) is the number of regenerative levels and \(w_{n,l}\) is the distribution ratio of the input noise power at each level. When \(w_{n,l}\) is identical for each level, i.e., \(w_{n,l}=1/M\), Equation (12) can be simplified as follows:  
\[\frac{1}{NRR}=\frac{1}{M}\sum_{l}^{M}\frac{1}{NRR_{l}} \tag{13}\]  
It should be noted that, for a single NOLM-based regenerator, the NRRs at the regenerative levels are usually unequal even though \(w_{n,l}\) is identical for each level.  
Similarly, we can also calculate the overall SNR of the input or output PAM signal, and then the SNR improvement dependent on the overall NRR is expressed as follows:  
\[\Delta SNR=\frac{SNR_{out}}{SNR_{in}}=\mathrm{g}_{s}\Delta NRR \tag{14}\]  
where \(\mathrm{g}_{s}=\frac{\sum_{l}^{M}y_{l}}{\sum_{l}^{M}x_{l}}\) is the normalized gain of an ideal PAM signal.  
For example, an optical PAM-4 signal degraded by a Gaussian white noise is input into two NOLM regenerators with different splitting ratios \(\rho=0.97\) and \(0.92\) as shown in Figure 2a,b. To achieve multiple noise suppression, the nonlinear phase factor should be \(\mathrm{\varphi}_{NL}=\gamma PL_{eff}=6.68\). For this case, the NRRs at four levels are respectively (2.25 dB, 17.11 dB, 20.56 dB, 9.67 dB) and (22.25 dB, 6.11 dB, \(-2.56\) dB, \(-9.67\) dB), corresponding to the SNR improvements of 4 dB and \(-1.68\) dB. The calculated results show that, the optimal WPs take place at the third level with \(\rho=0.97\) and the first level with \(\rho=0.92\), identical with the result given in Figure 3. It should be pointed out that the nonlinear phase factor considered here is very high. For a common HNLF with the nonlinear coefficient of 11/W/km, it requires 500 mW optical power and 2 km fiber length. However, when the extra-high nonlinear fiber, i.e., Bismuth-based HNLF [19] is used, the required power level and fiber length can be significantly dropped down to 50 mW and 112 m, respectively. Therefore, to achieve the potential application of the proposed cascaded-NOLM scheme on the regenerative transmission system, the extra-high nonlinear fiber should be adopted to reduce the operational power and the fiber length.
# Use of Vine Shoot Waste for Manufacturing Innovative Reinforced Cement Composites
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM)  
The surface topography, morphology and composition of CP and CP10 VSW samples investigated by SEM-EDX are presented in Figures 5-8. Based on the analysis of the samples' topography (Figure 5), the CP sample has a porous surface with holes and small pores, while the CP10 VSW sample contains fewer holes and no significant pore structure. Since pore size is one of the factors that influences the transport properties, allowing the harmful matter to pass into the cement's inner volume [43], several measurements were carried out to identify the type of pore present in the samples (Figure 6). According to the literature [43], four types of pores can be encountered in cement: gel pores (<10 nm), small capillary pores (10-100 nm), large capillary pores (10-1000 nm) and air holes (>several \(\upmu\)m). The size of cement pores is directly influenced by the preparation technique (air pores), the water content, and the hydration process of cement grains (gel and capillary pores). In the hydration process, the pore space between cement particles is gradually filled by hydration products reducing the pore size [44]. In the studied sample, the pores' radius measured for the CP sample varies in 28.96-307.14 \(\upmu\)m corresponding to air holes, while for CP10 VSW sample, the pores' radius varies between 2.99-5.86 \(\upmu\)m, values which are closer to large capillary pores values. The obtained results suggest a better hydration process in the case of CP10 VSW compared with the CP sample.  
Figure 4: XRD patterns of (**a**) CEM, (**b**) CP, (**c**) CP10 VSW and (**d**) VSW samples.  
Figure 5: SEM images of (**a**) CP and (**b**) CP10 VSW samples surface topography.  
Figure 6: Pores structure of (**a**) and (**b**) CP and (**c**) CP10 VSW samples.  
