esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
All _Salmonella_ isolates were susceptible to cefoxitin, cefotaxime, chloramphenicol, ciprofloxacin, gentamicin, kanamycin and trimethoprim-sulfamethoxazole. However, it is important to note that first and second generation cephalosporions (cefoxitin and cefotaxime) and aminoglycosides (gentamicin and kanamycin) that appear effective in vitro, may be ineffective in clinical treatment and thus cannot be reported as susceptible. Widespread resistance against tetracyclines may arise as a result of selection pressure caused by the use of these agents in clinical and veterinary treatment, prophylaxis and as growth promoters for livestock [38]. Previous studies in South Africa found a high level of resistance to tetracyclines and ampicillin in _Salmonella_ from cattle meats [17; 23]. Additionally, in other African countries, resistance against these antibiotics in _Salmonella_ was highest [37]. However, the small sample of five _Salmonella_ isolates from this study does not provide a good overview of the potential threat of multi-drug resistance in _Salmonella_ in KZN. Virulence genes are crucial for _Salmonella_ pathogenesis and they manifest differently within the different hosts [39]. The five _Salmonella_ isolates were screened for the presence of eight virulence genes, namely, _invA_, _agfA_, _lpfA_, _hiLA_, _svH_, _sef_A, _sopE_, and _sopC_ using PCR. The _invA_ gene, responsible for the invasion of epithelial cells at the onset of infection, is typically present in all _Salmonella_ spp. and is therefore used as a target for rapid identification using PCR [8]. However, in the present study, the _invA_ gene was not observed in one of the five isolates (_S_. Heidelberg). Although _invA_-negative _Salmonella_ are considered rare, they have been identified in other studies [40; 41]. Nevertheless, _invA_-negative strains may use other mechanisms for invasion [42; 43]. The virulence gene that was common to all five isolates was _agfA_, which encodes fimbiae that increases the adhesion of the bacterium to intestinal cells and is also involved in biofilm formation [8]. Other genes were variably present and _spvC_, a plasmid mediated gene involved in vertical transmission [8] was not present in any of the six isolates. All isolates, including the three \(S\). Stanley isolates, had a different virulence profile, indicating a different potential for infection of animals and humans [9]. The limitation of this study is that due to the low prevalence of _Salmonella_, we could not give a representative view on the characteristics of _Salmonella_ on beef meat and beef organs. The serotypes detected in this study might not be representative of the serotypes that can be present. Antimicrobial resistance was low, with only one strain being resistant, though we only investigated five strains, which is not representative of the potential resistance that can be found in _Salmonella_ from beef products. Similarly, the virulence genes detected in this study may be an underrepresentation of the virulence of _Salmonella_ from beef.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Sybil Attack  
In the Sybil attack, an attacker may forge multiple identities and pretend to be in multiple places at the same time. The basic goal of these false identities is to deny the information passing procedure. These multiple identities can be occupied by fabricating faults or stealing the identities of legitimate nodes. Hence, the Sybil attack can also cause severe damage to distance-based or location-based routing protocols. Moreover, the attacker can behave as a base station or receiver, which sends acknowledgment packets to sensor nodes to avoid retransmission.  
Authentication and position verification are feasible countermeasures, but position verification is difficult owing to mobility. The author of [51] presented a robust and lightweight scheme to detect a Sybil attack, which is based on received signal strength indicator (RSSI) readings of messages. Another countermeasure is the random key pre-distribution [52], which relies on cryptographic principles and is easy to analyze. In [53], a Sybil attack detection scheme based on state information of the nodes is proposed, which assume the availability of beacon nodes. But, this scheme assumes that each node is stationary and has the same transmission range over bidirectional links, and depends on the density of legitimate neighboring nodes.
