esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
## 3 Evaluation Criteria for Residual Stress  
To examine the residual stress comprehensively from a macroscopic perspective, it is necessary to establish an evaluation criterion for residual stress. The distribution of residual stress along the thickness is shown in Figure 2. The initial residual stress before levelling is uncertain because it depends on the initial condition of the sheet. However it has no influence on the generation of rolling residual stress and will be substantially eliminated in the first bending whether it is compressive or  
Figure 1: Variation of the residual stress in multi-roll levelling.  
tensile. Thus, the uncertainty of the initial residual stress has little effect on the evaluation of residual stress. The distribution of the residual stress after levelling typically consists of line segments and each of them corresponds to a reverse bending of the sheet performed by a work roll. Each part of the residual stress can be compressive or tensile, but we only consider the magnitude of the value. In this article, the absolute mean value \(\overline{|\sigma_{r}|}\) of the residual stress is used as the evaluation criterion to describe the overall characteristics of the residual stress of the sheet. In Figure 2, the coordinates of a point \(T\) on the lines of the residual stress are \((\sigma_{r}(h),h)\) and \(\overline{|\sigma_{r}|}\) is calculated by the following integral:  
\[\overline{|\sigma_{r}|}=\frac{1}{H}\int_{-H/2}^{H/2}\bigl{|}\sigma_{r}(h) \bigr{|}dh \tag{3}\]
## 2 Material and Methods
### Statistics  
The statistical analyses were performed with the Jamovi Project ver. 2.3 computer software (2022). To test for between-group differences, a statistical test was selected based on the distribution of the results of the variable in question: when the results of the Shapiro-Wilk test did not indicate that the distribution of a variable was close to normal, the Mann-Whitney _U_-test was applied (for the following variables: adrenalaine, dopamine, and serotonin); otherwise, the Student's _t_-test was used with Bonferroni correction. General linear model (multivariate GLM) analyses for dependent variables were employed to examine changes in the studied parameters over time. In the case of variables whose result distribution was not close to normal, the multivariate GLM was used: many studies indicate that type I error and the power of the F-test are not modified when the assumptions of normal distribution and sphericity are not met [24]. [PERSON]'s test served to test the assumption of sphericity of variance. When this assumption was not met, the Greenhouse-Geisser correction was applied in GLM analyses [25]. A limitation of our study is the relatively small number of participants (30 individuals). This increases the probability of type I and type II errors. However, it is acceptable to perform this type of analysis with a considerable degree of caution [26].  
The model assumed two forms: (1) for 2 measurements: the first measurement (24 h before) and the last measurement (24 h after); time [measurement 1 vs. measurement 3] \(\times\) group [winter swimmers vs. control group]; (2) in addition, for the results of the POMS questionnaire in the winter swimming group, an analysis was carried out for 3 measurements: time [measurement 1 vs. measurement 2 vs. measurement 3]. To determine the effect size, the partial eta square (\(\eta_{\text{p}}\)\({}^{2}\)) was calculated, with values \(>\) 0.01, 0.06, and 0.14 corresponding to small, medium, and large effect sizes [27]. When significant interactions were found, probabilities were calculated for post hoc tests using the Tukey test [28].  
Figure 1: Research design.  
In view of the need for considerable caution when interpreting the results of parametric analyses, a decision was taken to perform analogous analyses with non-parametric tests (the Wilcoxon test for comparing two measurements and the [PERSON] test for comparing three measurements with the post hoc Durbin-Conover test). A limitation of these tools is the inability to examine the time \(\times\) group interaction, which is crucial for drawing conclusions indicating whether the observed changes over time differ between the studied groups. Hence, the decision to apply both types of analysis was made.  
The significance level was set at 5% (\(\alpha=0.05\)); additionally, a situation where the \(p\)-values fell within the range of 0.05-0.10 was considered as a trend.
## 1 Introduction
### 1.4.  
Fresh mushrooms have a limited shelf life at ambient temperatures, primarily due to their high moisture content (87-95%), fast metabolic processes, enzyme activity, and sensitivity to bacterial growth. This leads to a decline in quality, including moisture loss, discoloration, changes in texture and flavor, and reduced nutrient levels. To prolong their shelf life, several preservation methods like drying, boiling, cooking, marinating, freezing, picking, frying, gamma irradiation, and fermentation have been investigated and applied [37].  
