esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence Analysis of Liquefiable Interlayer on Seismic Response of Underground Station Structure
## 5 Conclusions  
Based on the finite difference numerical calculation method, the influence law of the liquifiable interlayer on the dynamic response of underground station structures was analyzed, and the influences of interlayer location distribution, relative density, and layer thickness on structural force deformation were explored. The main conclusions are as follows:  
1. The liquefaction range of the liquefiable interlayer is basically symmetrically distributed, and the liquefied area is mainly distributed at the bottom of the station structure and on both sides, away from the structure area. When the wall stiffness is great compared with the surrounding soil, the site soil near the structural side wall cannot easily liquefy.
2. The relative location distribution of the liquifiable interlayer has a strong influence on the structural seismic response. When the liquifiable interlayer crosses the middle of the station, the shear stress and lateral deformation of the structure are the greatest, while when the liquifiable interlayer is located in the bottom area of the station structure, the dynamic response of the station structure is greatly reduced, and at this time, the liquifiable interlayer has certain effects of seismic damping and seismic isolation, which are beneficial in supporting the seismic resistance of the station structure to a certain extent.
3. When the liquifiable interlayer is distributed in the middle of the lateral foundation of the station structure, the shear stress of the negative one-story wall and column decreases with increasing relative density, and the structural shear stress of the wall column near the bottom plate is not greatly affected by the relative density. When the relative density of the sand interlayer is low, the internal acceleration response of the structure is strong, and the acceleration amplification effect of the middle column structure is especially prominent.
4. When the whole liquifiable interlayer is only located in the lateral foundation of the structure, with an increase in the thickness of the interlayer, the shearing effect on the station structure under the earthquake action becomes stronger, and the probability of damage becomes greater. However, when the thickness of the liquifiable layer  
Figure 17: Influence of liquifiable interlayer layer thickness on the uplifting displacement of the station.  
increases to a certain extent, the station structure is wholly placed in the liquifiable interlayer, and although the horizontal disturbance of the structure decreases at this time, the uplift displacement of the structure reaches its maximum.  
Writing--original draft preparation, J.Y.; writing--review and editing, Y.L. and J.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data are contained within the present article. The other data presented in this research are available in [27].  
The authors declare no conflict of interest.
## 5 Calibration of Structural Capacity
### Optimization of the ANN Model and Performance Evaluation  
The parameters for optimizing the neural network to predict the response ratio of the bridge are the performance function and the number of neurons in the hidden layer, consisting of two and eight parameters, respectively. The performance capabilities of 16 models, which combine the performance function and the number of neurons parameter in the hidden layer, were evaluated. The response ratio predicted by training each model is shown in Table 6, using the coefficient of determination (\(R^{2}\)), which is an indicator that evaluates the accuracy compared to the target value, and the mean squared error (MSE), which evaluates the error with the target value.  
The model of N12S showed a result that has a higher performance compared to the other model structures, as shown in Table 6. Furthermore, the response ratios of N12S were similar to the target data, and the error ratio of N12S was also the highest for the prediction performance, as shown in Figure 10. Figure 11 presents the response ratio between the two models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ANN Model**} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**Performance Evaluation**} \\ \cline{2-5}  & **Performance** & **Number of** & **R2** & **MSE** \\  & **Function** & **Neurons** & **R2** & **MSE** \\ \hline N2M & & 2 & 0.46 & 0.024 \\ N4M & & 4 & 0.64 & 0.014 \\ N6M & & 6 & 0.62 & 0.015 \\ N8M & & 8 & 0.55 & 0.022 \\ N10M & MSE & 10 & 0.36 & 0.045 \\ N12M & & 12 & 0.26 & 0.065 \\ N14M & & 14 & 0.49 & 0.025 \\ N16M & & 16 & 0.18 & 0.091 \\ N18M & & 18 & 0.44 & 0.040 \\ \hline \multicolumn{5}{c}{Best model: N4M} & \multicolumn{2}{c}{0.64} & 0.014 \\ \hline N2S & & 2 & 0.57 & 0.019 \\ N4S & & 4 & 0.57 & 0.025 \\ N6S & & 6 & 0.04 & 0.111 \\ N8S & & 8 & 0.22 & 0.156 \\ N10S & SSE & 10 & 0.14 & 0.097 \\ N12S & & 12 & 0.82 & 0.008 \\ N14S & & 14 & 0.51 & 0.025 \\ N16S & & 16 & 0.19 & 0.117 \\ N18S & & 18 & 0.05 & 0.402 \\ \hline \multicolumn{5}{c}{Best model: N12S} & \multicolumn{2}{c}{0.82} & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of ANN model.  