# Optimized Travel Itineraries: Combining Mandatory Visits and Personalized Activities
## 3 Methodology
### Adaptive Neighborhood Simulated Annealing Algorithm (ANSA)
#### 3.3.3 Neighborhood Mechanism  
Five neighborhood structures are incorporated into the proposed algorithm to investigate various route combinations. The following is an explanation of the neighborhood structures:  
1. Mechanism 1: Swap (Sol 1) operation The swap procedure commences by selecting the \(i\)th and \(j\)th positions of a solution at random and systematically swapping the integers in these positions. This operation is exclusively executed in Sol 1.  
2. Mechanism 2: Reverse (Sol 1) The reverse procedure operates by reversing the direction between two randomly chosen positions, \(i\)th and \(j\)th, within a solution in Sol 1.
3. Mechanism 3: Random Insertion (Sol 1) The \(i\)th and \(j\)th positions of Sol 1 are randomly chosen, and an \(i\)th number is inserted in front of the \(j\)th position to implement this neighborhood operator.
4. Mechanism 4: Mandatory Insertion (Sol 1) This operator is executed by randomly selecting the \(i\)th in mandatory list (\(N_{m}\cup N_{d}\)) and \(j\)th positions of Sol 1 and then inserting an \(i\)th number in front of the \(j\)th position.
5. Mechanism 5: Random Change (Sol 2) The \(i\)th position in sol 2 is chosen. Then, the assigned number in this position is replaced by the random value from 1 to \(2^{|A|}-1\), while \(|A|\) is the number of activities.
# DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking
## 4 Experimental Results and Analysis
### Comparison with the State-of-the-Art Methods  
[PERSON] et al. [24] and ReDMark [25] used standard grayscale images and binary watermarks in training and testing, and to improve robustness, the same method was used to simulate an attack method in the training phase, which was consistent with DARI-Mark. Therefore, to evaluate and discuss the performance of DARI-Mark, the proposed method was compared with the state-of-the-art methods: [PERSON] et al.'s method [24] and ReDMark [25]. In the original paper, [PERSON] et al. [24] embedded the watermark in the Y channel (luminance) of the color image. When replicating, we changed it to embed the watermark directly in the grayscale image. To ensure the reliability of the comparison results, the same training set, validation set, test set, and randomly generated binary watermarks were used when replicating [24,25]. During training, the attack layers of the three methods were the same, including JPEG compression (\(Q\) = 50), Gaussian noise (\(\sigma\) = 0.1), salt-and-pepper noise (\(s\) = 0.04), and sharpening (\(R_{5}\) = 10). The rest is consistent with the network structure of the original paper.  
Figure 9: NCC curves under different attacks: (**a**) JPEG compression, (**b**) sharpening, (**c**) cropping, (**d**) Gaussian noise, (**e**) scaling, and (**f**) salt-and-pepper noise.  
Figure 10: Visualization of attacked images and extracted watermarks.  
It is known from Section 4.3 that as the \(\alpha\) increases, DARI-Mark's imperceptibility decreases and its robustness increases. Since the focus of this paper was on robust watermarking, we selected \(\alpha=1.0\) for comparison. The PSNR of the three methods are shown in Table 2. It can be seen that the PSNR of the watermarked image generated by DARI-Mark was improved, and it had better imperceptibility. To compare the robustness, the imperceptibility of the three methods was the same by modifying the watermark embedding strength \(\alpha\), that is, the PSNR of the generated watermarked image was the same. The robust performance comparison results are shown in Table 3 (the BER values are presented as percentages). The data with the lowest BER and highest NCC of the three methods under each attack are bolded to indicate which method was more robust under that attack. It can be seen that DARI-Mark was more robust to JPEG compression, sharpening, cropping, Gaussian noise, and salt-and-pepper noise. However, the robustness to geometric attacks such as scaling attacks needs to be improved, which is a limitation of DARI-Mark.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 4 Results and Discussion
