esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FEM Simulation
Again, the third-order Ogden model (\(n=3\)) had a better performance, despite the clear deviation (max stress of 55 kPa) from the experimental data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**Longitudinal**} & \multicolumn{3}{c}{**Circumferential**} \\ \hline Ogden & \(k\) & \(\alpha_{k}\) & \(\mu_{k}\) & \(k\) & \(\alpha_{k}\) & \(\mu_{k}\) \\ \hline \(n\,=\,1\) & 1 & 7.66217 & \(5.1078\times 10^{-3}\) & 1 & 5.46193 & \(9.30213\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(n\,=\,3\)} & 1 & 2.14665 & \(-4.01108\) & 1 & 1.22825 & \(-1.27365\) \\  & 2 & 2.61517 & 1.98579 & 2 & 2.06915 & 0.60762 \\  & 3 & 1.64414 & 2.03311 & 3 & 0.25084 & 0.68139 \\ \hline Reduced & \(k\) & \(C_{\text{kl}0}\) & \(k\) & \(C_{\text{kl}0}\) & \\ \hline \(n\,=\,1\) & 1 & 4.78018\times 10^{-3}\) & 1 & 7.68995\(\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(n\,=\,2\)} & 1 & 2.35956\(\times 10^{-3}\) & 1 & 5.10340\(\times 10^{-3}\) \\  & 2 & 6.85663\(\times 10^{-3}\) & 2 & 3.81972\(\times 10^{-3}\) \\ \hline \multirow{3}{*}{\(n\,=\,3\)} & 1 & 2.88171\(\times 10^{-3}\) & 1 & 5.72309\(\times 10^{-3}\) \\  & 2 & 4.17404\(\times 10^{-3}\) & 2 & 1.73464\(\times 10^{-3}\) \\  & 3 & 1.56270\(\times 10^{-3}\) & 3 & 9.88421\(\times 10^{-4}\) \\ \hline \multirow{3}{*}{\(n\,=\,5\)} & 1 & - & 1 & 6.46830\(\times 10^{-3}\) \\  & 2 & - & 2 & \(-3.36837\times 10^{-3}\) \\  & 3 & - & 3 & 8.66493\(\times 10^{-3}\) \\  & 4 & - & 4 & \(-3.84478\times 10^{-3}\) \\  & 5 & - & 5 & 5.95269\(\times 10^{-4}\) \\ \hline \multirow{3}{*}{\(n\,=\,6\)} & 1 & 3.31451\(\times 10^{-3}\) & 1 & - \\  & 2 & 8.34169\(\times 10^{-4}\) & 2 & - \\ \cline{1-1}  & 3 & 4.97211\(\times 10^{-3}\) & 3 & - \\ \cline{1-1}  & 4 & 1.60414\(\times 10^{-3}\) & 4 & - \\ \cline{1-1}  & 5 & \(-2.09095\times 10^{-3}\) & 5 & - \\ \cline{1-1}  & 6 & 4.16037\(\times 10^{-4}\) & 6 & - \\ \hline \end{tabular}
\end{table} 
Table 1: Constitutive FEM models considered for this study. Figure 11: Comparison of circumferential experimental mean curve (last tensile cycle) and FEM curves. Figure 10: Comparison of longitudinal experimental mean curve (last tensile cycle) and FEM curves.