Figure 9: Schematic diagram of ANN model.  
prediction and target data using a scatter plot. Overall, the prediction of N12S showed linear trends compared to the point for the target data. Some data estimated outliers other than the target data. Although it is possible to develop a model with a better learning performance by adjusting some hyperparameters, as shown in Figure 12, generalization is limited due to overfitting. Therefore, the determined ANN model can be used to estimate the correction factor to calibrate the calculated design strength.  
Figure 11: Comparison of target and prediction.  
Figure 10: Performance of N12S model. (**a**) Comparison between target and prediction of N12S. (**b**) Error rate of N12S prediction.
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.5 Encoding Categorical Data  
One-hot vectors were used to represent categorical variables in a way that avoids introducing unintended relationships or order among categories. When categorical data are encoded as integers, it can falsely imply an ordinal relationship. One-hot encoding addresses this issue by assigning each category a unique binary vector where all categories are treated equally. This approach is compatible with many ML algorithms that require numerical input, converting categorical data into a format that algorithms can process without bias [41]. Specifically:  
* Standing activity with a \"0\" assigned value got a [1,0,0,0,0,0] one-hot vector;
* Walking (without crate) with a \"1\" assigned value got a [0,1,0,0,0,0] one-hot vector;
* Bending with a \"2\" assigned value got a [0,0,1,0,0,0] one-hot vector;
* Lifting crate with a \"3\" assigned value got a [0,0,0,1,0,0] one-hot vector;
* Walking (with crate) with a \"4\" assigned value got a [0,0,0,0,1,0] one-hot vector;
* Placing crate (with crate) with a \"4\" assigned value got a [0,0,0,0,0,1] one-hot vector.
# Gathering Big Data in Wireless Sensor Networks by Drone +
Footnote †: The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several works. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone's energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for big data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node's radio range. Our experimental results showed that the proposed algorithm surpasses, by up to 30%, the state-of-the-art heuristics' performance in finding drone tours in this type of scenario.
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.4 Effect on Biomass Production and the Proximate Chemical Composition of Microalgae
Moreover, it promotes environmental sustainability by recycling organic waste and reducing the risks of water pollution. MANOVA revealed a significant and diverse main effect of the factors (MEDIUM and ALGA) and their interaction on the combined biomass chemical compositions (see Figure 8). The observed patterns in the biochemical composition of both microalgae in response to different concentrations of TRW% in the culture medium can be interpreted in the context of nutrient availability and its impact on metabolic processes and growth parameters. A first pattern, characterized by higher cellular contents of carbohydrates and lipids in TRW-20% compared to TRW-10% (Figure 8J,M), suggests that microalgae allocate more resources towards energy storage and structural components when exposed to moderate percentages of TRW. This could be attributed to an increased availability of carbon and other essential nutrients, promoting the synthesis and accumulation of carbohydrates and lipids as energy reserves. Conversely, the lower protein content in TRW-20% (Figure 8O) indicates a potential trade-off between energy storage and protein synthesis, with microalgae prioritizing energy storage over protein production under these conditions. The consistent carotenoid content across TRW-10% and TRW-20% (Figure 8U) suggests that carotenoid biosynthesis may be significantly influenced by changes in nutrient availability within this range. Additionally, the stable total chlorophyll content (Figure 8R) indicates that photosynthetic efficiency remains unaffected by variations in RW concentration within this range. A second pattern, observed between TRW-20% and TRW-30%, reveals contrasting responses in carbohydrate and lipid content (Figure 8J,M), with a decrease in both parameters as the TRW concentration increases. This suggests that microalgae may reallocate resources away from energy storage towards other metabolic pathways or biomass production when exposed to higher concentrations of TRW. The decrease in protein and chlorophyll content further supports this interpretation, indicating a shift in metabolic priorities toward biomass synthesis and growth under nutrient-rich conditions. The consistent carotenoid content suggests that carotenoid biosynthesis remains unaffected by changes in RW concentration within this range, similar to the first pattern.