Figure 7: Surface topography and morphology of (**a**) CP and (**b**) CP10 VSW samples at high magnification.  
The surface topography and morphology of CP and CP10 VSW at high magnification can be observed in Figure 7. The figures show various formations in CP, including CH, CSH and calcium aluminosilicate, whereas only calcium aluminosilicate phases are observed in CP10 VSW.  
EDX was used to map the surface of the investigated samples. The maps acquired are displayed in Figure 8 and the results are presented in Table 2. The CP10 VSW map shows multiple agglomerations of Ca-based phases compared to the CP map. According to the literature, the Ca/Si ratio of CSH in neat Portland cement pastes varies from \(\sim\)1.2 to \(\sim\)2.3 while the Ca/(Si+Al) ratio of CSH in cement-slag pastes varies from \(\sim\)0.7 to \(\sim\)2.4 [45]. The results presented in Table 1 reveal that both ratios are higher in the case of CP10 VSW compared to CP and higher than the values presented above. These results suggest that the introduction of 10% vine shoot waste modifies the surface structure of the obtained cement by favoring the formation of Ca-based phases, such as CH and CaCO\({}_{3}\).
###### Abstract  
Background: Some patients with Sjogren's syndrome (SS) do not develop xerostomia despite advanced involvement of the salivary glands and the presence of focal lymphocytic sialadenitis (FLS). The aim of the study is to determine possible correlations between xerostomia, symptoms of sica syndrome, FLS, and other features in SS patients. Methods: The study group comprised 50 patients with SS. The comprehensive assessment of patients included clinical, laboratory, and serological examinations. All patients underwent labial salivary gland biopsies. Dry mouth and dry eyes were assessed by unstimulated whole salivary flow rate (USWSF) and Schirmer's test, respectively. Results: Xerostomia and xerophthalmia are closely related components of sica syndrome. Xerostomia did not correlate with any serological or laboratory values, including ANA titers, SSA, SSB, Ro52 antibodies, rheumatoid factor, C-reactive protein, and Erythrocyte Sedimentation Rate. There were no correlations between xerostomia and FLS or Focus score. USWSF results correlated with xerostomia reported by patients, contrary to [PERSON]'s test, which did not correlate with xerophthalmia. Conclusions: Dry mouth in SS is independent of any serological or inflammatory parameters. The occurrence of FLS does not determine xerostomia and its severity. Dry mouth in SS is influenced by other undetermined factors and mechanisms independent of salivary gland involvement.  
Sigoren's syndrome; xerostomia; focal lymphocytic sialadenitis; labial salivary gland biopsy; sialometry
## 2 Materials and Methods
### Characterization  
The transmission electron microscopy (TEM) and high-resolution transmission electron microscopy (HRTEM) images were obtained with a JEOL JEM2100 microscope (JEOL USA, Peabody, MA, USA) operated at 200 kV. High angle annular dark field (HAADF) and energy dispersive X-ray spectroscopy (EDS) images were acquired using a FEI TECNAI G2 F20 (The Ames Laboratory, Ames, IA, USA) operated at 200 kV. Samples for HRTEM, HAADF-STEM (High-angle annular dark-field scanning transmission electron microscopy), and EDX-STEM (Energy-dispersive X-ray spectroscopy scanning transmission electron microscopy) were prepared by drop-casting an aqueous suspension of the nanostructures over a carbon-coated copper grid, followed by drying under ambient conditions. The Au atomic percentage was measured by flame atomic absorption spectrometry (FAAS) with a Shimadzu spectrophotometer, model AA-6300 (Shimadzu Corp., Kyoto, Japan), equipped with an air-acetylene flame. Leaching tests were performed using ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometry). An Agilent 720-ES ICP-OES (Agilent Technologies, Santa Clara, CA, USA) was used for determining the elemental composition of the reactant solutions.  