# Use of Social Media Data in Disaster Management: A Survey
###### Abstract
While the existing surveys mainly discuss the novel techniques used to analyse social media data, their proposed classifications cannot represent the overall perspectives for applying social media data in disaster management. Overall, this survey proposes a novel taxonomy designed for understanding all significant aspects of social media data management and analysis for disaster management challenges, ranging from data sources to social media applications. Our taxonomical framework's main advantage is that it can provide the guidance of data management processes required in the context of using social media data for disaster management. The focus of the work presented in this paper is on understanding how social media data contribute to disaster management. We do this by surveying the literature for methodologies for social media data management and analysis for disaster management. We classify social media data based on their sources, language, information dimension, methodologies for data management, analysis, evaluation, and applications. The aim of this work is to provide a useful classification that could potentially be used to improve decision-making by enabling disaster managers to identify the appropriate data sources and the corresponding methodologies for analysis and management. The main contributions of this paper are as follows:  
1. Identification of the research challenges involved in using social media data for disaster management and the methodologies for data analysis and management;
2. A research taxonomy for analysis and management of social media data;
3. Application of the proposed taxonomy to survey the existing literature on data analysis and management. The rest of this paper is structured as follows: The background and motivation for this work are presented in Section 2, followed by the details of the survey method in Section 3. Classification details are presented in Section 4, including a categorisation of the data sources, language analysis, and identification of types of users. The languages presented in social media data that are used for social media data analysis are discussedin Section 5. In Section 6, the approaches to social media's inclusion of spatial and temporal information are discussed. The methodologies for social media data management and the application of these data in disaster management are discussed in Sections 7 and 8, respectively, before concluding the paper in Section 9.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 2 Materials and Methods
### Microgravity Simulation with RWV  
One type of device that can simulate microgravity conditions is the single-axis microgravity simulator (Rotary Wall Vessel--Rwv)--Figure 6. Such devices are being used increasingly often to understand how microorganisms react to space conditions [57; 59; 67]. They allow cells to grow in three dimensions and aggregate them based on natural cellular affinity [68]. These devices ensure that the cells are placed in a constant state of suspension and deprived of shear and turbulent forces [69].  
We developed our own laboratory version of the RWV (Figure 6b). The most important features of the device were to assure the application of the container with cells (e.g., lab-on-chip) and automatic adjustment of the rotation speed in the range of 0-50 RPM. Additional requirements, such as small dimensions and weight (fitting inside the incubator) and user-friendly operation, were assumed.  
Our RWV is a flat, vertically placed platform with diameter of 122 mm that can rotate at any given speed. Test elements, e.g., a lab-on-chip, can be attached to its surface. It is driven by a small, 3 W DC motor and two stage gear train with a gear ratio of 63:1. The platform itself is the second stage output gear. Speed control is performed electronically in an open-loop system via an LM317 integrated circuit. The lab-on-chip with inoculated fungi culture can be easily attached to the centre of the vertical platform. Based on recent literature reports [41; 43; 67], a constant speed of 1 rotation per 4 s was applied to simulate microgravity conditions. Our tests were conducted for at least 14 days in order to fulfil the requirements of long-term culture and repeated five times. Every RWV experiment was controlled by a reference culture.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 4 Results and Discussion
### Data Preprocessing  
To ensure the consistency and relevance of the input data, several preprocessing steps were applied to the EEG recordings prior to model training. First, the Fpz-Cz EEG channel was extracted from each EDF file to adapt the input to a single-channel architecture, minimizing the complexity while preserving critical information for sleep stage classification. To improve the data quality and address class imbalance, only epochs with valid sleep stage annotations were retained. Epochs labeled \"movement\" or left unannotated were discarded entirely because they introduced noise and lacked ground truth. Furthermore, epochs labeled stage W were selectively retained only during transitional periods around the onset of sleep and awakening; this was done to avoid the overrepresentation of wakefulness and to balance the distribution between classes. According to the AASM guidelines, stages 3 and 4 were combined into the N3 category. The final class labels were then numerically encoded as follows: N1 = 0, N2 = 1, N3 = 2, R = 3, and W = 4. After class filtering and relabeling, all EEG signals were normalized to the range [0, 1] using min-max normalization. No additional artifact rejection or filtering was applied, following an end-to-end modeling strategy where the model learned directly from the raw normalized signal.  