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Receptors and Intracellular Signaling
#### 2.2.3 IL-38  
As noticed above concerning the maturation process of IL-38, little is also known about its exact molecular mechanism of action. Since 2001, several hypotheses, occasionally contradictory, have been postulated. IL-1R1 was initially proposed to be an IL-38 receptor [32]; however, this has not been consistently confirmed later on. [PERSON] and colleagues suggested that IL-38 could instead bind IL-1 Rrp2, as IL-36 Ra does [33], and mediate a 42% reduction of the IL-36-dependent IL-8 production by human peripheral blood mononuclear cells (PBMC) (in comparison with the 75% IL-36 Ra-mediated). Interestingly, IL-38 seems to work as a nonclassical inhibitor, being more active at low (10 ng/mL) rather than high doses (1 \(\upmu\)g/mL) [33]. A recent study proposed that IL-38 might also bind IL-1 RAPL1, and confirmed the role of IL-1R1, but not IL-1 Rrp2, as an additional IL-38 cognate receptor [21].  
Overall, since conflicting data exist on IL-38 binding partners further studies are needed to delineate its specific molecular mechanism.
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 1 Introduction
The contributions of this article can be listed as follows:  
* A fully automated deep learning-based framework called AWEU-Net is proposed for improving the accuracy of lung nodule detection and segmentation;
* PAWE and CWEU mechanisms are proposed to model the correlation between the spatial and channel features and encourage the CNN model to learn the most relevant features that enhance its ability to discriminate between nodule and non-nodule feature representations;
* A comparative study of different nodule detection models and nodule segmentation models is presented using two publicly available datasets, namely LUNA16 and LIDC-IDRI. Section 2 of this article discusses the existing lung nodule segmentation systems based on classical computer vision and deep learning techniques. Section 3 introduces the proposed system workflow and model architecture. Section 4 presents and discusses experimental results. Finally, Section 5 concludes the article and highlights a future extension of this research.
## 4 Short-Circuit Characteristic Calculation Method
### Modification of the Matrices for the Fault DC Grid  
It is assumed that a pole-to-pole fault occurs between line \(l_{\mathrm{ij}}\) and line \(l_{(i+n)(j+n)}\). The fault point is denoted as \(n_{\mathrm{f}}\) at line \(l_{ij}\) and \(n_{\mathrm{\prime}+n}\) at line \(l_{(i+n)(j+n)}\). Note \(n_{\mathrm{\prime}+n}\) could be set as the reference node, and its reference voltage could be treated as \(-u_{\mathrm{dc}}/2\). Thus, the voltage of node \(n_{\mathrm{f}}\) is \(u_{\mathrm{dc}}/2\). Therefore, for pole-to-pole fault, the number of the unknown node voltages do not increase. However, branch \(l_{\mathrm{ij}}\) and branch \(l_{(i+n)(j+n)}\) are replaced by branch \(l_{\mathrm{\prime}}\), \(l_{\mathrm{\prime}j}\) and branch \(l_{(i+n)(j+n)}\), \(l_{(i+n)(j+n)}\). Since the node voltages \(V_{\mathrm{\prime}}\) and \(V_{\mathrm{\prime}+n}\) are treated as known, adding the pole-to-pole fault means the branches \(l_{\mathrm{ij}}\) and line \(l_{(i+n)(j+n)}\) no longer exist, while the origin nodes are connected to the known nodes. Therefore, the dimension of these matrices remains the same, and the elements are very easy to modify.  
