esa-sceva/satcom-chunk-collection huggingface dataset:
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 5 Discussion  
The quantitative and qualitative results confirm that the enhanced DeepLabV3+ model, with the integration of the underwater light propagation model, multi-scale feature extraction, and curvature flow-based guidance, significantly improves the detection and segmentation of underwater cracks. (1) Effectiveness of the underwater light propagation model: The light propagation model plays a crucial role in compensating for the distortions caused by underwater lighting, especially in turbid and low-light conditions. The enhancement of contrast and color balance allows the model to perform better in environments where the original DeepLabV3+ struggles. (2) Multi-scale feature extraction: Small cracks, often present in underwater structures due to corrosion or fatigue, are challenging to detect with standard segmentation networks. The multi-scale feature extraction mechanism proves to be particularly effective in addressing this challenge, as it allows the model to detect both small and large cracks by capturing features at various scales. This is evident in Table 2, where the IoU for small cracks improves substantially compared to other models. (3) Curvature flow-based guidance: The curvature flow-based guidance improves the model's ability to preserve the geometric properties of cracks, particularly at their boundaries. This allows the model to more accurately segment the cracks, as demonstrated in Figures 3 and 4, where the enhanced model shows superior delineation of crack boundaries compared to the baseline models. (4) Robustness in challenging conditions: Our model demonstrates a strong ability to maintain performance even in highly turbid water and low-light environments, which are common in real-world underwater inspection scenarios. The enhanced DeepLabV3+ model shows only a moderate degradation in performance, with an average of \(-6.2\%\) drop in IoU under highly turbid and low-light conditions (Table 3). In contrast, the original model suffers a greater performance drop, demonstrating the robustness of the proposed model. Dataset expansion (450\(\rightarrow\)1037 images) was motivated by the inverse-\(\sqrt{n}\) generalization error bound. Empirically, doubling \(n\) improved small-crack IoU by 8.2% (Table 5), confirming that larger datasets mitigate overfitting to sparse features  
\begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **450 Images** & **1037 Images** & \(\Delta\) \\ \hline Overall IoU (\%) & 74.3 & 82.5 & +8.2 \\ \hline Small Crack IoU (\%) & 69.9 & 78.1 & +8.2 \\ \hline Crack Det. Acc (\%) & 70.7 & 79.8 & +9.1 \\ \hline IoU Std. Dev. (Turbidity) & 4.8 & 4.2 & \(-12.3\%\) \\ \hline \end{tabular} Note: \(\Delta\) is change rate.  
\end{table}
Table 5: Effect of dataset size on model performance.  
(e.g., rare crack morphologies). Diversification (adding turbid/low-light scenes) further reduces distributional shift, aligning with the covariate shift minimization theory.
## 2 Data and Methods
### Radiometric Measurements  
The in situ remote sensing reflectance, \(R_{\mathrm{rs}}(\lambda)\), was calculated based on the measurements of radiance and irradiance sampled with the Radiometer Incorporating the Skylight-Blocked Apparatus (RISBA) [69], Hyper Spectral Radiometer HyperPro free-fall profiler (Satlantic, Inc. Halifax, Nova Scotia, Canada), or above water radiometers [70]. The \(R_{\mathrm{rs}}(\lambda)\) spectra from 350-800 nm with different spectral increments were interpolated to 1 nm resolution.  
Standard Level 3 MERIS \(R_{\mathrm{rs}}(\lambda)\) was acquired from the National Aeronautics and Space Administration (NASA) ocean color website ([[https://oceancolor.gsfc.nasa.gov](https://oceancolor.gsfc.nasa.gov)]([https://oceancolor.gsfc.nasa.gov](https://oceancolor.gsfc.nasa.gov))). The HPLC data from BATS were matched to Level 3 MERIS 8-day products, at 4 km resolution and plus or minus one pixel (3 \(\times\) 3 window). This criterion, although less restricting than NASA's 3-h window for data and algorithm validation [71], was adopted to maximize the number of match-ups.  
The MuPI model was applied to MERIS \(R_{\mathrm{rs}}(\lambda)\) imagery from 2002-2012 to obtain the seasonal variation of chlorophyll \(a\), \(b\), \(c\), photoprotective and photosynthetic carotenoids concentrations ([Chl-a], [Chl-b], [Chl-c], [PPC], [PSC]) near Bermuda. As examples, global maps of these five different pigments were also obtained from MERIS \(R_{\mathrm{rs}}(\lambda)\) imagery of 2007. The ratios of these concentrations to [Chl-a] are also presented to highlight their independence from chlorophyll \(a\).