Temperature-programmed reduction with hydrogen (H\({}_{\rm{2}}\)-TPR) was carried out in a Micrometrics Chemisorb 2705. Usually, 0.1 g of a catalyst was dried with N\({}_{\rm{2}}\) flow at 125 \({}^{\circ}\)C for 1 h. Then, the sample was cooled down to room temperature, and then reduced up to 1000 \({}^{\circ}\)C (ramp 5.\({}^{\circ}\)C-min\({}^{-1}\)) under H\({}_{\rm{2}}\) flow (30 mL\(\cdot\)min\({}^{-1}\)). X-ray photoelectron spectroscopy (XPS) data of the samples were obtained with a SPECSLAB II (Phoibos-Has 3500 150, 9 channeltrons) SPECS spectrometer, with Al K\(\alpha\) source (E = 1486.6 eV) operating at 12 kV, pass energy (Epass) = 40 eV, 0.1 eV energy step and an acquisition time of 1 s per point. The samples were placed on stainless-steel sample-holders, transferred under inert atmosphere to the XPS pre-chamber and held there for 2 h in a vacuum. The residual pressure inside the analysis chamber was \(\sim\)1\(\times\)10\({}^{-1}\) Torr. The binding energies (BE) of the Mn 2p, Au 4f and C 1s spectral peaks were referenced to the C 1s peak at 284.5 eV, providing accuracy within \(\pm\) 0.2 eV. Textural characteristics of the catalysts were determined from nitrogen adsorption isotherms, recorded at \(-\)196 \({}^{\circ}\)C in a Quantachrome Nova 1000. The samples (ca. 100 mg) were degassed for 3 h at 150 \({}^{\circ}\)C before analysis. Specific surface areas were determined by the Brunauer-Emmett-Teller equation (BET method) from an adsorption isotherm generated in a relative pressure range 0.07 \(<\) P/P\(o\) \(<\) 0.3. The total pore volume was calculated from the amount of N\({}_{\rm{2}}\) adsorbed at a relative pressure close to unity. The average pore diameter was determined by the Barrett-Joyner-Halenda (BJH) method, from the N\({}_{\rm{2}}\) desorption isotherms.
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 3 Results
### Functional Overview and User Interaction Design for Web Application Development  
The web application features a carefully designed graphical user interface (GUI) that prioritizes accessibility and efficient navigation. The interface is systematically divided into two primary sections: the left panel facilitates task selection and configuration, while the right panel presents the outcomes of executed tasks through an interactive map, complemented by contextual informational notifications. This structured layout is intentionally developed to enhance usability and support effective user interaction, ensuring a coherent and productive experience.  
The application comprises four (4) primary functionalities:  
1. Search Sentitel-2 images: As shown in Figure 9, users can search for satellite imagery (Sentinel-2 L2A, Surface Reflectance) based on specific parameters, including area of interest (AOI; this can be uploaded in either GeoJSON or KML format), cloud cover percentage, and date ranges. Additionally, users have the option to upload their dates  
Figure 8: Growing season plot of LAI.  
Figure 7: Growing season plot of GNDVI.  
of interest in a text format, with dates separated by commas. Moreover, users can optionally apply a cloud mask and a shadow threshold to remove cloud-covered areas and pixels affected by cloud shadows.  
2. Vegetation index calculator: This feature enables users to select and compute various spectral indices from the chosen Sentinel-2 images, Figure 10. The resulting vegetation index maps are displayed interactively and can be exported. Additionally, users can generate and visualize histograms of the computed indices.  
Figure 10: Vegetation index calculator.  
Figure 9: Search for available Sentinel-2 images.  
* Yield prediction: This feature leverages the selected vegetation index to predict crop yield for the entire growing season or a specified subset of the season, Figure 13. Additionally, it offers the option to update the fields with the generated yield predictions, Figure 14.  
Figure 14: Yield prediction—updated information per field.  
Figure 13: Yield prediction.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 3 Problem Formulation and Preliminaries
### GNNs for Graph Edge Classification  
GNNs can be treated as a deep learning approach for graph-based data. GNNs act as an extension of convolutional neural networks (CNNs) and can be generalized to non-Euclidean data. For each node in the graph, this means aggregating the adjacent node features as a new embedding of the current node, which takes into account the adjacent information. Therefore, node embeddings are capable of capturing the topological characteristics of the network structure where the nodes are located, as well as the unique properties of each individual node. Similarly, the embedding process can be applied to edges and graphs. By doing so, we can obtain edge embeddings and graph embeddings, which encode the relevant features of edges and entire graphs, respectively.  