### Model Evaluation
This value, slightly higher than the 72.77% from the validation set, suggests consistent performance in generating reliable agreements. The results show the model's overall strong performance, effectively managing class imbalances and excelling in identifying negative classes. Although there is room for improvement in classifying less frequent categories, the model maintains a good balance between precision and recall, minimizes random predictions, and generalizes well across datasets. To reinforce these outcomes, Figure 14 presents the overall ROC curve of the model, which reflects the model's ability to differentiate among classes using different decision thresholds. The area under curve (AUC) values reflect high performance for nearly all classes: W and N3 both achieve an outstanding AUC of 0.99, while R and N2 also produce excellent results, with AUCs of 0.95 and 0.96, respectively. Although the N1 class has a lower AUC of 0.85, it still indicates an acceptable discriminative ability, especially considering its low representation in the data distribution. The ROC curves illustrate the model's strong ability to distinguish between sleep stages, maintaining high true positive rates while minimizing false positives, even with unbalanced class distributions. Complementarily, the model's overall precision-recall curve, particularly valuable in evaluating performance under class imbalances, is presented in Figure 15. The curves for classes 1 through 4 (N2, N3, R, and W) remain high, indicating a favorable equilibrium  
Figure 14: Overall ROC curve of the model. between recall and the correctness of positive predictions. In particular, N3 and W exhibit the most stable and elevated curves, maintaining high precision levels even with increasing recall. In contrast, the precision of class N1 decreases as the recall increases, reflecting the difficulty of the model in identifying this class without generating false positives. Although the model exhibits strong results across most stages, improvements are still needed to handle less frequent categories. The per-class metrics and the analyses conducted confirm the model's classification capabilities across various sleep stages, despite variations due to class imbalance. Although N1 performs poorly relative to more prevalent stages, such as N2 and N3, the model manages the dominant classes well, with a good balance between precision and recall. Global metrics such as the F1-score and Jaccard index indicate its solid classification capacity, which could be further improved by addressing the dataset imbalance. The ROC and precision-recall curves reinforce the evidence of the model's reliable discriminative power. Despite its limitations with minority classes, the model demonstrates strong generalization and overall robustness. The next section will provide a detailed discussion of these results, their implications, and comparisons with previous studies to guide future model development.
## 5 Experiment Results and Analysis
### Visualization of the Results  
To compare the effectiveness of the proposed algorithm with the YOLOv8n algorithm in detecting surface defects on strip steel more intuitively, the DEW-YOLO model and the YOLOv8n model were applied to detect defects in the same test set, and some of the defect detection effects are shown in Figure 11. As can be gleaned from the figure, the original  
Figure 10: P-R curve of DEW-YOLO algorithm.  
Figure 9: P-R curve of YOLOv8n algorithm.  
YOLOv8n model suffered from the problem of missed detection or low accuracy, with some categories of detection performance being especially poor, while the DEW-YOLO model was significantly better than the original YOLOv8n model in terms of effect; it could more comprehensively identify the defective targets when detecting some of them, and the detection accuracy also significantly improved. The DEW-YOLO model detected defects that were missed by the baseline model and had a higher confidence level than the baseline model in detecting defects in the same location. The test results verify the effectiveness of the DEW-YOLO model for defect detection and provide solid support for quality control in the strip production process.
# B-Spline Curve Fitting of Hungry Predation Optimization on Ship Line Design
## Appendix A B-Spline Curve Fitting of Hungary Predation Optimization Algorithm with Knot Guidance
### Fitness Function Selection  
As a key component of the algorithm, the selection of fitness function directly affects whether it can find the optimal solution and the convergence speed of HPA. In the whole process of population size search, it does not rely on external information, only based on the fitness function. Individuals in a population adjust their positions based on fitness.  
This paper proposes three fitness functions that can be applied to HPA.  
(a) Bayesian Information Criterion (BIC) [22]: its fitness function is described as Formula (17).  
\[\mathit{Fitness}=-2\mathrm{Ln}(L_{1})+\mathrm{Ln}(n_{1})\ \times\ N_{p} \tag{17}\]  
where \(L_{1}\) is the likelihood function, \(n_{1}\) is the sample size and \(N_{p}\) is the number of parameters.  