The transformation of substrates and the production of bioactive or bioavailable compounds during fermentation results in a higher nutritional value in addition to a longer shelf life [38; 39]. Microorganisms play a crucial role in the development of fermented foods, metabolizing certain compounds in food, leading to distinctive flavors in the fermented product and potentially offering various health benefits. Currently, the microorganisms most utilized in vegetable fermentation include bacteria, notably lactic acid (LAB) and acetic acid bacteria. Among these, _Lactobacillus plantarum_ is a frequently employed lactic acid bacterium in food fermentation, particularly foods with a plant origin [40]. Lactic acid fermentation is an anaerobic biological process in which glucose and other six-carbonsugars, including disaccharides like sucrose and lactose, are converted into cellular energy, lactic acid, and smaller amounts of other acids, resulting in a pH decrease [41]. Lactic acid fermentation can spontaneously occur in raw vegetables and fruits when favorable conditions--such as proper moisture, water activity, temperature, and salt concentration--are present for the growth of native lactic acid bacteria. The addition of salt and sugar or immersing vegetables in brine enhances this environment, promoting LAB growth by protecting the vegetables from light and oxygen. Physical air exclusion and the depletion of oxygen by plant cells during the early stages of fermentation helps establish the anaerobic conditions required for LAB to thrive [42].  
Lactic fermentation is a traditional method employed to preserve both wild and cultivated edible mushrooms in various regions of the world such as Southeast Asia, Japan, and India. Various fungi species have been salted and lato-fermented by Eastern Slays, Estonians, and Poles, serving as a primary method for preserving mushrooms during the winter months [43]. Fermented mushrooms may also be used as meat replacement due to their physico-chemical and organoleptical properties [44]. While mushroom fermentation holds considerable promise for innovation, product diversification, and sustainable development across multiple industries, research on the lactic acid fermentation of edible mushroom fruiting bodies remains relatively limited [26; 28; 45].  
The aim of the study was to investigate the effect of different pretreatment technologies on the amino acid and biogenic amine content of fresh and fermented oyster mushroom.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 4 Experiment
### Ablation Study  
We conducted an ablation study experiment on the MOT17 training dataset, as most previous works do, using the StrongSORT framework [1] as the baseline. First, the original Kalman filter was replaced with our HMM-based Kalman filter (denoted as \(\mathcal{R}\)). As reported in Table 1, this modification reduced the average ID count by 30, demonstrating that the HMM effectively enhances target location estimation and mitigates target missing errors. At the same time, the IDF1 and HOTA metrics exhibited significant improvements due to more reliable association. Subsequently, the default weight parameter (0.98) for multi-cue fusion was replaced with a weighted entropy mechanism (denoted as \(\mathcal{W}\)), which adaptively balances appearance and motion feature contributions, as shown in Figure 2. The results show that \(\mathcal{W}\) achieved an average IDF1 increase of 1.1 over \(\mathcal{R}\), accompanied by a further reduction in IDs. This improvement is attributed to the weighted entropy's capability to dynamically calibrate the trade-off between appearance and motion features, enhancing HOTA at the same time. Notably, since both HMM and the weighted entropy mechanism were not applied in the detecting stage, the proposed method had a negligible impact on MOTA as expected. Further, we replaced the original framework of track management with the multi-agent approach (denoted by \(\mathcal{M}\)), which assigns each track to an agent for management. The significant increase in FPS demonstrates that the proposed approach can offset the additional computation loads caused by the application of the HMM-based Kalman filter and weighted entropy mechanism effectively.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sequence** & **Method** & **IDF1(\(\uparrow\))** & **MOTA(\(\uparrow\))** & **HOTA(\(\uparrow\))** & **IDs(\(\downarrow\))** & **FPS(\(\uparrow\))** \\ \hline \multirow{4}{*}{MOT17-02} & Baseline & 60.5 & **79.4** & 55.0 & 124 & 6.2 \\  & +\(\mathcal{R}\) & 65.9 & **79.4** & 58.8 & 96 & 5.2 \\  & +\(\mathcal{R}\)+\(\mathcal{W}\) & **68.9** & **79.4** & **59.8**
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 1 Introduction  
Cybersecurity plays a crucial role in users' daily lives. Without it, individuals are vulnerable to malicious threats that can compromise their online security, reduce productivity, and harm their privacy. Reducing spam messages can significantly lower the risk of cybercrime and help users feel more confident and secure in performing their daily online activities. Spam can take various forms: text-based, image-based, or hybrid. A newer and more evasive type, known as image-based spam, involves embedding malicious text within images to bypass traditional text-based spam filters. This type of spam is characterized by a more complex structure compared to its text-based counterpart, making detection increasingly difficult. Traditional filtering methods typically rely on text analysis techniques such as keyword matching, pattern recognition, and heuristic rules, which are ineffective against visually embedded content. The widespread use of multimedia on platforms likeemail and social media has introduced new challenges for spam detection. For instance, attackers frequently use deceptive visuals, such as fake banking login screens or fraudulent promotional banners, to trick users into clicking harmful links. This tactic is commonly employed in financial scams, fake advertisements, and malware distribution, making detection more challenging. Therefore, there is a growing need for a reliable and accurate image-based spam detection system. Although existing studies have explored both traditional and deep learning models, very few have provided comparative performance analysis across a unified image dataset. This research addresses that gap by evaluating various machine learning models for image-based spam detection, focusing on each model's ability to effectively classify spam and legitimate (\"ham\") images. The results aim to lay a strong foundation for developing robust and accurate spam detection systems tailored to visual content. Our contributions can be summarized as follows:  
* We present a comprehensive comparative analysis of six machine learning models--ResNet50, XGBoost, Logistic Regression, LightGBM, VGG16, and SVM--for image spam detection, using a standardized dataset and unified preprocessing pipeline.