## 3 Results and Discussion
### Antioxidant Stability Assessment Results of GCP-II
#### 3.6.2 Impact of Food Ingredient Components on Antioxidant Stability of GCP-II  
The effects of common food ingredient components on the antioxidant stability of GCP-II are illustrated in Figure 6. Among them, the ferrous ion chelating ability of GCP-II was largely unaffected by various concentrations and types of food ingredients, remaining stable at over 95%. In contrast to its thermal stability, the superoxide anion scavenging capacity of GCP-II was more significantly influenced by food components. Both NaCl and citric acid can significantly reduce its stability when their concentrations reach 4% and 0.2%, respectively. Among them, the superoxide anion scavenging ability of GCP-II was most affected by the composition of food ingredients. When the concentration of NaCl reached 1%, the superoxide anion scavenging retention rate decreased to 77.15%, and when the concentration of NaCl reached 4%, the superoxide anion scavenging retention rate decreased to 59.16%. In addition, in the experiment on the effect of different concentrations of citric acid on the antioxidant stability of GCP-II, the ABTS\({}^{+}\) scavenging ability was also greatly affected, and its activity retention rate was 71.57% when the concentration of citric acid was 0.2%. Therefore, during the processing and storage of GCP-II, it was advisable to avoid high-concentration salt environments and acidic conditions to the greatest extent possible.  
Figure 5: The maintenance rate of the antioxidant activity of GCP-II in the range of 4 °C to 100 °C.
## 3 Results and Discussion
### Photocatalytic Performance Results
#### 3.5.1 Effect of Catalyst Loading  
In photocatalysis, the degradation efficiency is affected by the photocatalyst dose. To examine the effect of the catalyst dose on the photodegradation of MO for 30 min, the amount of TiO\({}_{2}\)/zeolite was varied from 0.2 to 1 g at a constant MO concentration of 10 ppm. As shown in Figure 9, as the amount of photocatalyst increased from 0.2 to 1 g, the MO degradation efficiency increased (0%, 45%, 58%, 88%, and 95% for 0.2, 0.4, 0.6, 0.8, and 1 g, respectively). This occurs because a higher catalyst concentration offers additional active sites for redox reactions. In other words, irradiation produces more charge carriers to participate in redox reactions [60].  
Figure 8: \(\ln(C_{0}/C_{t})\) as a function of time for the photodegradation of methyl orange at concentrations of (**a**) 10 ppm and (**b**) 20 ppm for TiO\({}_{2}\)/zeolite and pure TiO\({}_{2}\).
## 4 Test Experiments and Discussion of DMMAEO
### Impacts of Enhancement Mechanisms on EO  
This subsection investigates the impacts of three enhancement mechanisms introduced in the DMMAEO, including the dynamic multi-population guidance mechanism, dynamic Gaussian mutation-based sub-population concentration updating mechanism, and dynamic Cauchy mutation-based sub-population equilibrium candidate generation mechanism. For this purpose, a performance evaluation of the canonical EO and four EO variants (DMEO, GMEO, CMEO, and DMMAEO) is conducted on the 29 representative test functions. Among the four EO variants, DMEO means that EO is only in combination with dynamic multi-population guidance mechanism; GMEO means that EO is only in combination with the dynamic Gaussian mutation-based sub-population concentration updating mechanism; CMEO means that EO is only in combination with the dynamic Cauchy mutation-based sub-population equilibrium candidate generation mechanism; and DMMAEO indicates that EO is in combination with the three enhancement mechanisms simultaneously. Tables 1-5 list the comparison results of the Ave., Std., Wilcoxon signed rank test, and Friedman test from the canonical EO and four EO variants mentioned above. These comparison results are obtained by implementing each algorithm 30 times independently across each test function. The best results achieved by the various EOs on the 29 repre  
sentative test functions, as well as the top-performing algorithm within the different EO variants, are presented in bold for highlighting the comparative results. According to the results in Tables 1-4, all four EO variants exhibit superior performance compared to EO across most test functions, which reveals that each of the three enhancement mechanisms contributes to improving the optimization ability of the canonical EO. Among the four EO variants, the DMMAEO achieves the best results across 27 of the 29 representative test functions and shows better optimization capability than other variants of EO in most cases, which means that the DMMAEO can obtain the best optimization ability by combining the three enhancement mechanisms simultaneously. Additionally, Figure 2 exhibits the convergence curves for the canonical EO and four EO variants across nine test functions, which is utilized to provide a visual analysis of convergence performance. As per the results given by Figure 2, the DMMAEO exhibits superior convergence speed and convergence accuracy compared to the other four EO variants.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 6 Numerical Results and Discussions