Regarding data organization, complete EDF files were stored per patient. The dataset was divided by assigning entire EDF files: 60% for training, 20% for validation, and 20% for testing. This prevented the split of files across subsets, ensuring full recordings per patient and a subject-independent evaluation. To address class imbalance, the E-LRUS technique was applied to the training and validation sets, promoting balanced learning and better model generalization. This preprocessing pipeline is designed to maintain data integrity, reduce bias, and enable robust subject-level evaluation, ensuring that the model is trained and tested under realistic and representative input conditions.
## 3 Method
### Retriever Consensus Confidence Fusion (RCCF)
#### 3.3.5 Computational Complexity  
The time complexity of RCCF is analyzed to demonstrate its practical efficiency. The complexity breakdown includes score normalization, requiring \(O(n\times k)\) operations where \(k\) is the number of documents per retriever, consensus computation requiring \(O(|D|\times n)\) where \(|D|\) is the total number of candidate documents, and fusion score computation requiring \(O(|D|\times n)\) operations. The overall complexity is \(O(n\times k+|D|\times n)\). Since, typically, \(|D|\leq n\times k\), the overall complexity is \(O(n\times k)\), which is comparable to traditional fusion methods while providing significant improvements in result quality and reliability.
## 3 Materials and Methods
### Model Metrics  
The detection of predictive ability is the vital link of LSM to pick out the model with the best performance [75]. Based on previous research, a large number of statistical indicators have been applied to evaluate the machine learning model [28]. In this study, the predictive ability of LSM was carried out on the quantitative. Through the comparison of actual tag and forecast, the calculation of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). Then, based on the computed results of the aforesaid four indicators, the sensitivity or specificity, positive predictive value (PPV) or negative predictive value (NPV), accuracy (ACC) and Kappa index can be calculated as follows:  
\[\text{Sensitivity}=\frac{\text{TN}}{\text{TN}+\text{FP}};\,\text{Specificity}= \frac{\text{TN}}{\text{TP}+\text{FN}} \tag{20}\]  
\[\text{PPV}=\frac{\text{TP}}{\text{TP}+\text{FP}};\,\text{NPV}=\frac{\text{TN }}{\text{TN}+\text{FN}} \tag{21}\]  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\times 100\% \tag{22}\]  
In addition, the receiver operating characteristic (ROC) is a curve plotted with the 100-specificity (false positive rate) and sensitivity (true positive rate) on the x-axis and y-axis to draw under the curve (AUC), which is the area under the ROC curve and represents the performance of the two classification models [76].  
In order to estimate the significance of the difference among LSMs, the Wilcoxon signed-rank test is more effective than the traditional test with positive and negative signs and supports the pairwise comparison between sensitivity models [77]. When the \(p\) value is below the critical threshold (0.05) and the \(|z|\) value surpasses 1.96, the null hypothesis will be rejected.
## 4 Conclusions  
Nonthermal food preserving technologies, such as HPP, are emerging in Europe. Therefore, EFSA also requires more results based on food studies using HPP technology to ensure product safety [3]. This study demonstrated that HPP treatment can improve the microbial safety of smoked RTE fish products and prevent the growth of pathogenic _L. monocytogenes_ in cold-smoked and warm-smoked rainbow trout. Use of natural antimicrobials, such as smoke with a moderate salt concentration, could also increase the efficacy of the HPP treatment. However, to ensure an efficient treatment, the pressure must be at least 600 MPa for 3 min when a high bacterial exposure dose is applied. When applying the treatment of 600 MPa, despite a high amount of inoculated _Listeria_, after 28 storage days the numbers of _L. monocytogenes_ colonies, both in cold-smoked and in warm-smoked RTE fish samples, were within the official limit of the Regulation (EC) No. 2073/2005.  
Finally, by using suitable processing parameters, HPP could improve microbiological safety and increase the shelf life of cold-smoked and warm-smoked RTE fish products without chemical preservatives or heating. However, the utmost important factors are good microbiological quality of raw materials followed by good manufacturing practices, which together can ensure a high-quality product during the whole shelf life. In addition, fish products require unbroken low-temperature storage during the whole supply chain.  