Since computer networks and the network flow can inherently be depicted in the form of graphs, leveraging GNNs for network intrusion and anomaly detection presents a highly promising method for identifying novel and sophisticated network attacks, like Advanced Persistent Threat (APT) attacks. In this scenario, we form a network flow graph. IP addresses can serve as representations of nodes, while the communication of packets or traffic flow between nodes can be used to represent edges.  
Traditional GNNs, such as GCN, GraphSAGE, and GAT, have been successfully applied to a wide range of applications. However, these methods mainly focus on applying node features to node classification and graph classification tasks. Generally, the features of the edges in the graph cannot be considered in message passing and aggregation, so edge classification cannot be considered.
## Prediction of Experimental Rainfall-Eroded Soil Area Based on S-Shaped Growth Curve Model Framework  
**[PERSON] 1,2,*, [PERSON] 2,\({}^{\dagger}\), [PERSON] 3, [PERSON] 4, [PERSON] 3,\({}^{\dagger}\) and [PERSON] 5,\({}^{\dagger}\)**  
\begin{tabular}{p{284.5 pt}}
1 State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China \\
2 State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China; E-Mail: [EMAIL_ADDRESS] \\
3 State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, Sichuan, China; E-Mails: [EMAIL_ADDRESS] (Q.-G.Z.); [EMAIL_ADDRESS] (F.-L.X.) \\
4 School of Aeronautics \& Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China; E-Mail: [EMAIL_ADDRESS] \\
5 School of Science, Southwest Petroleum University, Chengdu 610500, Sichuan, China; E-Mail: [EMAIL_ADDRESS] \\ \end{tabular}  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-017631199092.  
Academic Editor: [PERSON]  
_Received: 10 May 2015 / Accepted: 7 July 2015 / Published: 14 July 2015_  
**Abstract:** Rainfall-induced soil erosion of a mountain area plays a significant role in supplying sediment and shaping the landscape. The related area of soil erosion, as an index of the changed landscape, is easier to calculate visually using some popular imaging tools. By image analysis, our work shows that the changing of the soil erosion area admits the structure of an S-growth curve. Therefore, we propose to establish an S-curve model, based on incremental learning, to predict the soil erosion area. In the process of incremental learning, we dynamically update the accumulative rainfall and rainfall intensity to train the parameters of our S-curve model. In order to verify our prediction model, the index of area is utilized to express the output of eroded soil in a series of experiments. The results show that the proposed S-growth curve model can be used to estimate the growth of the soil erosion area (average relative error 3%-9.7%) according to variable soil material and rainfall intensity. The original S-growth curve model can calculate the erosion areas of just one soil material and one rainfall condition whose average relative error is 7.5%-12.2%; compared to the simple time series analysis-moving average method (average relative error 5.7%-12.1%), our proposed S-growth curve model can reveal the physical mechanism and evolution of the research object.  
**Keywords:** eroded soil area; S-shaped growth curve; time series analysis; incremental learning
# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 2 Related Work  
As 3D data acquisition technology continues to advance and becomes more affordable, 3D computer vision shows considerable potential. Specifically, 3D point cloud semantic segmentation has found several important applications in areas such as automatic driving, industrial control, and remote sensing mapping. The increasing popularity of deep learning has led to a surge in point cloud semantic segmentation methods, which can be classified as voxelization-based, projection-based, and point representation-based.