(b) Maximum error of a single point of the curve is described as Formula (18).  
\[\mathit{Fitness}=Max(P_{\mathit{ir}}-P_{\mathit{if}}) \tag{18}\]  
where \(P_{\mathit{ir}}\) is the actual given data point, and \(P_{\mathit{if}}\) is the point on the corresponding fitting curve.  
(c) Overall standard deviation of curve is described as Formula (19).  
\[\mathit{Fitness}=\!\sqrt{\frac{\sum_{i=1}^{n}\left(P_{\mathit{ir}}\!-\!P_{ \mathit{if}}\right)^{2}}{N_{k}}} \tag{19}\]  
where \(N_{k}\) is the number of knot vertices.  
The BIC method considers the complexity of the calculation model and avoids the over fitting problem by adding the penalty term of model complexity. When the number of knots and control points is small, the BIC value will decrease with increasing the number of knots until it reaches the minimum value. Then, as the number of knots continues to increase, the accuracy of the curve improves slowly, and the BIC value will increase. In practical engineering application, Formulas (18) and (19) are more intuitive and convenient.
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
## 2 Materials and Methods
### Test Methods
#### 2.2.2 Water Erosion Model Test  
Flume tests were conducted on unreinforced, reinforced three times, and reinforced six times sand models to evaluate their resistance to erosion during the embankment slope erosion process. The flume model tests are depicted in Figures 5 and 6. The testing procedure is outlined as follows:  
1. Sediment samples were placed in sandboxes, which were subsequently positioned at the designated location of the sink and secured with plasticine (the inclined plate has a slope of 6.8 degrees) to ensure stability of water flow.
2. Water volume was regulated using a valve, gradually increasing it while maintaining the water level between 9 and 10 cm through the tailgate for each group of flow rates. Approximately 100 mL of initial upstream water was collected using a plastic hose; thereafter, water samples at one to two and two to three minutes after initiation were gathered via a funnel. Downstream suspended mass was collected at intervals of one minute thirty seconds and two minutes thirty seconds, approximately 100 mL each time, for calculating erosion amounts; sediment load material during the period from three to four minutes along with suspended mass at three minutes thirty seconds was also collected for particle size distribution analysis.
3. Flow rate \(Q\) and water depth \(h\) were simultaneously recorded for shear stress calculations.
## 5 Conclusions  
This study underscores the effectiveness of integrating InSAR, GPR, and ERT in diagnosing and addressing highway pavement subsidence. Through Persistent Scatterer Interferometry (PSI), InSAR provided precise surface subsidence measurements, identifying localized subsidence rates up to \(-2.7\) cm/year, particularly at critical points such as Point C. These findings suggest that subsurface voids, soil compaction deficiencies, and groundwater fluctuations are key factors influencing ground instability. GPR contributed valuable insights into shallow subsurface anomalies, revealing disintegrated fill layers and air voids, while ERT extended the analysis to greater depths, detecting resistivity anomalies associated with karstic cavities and groundwater pockets.  
Borehole drilling further validated the geophysical data, confirming that weathered chalk formations, karstic voids, and variations in groundwater flow significantly contribute to subsidence in the study area. The integration of these methods allowed for a more precise and reliable assessment of structural vulnerabilities, enhancing the ability to delineate affected zones accurately. InSAR's high-resolution monitoring capabilities, combined with GPR's detailed subsurface imaging and ERT's ability to detect deeper anomalies, created a robust framework for evaluating ground stability.  
By leveraging the strengths of these techniques, this study provides a roadmap for improving predictive models and developing targeted mitigation strategies. The results emphasize the need for a multi-method approach in geotechnical investigations, ensuring a more comprehensive understanding of subsurface conditions. Future research should focus on implementing real-time monitoring systems and refining geophysical methodologies to enhance infrastructure resilience in geologically complex regions.  