**Author Contributions:** Conceptualization, J.K.; methodology, J.K., K.R. and K.M.; validation, K.R. and K.M.; formal analysis, K.M. and K.R.; investigation, K.R. and K.M.; resources, J.K.; data curation, J.K.; writing--original draft preparation, K.R.; writing--review and editing, K.R., K.M. and J.K.; visualization, K.R. and K.M.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the EU European Regional Development Fund and the Regional Council of Northern Savo \"Improving Shelf-Life and Ensuring Quality in Food Using New Technologies\" development project 2019-2022.  
**Data Availability Statement:** Not applicable.  
We wish to thank [PERSON] from Escamar Finland Ltd., Kuopio, for providing the cold and warm smoked rainbow trout for our study and [PERSON] from Toripiha Ltd., Suonenjoki, Finland, for sharing his expertise on high pressure processing.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Results and Discussion
### Comparison of Visualization Effects
Figure 18 shows the focus areas of the Dense-DSC-CBAM model in each module. From front to back, the resolution of the thermal map gradually decreases from 64 \(\times\) 64 to 32 \(\times\) 32, 16 \(\times\) 16, and 8 \(\times\) 8, so the red area becomes larger and larger. Our area of interest is in the low-frequency region in the lower left corner where near-surface defects often appear. Figure 17: Focus areas of models with different attention mechanisms. Figure 18: Focus areas of the Dense-DSC-CBAM model in each module. As can be seen from Figure 18, the shallow convolution layer for feature extraction is focused on the key information of low-frequency positions. With the continuous backward transmission of features, the focus area of the convolution layer of the third dense block gradually expands. As for the fourth dense block, its focus area has deviated from the feature area containing critical information about the defect signal, which is due to the feature reusability of the DenseNet structure. However, the CBAM module redistributes the weight of space and channels, returning the focus area of the entire model to true key feature areas. The results indicate that the defect detection method based on SST can accurately detect and locate the defects, and the proposed Dense-DSC-CBAM model is lightweight and accurate in identifying the defect type. The Dense-DSC-CBAM model is very effective in capturing key time-frequency information. Compared with VGG and ResNet, it is evident that a high level of accuracy does not necessarily guarantee its effectiveness in image classification tasks with physical significance. Achieving optimal results in such tasks requires consideration of a range of indexes beyond conventional metrics like accuracy, F1-score, and FLOPs. It is equally important to assess the model's decision-making basis, which involves analyzing the focus area and whether it appropriately targets the most critical information. Evaluating a model's interpretability is essential for assessing its performance.
## 1 Introduction
The sparse deployment leads toward the creation of a void hole and dense deployment results in an excessive amount of sensors failure. The energy and network stability have a direct relation. As the energy of sensors increases, the stability of the network will be longer, and vice versa. Void holes are areas within the transmission range of a network where a node cannot find its next neighbor or forwarder. The void holes creation has following reasons (1) node becomes dead due to a lot of energy usage and (2) no forwarder node. Topology control has the ability to overcome the undesired effects of UWSNs and consequently to enhance the performance of routing protocols. The relationship between topology control and UWSN is summarized as follows [3] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Base of Difference** & **UWSN** & **WSN** \\ \hline Energy consumption & Very high & Low \\ Propagation delay & High & Low \\ Bandwidth & Low & High \\ Dynamic topology operation & High & Low \\ Efficiency & Low & High \\ Data transmission rate & Low & High \\ Environmental and noise interference & High & Low \\ Communication medium & Acoustic waves & RF waves \\ Speed of propagation & 1200 m/s to 1500 m/s & 3 \(\times 10^{8}\) m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic differences between UWSN and WSN. * In UWSNs, wireless communication is provided by the acoustic channel to enable networking services. * UWSNs have many peculiar characteristics that enhance the challenges in effective networking design. * To overcome these challenges and increase the performance of the network, the topology control method is the best solution. Localization of sensor network in underwater is indispensable. The gathered data is useless until it is not correlated with the specified position of the sensor node. Localization in UWSNs is very important as it has many useful applications, e.g., target tracking, underwater environment monitoring, pollution control and geographic routing protocols. Nevertheless, UWSNs cannot use the Global Positioning System (GPS) due to high energy dissipation and high attenuation of RF signals [4; 5]. In this work, we proposed GEographic and opportunistic routing with Depth and Power Adjustment Routing (GEDPAR) and End to End Void Hole Recovery (E2 EVHR) routing techniques. GEDPAR and E2 EVHR are compared with GEographic and opportunistic routing with Depth Adjustment Routing (GEDAR) and Layered Multi-path Power Control (LMPC) state of the art routing protocols. Simulations are performed to check the effectiveness of the proposed schemes. The remainder of this work is organized as follows: Section 2 provides a brief overview of state of the art work. The problem statement is elaborated in Section 3. Section 4 presents the proposed system model. Discussion on the simulations is given in Section 5. Feasible regions of the proposed protocols are presented in Section 6. Finally, Section 7 summarizes the whole work.
## 3 Results and Discussion
### Gc-Ims
#### 3.2.2 Identification Analysis
The above results reinforce the general opinion that the discrepancies of odor emissions are associated with water quality and different treatment units [1]. Figure 4: Topographic plots of GC-IMS (gas chromatography-ion mobility spectrometry) spectra of IW (influent wastewater), PSO (primary sedimentation tank outlet), AO (aerobic tank outlet) and DNO (denitrification outlet).
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 4 Finite Element Modeling Results and Discussion
### Horizontal and Vertical Displacements at Different Stages of the Soil-Nailing Process  
The horizontal and vertical displacements of the soil mass during the construction stage and loading stage are portrayed in Figures 6 and 7. The red color in Figures 6 and 7 and its intensity shows the soil movement horizontally and vertically. The horizontal displacement of the slope face is given in Section 4.2. The amount of vertical settlement of footing is depicted in Section 5.2. It infers from Figures 6 and 7 that, during the construction stages, there were small horizontal movements of the slope face, and these movements were increased by increasing the overburden pressure, particularly at the middle third of the slope towards the toe of the slope. The maximum horizontal displacement of the slope face occurred in the middle third of the slope and decreased as we moved far from the slope face. During the overburden pressure stage, the horizontal movements increased near the slope face at the top and around the nails. The minimum displacement was at the upper third of the slope at various stages of the soil-nailing process. The vertical movement was decreased as we moved far from the foundation location in all the directions during the overburden pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Soil Type**} & **Bulk Density** & \multicolumn{4}{c}{**Soil Model,** **Mohr-Coulomb**} & **Plastic Straining Due to Primary** & **Elastic Unloading/Reloading** & \multicolumn{1}{c}{**Stress-Dependent**} \\  & **(kN/m\({}^{3}\))** & \(\mathbf{\varphi}\) & **C (kPa)** & \(\mathbf{\Psi}\) 1 & \(\mathbf{\upsilon}\) & **Compression/Deviator** & **(\(E_{ur}\))\({}^{ref}\)** & **Stiffness (kPa)** & **(m)** \\ \hline Loose Sand (RD = 34\%) & 16.06 & 30 & 0.2 & 0 & 0.33 & 1327 & 3981 & 0.2 & 0.5 \\ Medium Sand (RD = 48\%) & 16.41 & 34 & 0.2 & 4 & 0.31 & 1959 & 5887 & 0.2 & 0.5 \\ Dense Sand (RD = 68\%) & 16.92 & 40 & 0.2 & 10 & 0.26 & 2632 & 7896 & 0.2 & 0.5 \\ \hline \end{tabular}  
* Angle of dilatancy (degrees).  
\end{table}
Table 5: Slope material model parameters [33].