That is, the sender located near the receiver has a high channel access probability. Hence, the fairness of the contention-based MAC protocols, such as SFAMA and CSMA/CA in CH-MAC, is low. Although CT-MAC in DCO-MAC is contention-based, the level-1 node can invite other level-2 nodes to join in the transmission after a successful channel reservation, which means that the level-2 nodes have the same opportunity to transmit data packets, regardless of their locations. Moreover, as RSV-MAC in DCO-MAC is receiver-initiated, the receiver schedules the senders' transmission in a fair manner. Therefore, the DCO-MAC protocol has a good fairness performance. As a receiver-initiated protocol, the fairness of the RIPT protocol also performs well. To show the influence of the number of level-2 sub-networks on the performance, we changed the number of level-2 sub-networks in the simulation topology. Figure 18 shows the impact of the number of level-2 sub-networks on the performance of the DCO-MAC, SFAMA, RIPT, and CH-MAC protocols, in terms of the normalized throughput. From Figure 18, we observe that the normalized throughput of the SFAMA and CH-MAC protocols decreases as the number of level-2 sub-networks increases. As SFAMA and CSMA/CA in CH-MAC are contention-based protocols, the collision probability in the level-1 sub-network increases along with the increase of the number of level-2 sub-networks, which results in the performance degradation. As RIPT and RSV-MAC in DCO-MAC are receiver-initiated protocols, the receiver schedules the data transmission of the sensor nodes to avoid the collision. The traffic load in the level-1 sub-networks increases along with the increase of the number of level-2 sub-networks. Therefore, the normalized throughput of the receiver-initiated protocols increases. Figure 17: The fairness index of the DCO-MAC protocol.
## 3 Results and Discussion
### Effect of F-T Cycles on Resilient Modulus  
To better investigate the effect of F-T cycles on resilient modulus for the two materials, the variation curves of resilient modulus versus F-T cycles under different stress states are drawn in Figure 6.  
Figure 5: Variation curve of resilient modulus versus bulk stress for test soils after (a) 0, (b) 1, and (c) 7 F-T cycles.  
As Figures 6 and 7 show, the resilient modulus of unmodified SC is slightly higher than that of the modified soil under the same deviator stress before F-T cycles. With increasing F-T cycles, the test data for the two materials show different variation characteristics. After the first F-T cycle, the resilient moduli of both materials decrease sharply. The resilient modulus value of unmodified SC is still higher than that of the modified soil. As F-T cycles continue, the resilient modulus of unmodified SC decreases continually, while the resilient modulus of the modified soil has remained stable. After 7 F-T cycles, the resilient modulus of unmodified SC is lower than that of the modified SC. This means that after sufficient numbers of F-T cycles, the mechanical performance of the modified soil is better than that of unmodified soil.  
The calculated damage ratios of resilient modulus and the corresponding fitting equations for unmodified SC and the modified SC are listed in Tables 5 and 6.  
Figure 6: Variation curves of resilient modulus for test soils after F-T cycles of loading process (**a**) 40-1 and (**b**) 50-2.  
In Tables 5 and 6, the damage ratios of unmodified SC increase gradually with increasing F-T cycles, while the calculated data of the modified soil decrease after the first F-T cycle and then remain stable. For unmodified SC, the damage ratios in low confining pressure (\(\sigma_{\rm c}\) = 40 kPa) are close to those under the same deviator stress (\(\sigma_{\rm d}\)) in high confining pressure (\(\sigma_{\rm c}\) = 50 kPa). As for the modified SC, the damage ratios in high confining pressure (\(\sigma_{\rm c}\) = 50 kPa) are slightly less than those in low confining pressure (\(\sigma_{\rm c}\) = 40 kPa). Furthermore, the damage ratios of both materials under the deviator stress of 25 kPa (\(\sigma_{\rm d}\) = 25 kPa) are higher than those under the deviator stresses of 15 and 5 kPa (\(\sigma_{\rm d}\) = 15 kPa and \(\sigma_{\rm d}\) = 5 kPa). It revealed that for both materials, deviator stress is the main factor affecting the damage ratio of the resilient modulus after F-T cycles.  
The correlation coefficients of the fitting equations for both materials are strong. The constant term \(y_{0}\) represents the damage ratio of materials after sufficient numbers of F-T cycles. It can be called a stable damage ratio after F-T cycles. The fitting results indicate that the stable damage ratios of the modified soil are better than those of unmodified soil. Therefore, the modified SC will have better application potential than unmodified SC in seasonally frozen regions.