The elements in \(\mathbf{J_{0}}\) and \(\mathbf{J_{c}}\) should be modified as:  
\[\left\{\begin{aligned} & l_{ij}^{\prime}=0\\ & l_{(i+n)(f+n)}^{\prime}=0\\ & l_{ci}^{\prime}=l_{ci}+G_{if}V_{f}-l_{if}\\ & l_{c(i+n)}^{\prime}=l_{c(i+n)}+G_{(i+n)(f+n)}V_{(f+n)}-J_{(i+n) (f+n)}\\ & l_{cj}^{\prime}=l_{cj}+G_{fj}V_{f}+l_{fj}\\ & l_{c(j+n)}^{\prime}=l_{c(j+n)}+G_{(f+n(j+n))}V_{(f+n)}+J_{(f+n) (j+n)}\end{aligned}\right. \tag{21}\]  
The correlation matrix \(\mathbf{A_{0}}\) remains the same, but the admittance matrix \(\mathbf{G_{0}}\) need to be modified. Since there are no new nodes, only the self-admittances of node \(n_{i}\) and \(n_{j}\) should be modified and the mutual admittance between node \(n_{i}\) and \(n_{j}\) should be eliminated.  
\[\left\{\begin{aligned} & G_{0}^{\prime}(i,j)=G_{0}^{\prime}(j,i)=0\\ & G_{0}^{\prime}(i+n,j+n)=G_{0}^{\prime}(j+n,i+n)=0\\ & G_{0}^{\prime}(i,i)=G_{0}(i,i)+G_{if}\\ & G_{0}^{\prime}(i+n,i+n)=G_{0}(i+n,i+n)+G_{(i+n)(f+n)}\\ & G_{0}^{\prime}(j,j)=G_{0}(j,j)+G_{fj}\\ & G_{0}^{\prime}(j+n,j+n)=G_{0}(j+n,j+n)+G_{(f+n)(j+n)}\end{aligned}\right. \tag{22}\]
## 3 Results and Discussion
### Nanostructure Characterizations
#### 3.1.2 TGA Analysis  
The NZ TGA curve shows (Figure 2) a weight loss of about 8.03% at temperatures below 260 \({}^{\circ}\)C, which is related to the physical adsorption of water molecules with hydroxyl groups on the surface of the substrate; in addition, another weight loss at below 600 \({}^{\circ}\)C of about 6.33% is related to intermolecular hydrogen bonds in the zeolite structure [34]. The TGA curve of the PCD-zeolite (Figure 3) shows two weight reductions: the first reduction is about 6.99% at temperatures below 160 \({}^{\circ}\)C, which is related to the water adsorbed on the surface and the internal structure of the composite; and the second decrease is observed at 190 to 420 \({}^{\circ}\)C, which is related to the polymer structure around the zeolite. These results show that about 52.5% of the organic composition deposited on the zeolite is related to the polymer. The PCD-zeolite-IB TGA curve shows (Figure 4) weight reductions that are similar to the polymer NZ TGA curve. From the DTG curve of the PCD-zeolite, two significant peaks can be observed in the temperature range of 240 to 260 \({}^{\circ}\)C; this is related to the degradation of CA components and the temperature range of 260 to 400 \({}^{\circ}\)C, which, in turn, is related to the degradation of CD components. According to the DTG curve of PCD-zeolite-IB, the third weight loss in the range of 25 to 160 \({}^{\circ}\)C, 160 to 460 \({}^{\circ}\)C, and 460 to 600 \({}^{\circ}\)C can be observed; herein, the range of 25 to 160 \({}^{\circ}\)C is related to the water adsorbed on the surface, and the range of 160 to 600 \({}^{\circ}\)C is related to the organic compound on the surface of NZ. The DTG curve of NZ is observed in the temperature range from zero to 150 \({}^{\circ}\)C, which is related to water removal.
## 1 Introduction  
A technological breakthrough has made clinical investigations and tests for disease diagnosis feasible to a great extent; high-end diagnostic tools are readily available. Ayurveda uses certain human indices like pulse (Nadi), eyes, nails, and tongue to diagnose common ailments. Ancient traditional medicine used these parameters as a means of diagnosis, with no advanced technological tools available, to predict many abnormalities related to the health status of internal body organs. These traditional, non-invasive diagnostic practices can be adapted to give a preliminary prognosis by quantifying and automating the entire analysis process to remove the associated subjectivity.  