Conceptualization, M.F., A.H. and V.F.; methodology, M.F., A.H., D.S. and V.F.; software, W.E. and D.G.B.; validation, M.F. and V.F.; formal analysis, M.F., A.H. and V.F.; investigation, M.F., A.H. and D.S.; resources, M.F., A.H. and D.S.; data curation, V.F.; writing--original draft preparation, M.F., A.H. and V.F.; writing--review and editing, M.F. and A.H.; visualization, M.F., A.H. and D.S.; supervision, V.F., W.E. and D.G.B.; project administration, V.F. All authors have read and agreed to the published version of the manuscript.  
V.F. acknowledges the support from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie RISE project EffectFact, grant agreement No. 101008140.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by Geo-Scope Ltd. Authors [PERSON] and [PERSON] were employed by Caelus.space. The remaining authors declare that the research was conducted without commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Composite Optimization
### Sensitivity Analysis  
In order to obtain an image regarding the effect of the alteration of the values of the different variables of the problem, which represent the different laminae categories, in the robustness of the composite, it was decided to execute a sensitivity analysis. The criterion of the robustness of the composite was selected as it consists of the criterion of validating the alternative designs. The effect of reduction in laminae plies per variable on the robustness of the composite was studied and the results were examined to identify the variables with greatest impact. The results from the execution of this study are presented in Table 15. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{4}{c}{**Convergence Analysis for Optimized Lamination**} \\ \hline \multirow{2}{*}{**Element Size**} & \multicolumn{2}{l}{**Normal Stress**} & \multicolumn{2}{l}{**Normal Stress**} & \multicolumn{2}{l}{**Normal Stress**} & \multicolumn{2}{l}{**Shear Stress**} & \multicolumn{2}{l}{**Shear Stress**} \\  & \(\mathbf{(\sigma_{x})}\) & \(\mathbf{(\sigma_{y})}\) & \(\mathbf{(\sigma_{x})}\) & \(\mathbf{(\tau_{xy})}\) & \(\mathbf{(\tau_{yx})}\) & \(\mathbf{(\tau_{xz})}\) \\ \hline
27.5 mm & 173.88 (MPa) & 60.52 (MPa) & 6.16 (MPa) & 39.19 (MPa) & 12.58 (MPa) & 19.59 (MPa) \\
22.5 mm & 145.71 (MPa) & 75.49 (MPa) & 4.54 (MPa) & 56.78 (MPa) & 12.43 (MPa) & 19.33 (MPa) \\
15 mm & 143.85 (MPa) & 69.88 (MPa) & 4.57 (MPa) & 47.45 (MPa) & 12.35 (MPa) & 16.65 (MPa) \\
**Value of the failure criterion:** & 0.76 & \multicolumn{4}{c}{(Results from experiments with 15 mm element size)} \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Convergence checks on recalculated maximum absolute stress values. Figure 19: Location of the maximum computed value of failure criterion on the stressed monocoque. Figure 20: Depiction of the deformation of the monocoque shape. The sensitivity analysis was conducted by computing the elementary effects (E.E) of each variable on the model output across seven scenarios. For each scenario, the E.E. was calculated as the difference between consecutive output values, assuming a unit step change in input variables. The mean of the absolute elementary effects (\(\mu\)*), mean E.E. (\(\mu\)), and
## 2 Related Studies and the Current Contribution
### Current Contribution
This transformation aims to maximize the long-term rewards while adhering to the available network resource constraints. * **An inner-outer online optimization algorithm has been devised to address the optimal resource allocation policies for RIS-assisted mobile ad-hoc networks (MANET), even in uncertain environments. ** As the network exhibits high dynamism and complexity, the D-UCB algorithm is employed in the outer network for RIS and spectrum selection. In the inner network, the TD3 algorithm is utilized to acquire decentralized insights into RIS phase shifts and power allocation strategies. This approach facilitates the swift acquisition of optimized intelligent resource management strategies. The TD3 algorithm features an actor-critic structure, comprising three target networks and two hidden layer streams in each neural network to segregate state-value distribution functions and action-value distribution functions. The integration of action advantage functions notably accelerates the convergence speed and enhances the learning efficiency.