* We propose a consistent experimental setup that ensures a fair evaluation of all models by applying identical training conditions and evaluation metrics such as AUC, ROC, accuracy, precision, recall, and F1-score.
* We tested the models on a new dataset to see how well they work on unseen data, without retraining.  
This paper is structured as follows: Section 2 represents related work, Section 3 describes the methodology, and Sections 4 and 5 cater to the results and discussion. Challenges are presented in Section 6. The conclusion of this paper is highlighted in Section 7.
## 4 Results and Discussion
### Response of the Ballast Box and Surcharge Block  
In the ballasted tracked, ballast particles and the embedded geogrid form a reinforced composite foundation with improved performance. The structural properties of the mechanically stabilized layer are influenced by the depth of the confined zones. Therefore, the contact forces of the right-side wall before and after the pullout were measured and compared in simulation.  
As shown in Figure 12, the interlocking effect is the largest at about 70 mm on both sides of the geogrid, decreases during the transition zone, and then disappears at a distance greater than about 150 mm. For the contact force of the front wall before and after the pullout, the contact forces above and below the geogrid are relatively symmetrical due to confinement at both the top and the bottom. When the pullout displacement reaches 50 mm, the contact force distribution of the front wall of the triaxial geogrid is more concentrated than that of the biaxial geogrid. Therefore the triaxial geogrid has much larger front wall resistance than the biaxial geogrid in the fully confined zone.  
Figure 13 shows the vertical displacement contour of the top surcharge block at the pullout displacement of 50 mm, which reflects the volume change of the ballast sample. It shows that more movement (dilation) occurs on the right part, which implies that the dilative behavior is more obvious in the reinforced zone at the right-hand end of the sample. The top surcharge block can tilt during the pullout process, which is consistent with the observations in the experimental tests.  
Considering that the dilatancy mainly occurs on the right side of the sample, the average vertical displacement of the surcharge block was recorded. Figure 14 shows that the biaxial geogrid reinforced ballast samples compacted in the initial 10 mm and then expanded; meanwhile, the triaxial geogrid reinforced ballast samples compacted up to a displacement of about 18 mm and then expanded. The displacement at the peak pullout force (approximately 30 mm for the biaxial geogrid and 42 mm for the triaxial geogrid, as shown in Figure 6) is associated with the maximum rate of dilation for the sample, given by the average vertical displacement of the surcharge block. In addition, the extent of dilation in the triaxial geogrid reinforced sample was larger than that in the biaxial geogrid sample.  
Figure 12: Contact force applied on the front wall before and during the pullout test simulation.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 4 Results
### Model Application  
Research indicates that as landslide susceptibility assessment advances, machine learning models are playing an increasingly crucial role in predictive analysis. A key challenge in this process is optimizing model parameters to ensure maximum predictive accuracy and efficiency. This study focuses on enhancing the performance and reliability of three machine learning models, MLP, RF, and CDT, for landslide susceptibility assessment by systematically reconfiguring and fine-tuning their parameters using a grid search optimization technique.  