# Quality Index of Supervised Data for Convolutional Neural Network-Based Localization
## 3 Approach
### Quality Index Based on Important Pixels for CNN-Based Localization
#### 3.2.1 Step 1: Visualization of Important Pixels  
Step 1 visualizes the important pixels for CNN-based localization. To clarify the important pixels for CNN-based localization, we used an occlusion-based approach, which replaces some original input pixels with other pixels, namely occlusion pixels. If the occluded pixels are important for localization, the localization result changes compared to the result that was obtained using the original input data. The occlusion-based approach compares the localization results obtained from the original input data and occluded input data. The occluded input is generated using a randomized patch. Figure 6 depicts examples of occluded data. Given the input data \(x\), we augment the square patch, \(p\), of size \(h\times h\) on \(x\) in a sliding window fashion. Each pixel of this square patch is a random value. If the pixels occluded by this patch are important for localization, the localization results become worse. We generated 16,512 augmented data per frame of the original raw data.  
To reveal the important pixels for CNN-based localization, we compared the localization result obtained using the original raw data with the localization results obtained using the augmented data. If the pixels occluded by a randomized patch are important for localization, the difference between the localization results of raw data and augmented data becomes large. On the other hand, if the pixels occluded by a randomized patch are not important for localization, the difference between the localization results of raw data and augmented data becomes small.  
We consider these differences as the importance of the pixel and define them as  
\[Importance(m_{uv})=\sum_{j=1}^{l}\sum_{i=1}^{c}\Delta o_{m,i,j}, \tag{1}\]  
Figure 5: Overview of the system.  
where \(\Delta o_{m,i,j}\) is the difference in the localization of raw data and augmented data, \(m\) is the number of layer, \(c\) is the number of outputs, \(l\) is the number of channels, and \(u,v\) is the position of the randomized patch in the original image \(x\). Figure 7 shows an example of the importance map produced by Equation (1). In the example given in Figure 7, the important pixels for localization are around a pallet.
## 3 Results and Discussion
### Optimization of QuEChERS Extraction Conditions  
#### 3.2.1 Preliminary Experiments  
Due to the complex samples' matrix (dairy products) and the analytes variety, investigation of the critical extraction method's parameters was necessary. Preliminary experiments aimed at the selection of the appropriate extraction solvent as well as the suitable combination of the extraction and clean up sorbents. All parameters were studied in milk samples spiked with the EDCs mixture at a concentration level of 100 ng g\({}^{-1}\). In detail, acetonitrile, acetonitrile/methanol (1:1), methanol/dichloromethane (1:1) and methanol were tested as extraction solvents. However, acetonitrile was found to be the most suitable since the extracts contained less interfering compounds and as expected it was easily separated from the aquatic phase [28]. On the contrary, methanol alone as well as methanolic mixtures produced cloudy and dirty extracts, being difficult to separate from the milk substrate along with extremely low recoveries observed for all analytes.  
As far as the QuEChERS sorbents concerns, three basic extraction salt combinations were tested: (a) the original method using 4g MgSO\({}_{4}\)/1 g NaCl [29], (b) the official AOAC 2007.01 method using 6 g MgSO\({}_{4}\)/1.5 g NaAcetate [30] and (c) the official European CEN 15662/2008 method using 4 g MgSO\({}_{4}\)/1 g NaCl/ 1 g C\({}_{6}\)H\({}_{5}\)Na\({}_{3}\)O\({}_{7}\)\(\bullet\)H\({}_{2}\)O and 0.5 g C\({}_{6}\)H\({}_{6}\)Na\({}_{2}\)O\({}_{7}\)\(\bullet\)1.5H\({}_{2}\)O (citrate buffer) [31]. The first combination was found to be the most appropriate for the simultaneous bisphenol A and APs extraction from milk (Figure 1).  
In the final step, the cleanup salts' combination was studied as follows: (a) MgSO\({}_{4}\)/PSA, (b) MgSO\({}_{4}\)/PSA/C18, (c) MgSO\({}_{4}\)/PSA/GCB and (d) MgSO\({}_{4}\)/PSA/C18/GCB. As illustrated in Figure 2, the (b) salt combination was selected for the analysis of the target analytes since the first one exhibited lower recovery for BPA and NP isomers while at the same time producing quite enhanced (>120%) recovery for OP. The last two combinations, both containing graphitized carbon black (GCB) were rejected because recoveries were slightly reduced in all cases, whereas extracts were cloudy and tedious to separate.