The paper presents a novel method for multilabel disease classification from tongue analysis. Observation of the tongue is an important part of traditional Chinese medicine as well as Ayurveda. It was established that different parts of the tongue correspond to different internal organs. The tip of the tongue reflects the heart and lungs; the middle part reflects the spleen, pancreas, and stomach; its root represents the kidneys and intestines, and the right and left sides represent the liver and gall bladder.  
Over the past few decades, the use of decision-support systems in clinical practice has increased. Deep learning models allow reliable classification and object detection of medical images, showing remarkable accuracy comparable to that of physicians. An automated tongue diagnosis system is a way to bridge the gap between traditional diagnostic methods and modern western medicine. Quantitative analysis can overcome the subjective aspect, which stems from a skilled traditional practitioner's knowledge base and meticulous practice and establish its accountability and acceptability. In this paper, we have developed a multi-disease classification algorithm that is able to predict multiple disease classes with appreciable accuracy. According to the collected data samples, eight diseased states are considered. This paper contributes to the following fields:  
* The paper presents a MobileNetV2 deep learning model to detect the diseased tongue from a normal, healthy tongue. The model employs a five-fold cross-validation and transfer learning technique to achieve this binary classification. The main achievement is the sanguine results obtained with images taken by smartphone cameras rather than standard equipment.
* A multilabel classification model for eight common categories of ailments is developed. The DenseNet-121 architecture for disease classification achieves satisfactory results with the small dataset. There are eight disease labels: diabetes, hypertension, acidic peptic disease, pyrexia, hepatitis, cold cough, gastritis, and others.  
The organisation of this paper is as follows: Section 2 summarises the techniques used for tongue feature extraction. Section 3 provides an explanation of the related work, dataset, and training details, along with the experimentation and evaluation metrics used. Finally, Section 4 reports the performance analysis. Section 5 concludes the research.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 5 Preliminary Results and Validation of QKD Emulation with PUF
### Holding Input Combinations Constant  
To verify each of the seven input combinations, given the expected output, we hard coded each input combination and checked whether the output matches the input for matching bases, or whether the output matches the input approximately 50% of the time for mismatching bases. We held each combination constant and transmitted a total of 75 trits for each of the seven input combinations, for a total transmission of 525 trits.  
Table 2 shows that we obtained matching input and output for all six of the matching bases input combinations (zero errors) and non-matching input and output for 34 out of 75 trits for the mismatching basis input combination (34 errors). Thirty-four out of seventy-five is approximately 50%, which is what we expect for the mismatching input combination.  
These preliminary results validate the theory and mechanisms presented in this work by demonstrating the input matches the output for all matching bases combinations and that the input matches the output 50% of the time when bases do not match.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
###### Abstract
Iterations serve the objective of making the final output features contain the information of the entire graph by repeatedly computing the distribution. The steps for training the MDPN are as follows: Images are first supplied to the ISOL module. The ISOL module crops the images in accordance with the object's MBR. After that, the cropped images are sent to the backbone to extract features. The GMDP module is then used to extract the three levels of distribution features from the object features, which are subsequently utilized to update the original object features. Following a number of iterations in the GMDP module, the cross-entropy loss between the output features of the GMDP module and the ground truth labels is determined. Our main contributions are as follows:  
* As far as we are aware, MDPN is the first model to stack graph networks in series to extract several levels of distribution in few-shot image classification. Our follow-up ablation trials have shown its usefulness. Figure 2: Variable pseudo-objects in CUB-200-2011. * In order to increase the precision and completeness of the localization, we incorporated past knowledge into the target object region localization by utilizing segmentation information. The accuracy of the prior-knowledge-based method is significantly higher than that of the supervised-learning-based target area localization method. * We perform in-depth analyses on the CUB-200-2011 dataset. In both 5-way 1-shot and 5-way 5-shot settings, the classification accuracy of MDPN outperforms the baseline by 8.17% and 1.24%, respectively. In the same setting, it outperforms most of the existing few-shot classification methods.