For the RF model, two important parameters were selected: the number of iterations to be performed (numIterations), ranging from 1 to 200, and the random number seed, ranging from 1 to 30. After the parameter optimization process was completed, the optimal parameters (numIterations:16; seed:15) were used to improve the performance of the RF model (Figure 4). For the CDT model, two internal parameters were selected: numFold, which determines the amount of data used for pruning (ranging from 2 to 9), and seed, which is used for data randomization (ranging from 1 to 300). Combined with the training dataset, the optimal numFold value (8) and seed value (230) were obtained using the Weka software by comparing the AUC values of the model under different numFold and seed parameter values (Figure 5). The AUC is often used to quantitatively verify and compare the predictive power of models [68]. For the optimization of the MLP model in this study, the three parameters were applied to the parameter adjustment and optimization process: learning rate (the learning rate of the weight), training time (the number of epochs in training), and momentum (the momentum applied to the weight update). The learning rate values ranged from 0.1 to 1, with an interval of 0.1. Regarding the learning time value, we adjusted the momentum to within the range of 0.1 to 1 and the training time to within the range of 1 to 200, with intervals of 0.1 and 1, respectively. There were clear differences in the performance of the MLP model under different parameters, which was discovered after a large number of parameter adjustments (Figure 6). In the model optimization process, it could be seen that the performances of the three machine learning models were significantly affected by different parameters. After optimizing the selection of model parameters, the performance of the model was improved.  
Figure 4: Optimization figures for the RF model.
## 2 Proposed Theory
### Multiscale Guided Filter Enhancement
#### 2.2.2 Adaptive Multiscale Guided Filter Composition  
Each image can be decomposed into several layers; the details in the infrared image are typically weak. As is shown in Figure 2, the input image is one of the EOG guided transformed images, there are rich details captured by the HDR infrared sensors in the detail layers, but they are too weak to be observed.  
The composition of the base layer and detail layers can be described as follows.  
\[I_{enhanced}=B_{n}+\alpha_{1}D_{1}+\alpha_{2}D_{2}+\ldots+\alpha_{n}D_{n} \tag{11}\]  
The layers are linearly accumulated together, the value of each coefficient \(\alpha_{i}\) gives expression to the importance of the \(i\)th detail layer, the more the information in the layer, larger the \(\alpha_{i}\). In order to adaptively choose the value of \(\alpha_{i}\), we adopt entropy to evaluate the richness of information in each layer:  
\[Entropy(im)=-\sum_{i=0}^{1}p_{i}\log p_{i} \tag{12}\]  
where \(p_{i}\) is the probability of gray level \(i\) in the image.  
\[\alpha_{i}=\frac{C\cdot Entropy(D_{i})}{\sum_{k=1}^{n}Entropy(D_{k})+z}\qquad i =1,2,\ldots,n \tag{13}\]\(C\) is a fixed coefficient and \(z\) is a very small number added to prevent the denominator from being 0.  
In Figure 2 the effect of the step is shown; the weak details are enhanced. The weak details in detail layers (b,c) become much clearer in (e,f). What needs to be explained is that the figures of detail layers are stretched 10 times for better visibility; the details in fact are much weaker.  
The effect of the proposed multiscale guided filter enhancement in this section can also be obviously reflected in Figure 3. There are two groups of images: panels (a,c) are results of the proposed method without the multiscale guided filter enhancement, in which the information is ambiguous, while panels (b,d) are the results of proposed method, which is much more visually comfortable.  
Figure 3: _Cont._  
Figure 2: (**a**) Input Image. (**b**) Detail Layer 1. (**c**) Detail Layer 2. (**d**) Base Layer. (**e**) Enhanced Detail Layer 1. (**f**) Enhanced Detail Layer 1.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 3 Routing Protocols in MANET  
Routing protocols in MANETs can be categorized into three main types: proactive, reactive, and hybrid protocols. The following tables provide an overview of these routing protocol types, their characteristics, and examples:
## 3 Study Methodologies
### Experimental Measurement Study  
Compared with the theoretical analysis and numerical simulation method, the most direct method is experimental measurement, including indirect measurement and direct measurement.  
The indirect measurement method is to measure and analyze the parameters that are related to flow-induced noise such as pressure [73; 74; 75], velocity [76; 77; 78; 12] and vibration signal [79; 80; 81; 82; 83], then establish the relationship between these parameters and flow-induced noise. Different from the indirect measurement, the direct measurement is more convenient and it is realized by measuring the flow-induced noise directly. However, owing to the high-pressure and high-speed rotating characteristics of centrifugal pumps, the measurement of internal flow noise is to measure the flow noise at inlet and outlet pipes by using a flush-mounted hydrophone, while the external radiation noise is measured by a microphone at the monitoring points arranged evenly outside pumps. Besides, it is also difficult to distinguish the contributions of different noise sources, especially the flow-induced noise and mechanical vibration noise. Therefore, the experimental measurement is always combined with numerical simulation for relevant analysis.  
It is common knowledge that the change of noise is accompanied by the change of pressure, flow rate and so forth. [PERSON] et al. [84] designed a hydraulic performance and flow-induced noise measurement system based on LabVIEW, which realized the synchronous measurement of the head, flow rate and flow-induced noise. The system is beneficial for the understanding of the pump operating condition and has been adopted by many scholars [85; 79; 80].