## 3 Data and Method
### Method
#### 3.2.3 Kriging  
Spatial simulation used ordinary kriging (OK), a distance-weighted estimation algorithm that utilizes a variogram to characterize the variability within a given area and to optimize the weights assigned to the data points [48; 49; 50]. OK provides an estimate at an unobserved location of variable \(Z(x)\) based on the weighted average of adjacent observed sites within a given area. The theory can be described by considering an intrinsic random function denoted by \(Z(x_{i})\), where \(x_{i}\) represents sample locations [51]. An estimate of the weighted average given by the ordinary kriging predictor at an unsampled site, \(Z^{\prime}(x_{0})\), is defined by  
\[Z^{\prime}(x_{0})=\sum_{i=1}^{n}\lambda_{i}Z(x_{i}) \tag{1}\]  
where \(Z(x_{i})\) is the value of variable \(Z\) at sample point \(x_{i}\), \(\lambda_{i}\) is the weight assigned to each \(Z(x_{i})\) value, and n is the number of values used. With OK, the weights sum to one to ensure that the estimate is unbiased [49; 52]:  
\[\sum_{i=1}^{n}\lambda_{i}=1 \tag{2}\]  
The variogram that describes the spatial structure in data distribution using a geostatistical characteristic [49; 53] must be estimated prior to ordinary kriging [54]. The variogram is half the expected squared difference between paired data values \(Z(x)\) and \(Z(x+h)\) to the lag distance \(h\), by which locations are separated [55; 56]:  
\[r(x,h)=\frac{1}{2}E[Z(x)-Z(x+h)]^{2} \tag{3}\]  
It was impossible to achieve a reliable variogram across the whole region because of highly variable properties existing among different geological units. A multiple variogram approach was hence employed in this study to better characterize the heterogeneously distributed properties by analyzing variogram parameters for every single unit separately through the Variogram Analyzer tool (SKUA-GoCAD).
## 3 Problem Formulation
### Attributes Similarity Scoring Function
: _(Attribute similarity score of the graph) Given a subgraph \(H\subseteq G\) and a query node \(q\), the attribute similarity score of \(H\) is expressed as \(TScore(q,H)\), which is the minimum value of the similarity scores between \(q\) and all others nodes in \(H\)--that is, \(TScore(q,H)=\underset{v\in V_{H}}{\text{min}}TScore(q,v)\)._  
We formally define the time-weighted community search (TWC) based on user interest, as shown below. **Problem 1**. : (TWC-problem) Given an attributed graph \(G=(V,E)\), a query node \(q\), and a non-negative integer \(k\), the time-weighted community search based on user's interest can return a subgraph \(H\subseteq G\), satisfying the following conditions:  
1. Connectivity: \(H\) is a connected graph containing \(q\). 2. Structure cohesiveness: \(\forall v\in H\), \(deg_{H}(v)\geq k\). 3. Attribute score maximum: While satisfying (1) and (2), the attribute similarity score of \(H\), \(TScore(q,H)\), is the highest, where \(TScore(q,H)=\underset{v\in V_{H}}{\text{min}}TScore(q,v)\). 4. There does not exist another subgraph \(H^{\prime}\supseteq H\) satisfying the above three properties. We re-observe Figure 1 and let \(q\) be the query vertex, \(k=3\). Since \(TScore(q,H_{1})=0.226<TScore(q,H_{2})=0.67\) and a 4-core subgraph does not exist, we select \(H_{2}=\{q,v_{0},v_{1},v_{5}\}\) as the optimal solution.
## 4 Results and Analysis
### The Results Obtained from the DGNN Model
Previous research has demonstrated lower accuracy rates and methodologies that may not fully address the evolving needs of cognitive workload classification. However, the DGNN model stands out prominently in this analysis, showcasing the highest accuracy among the compared studies. Specifically, our model achieves an impressive accuracy rate of 99.45% in accurately classifying cognitive workload levels using EEG data. Figure 11 represents the plotted comparison of state-of-the-art results from Table 1, illustrating the superior performance of our DGNN model relative to these existing methodologies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Labels** & **Precision** & **Recall** & **F1 Score** \\ \hline Low & 1.00 & 1.00 & 1.00 \\ Average & 0.98 & 0.98 & 0.98 \\ High & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification metrics. Figure 10: Confusion matrix of DGNN model.
# The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
## 3 Antimicrobial Compounds Produced by Bacteria  
For centuries, fermentation was commonly used for the transformation of perishable food and beverages into stable and safe food products. Nowadays, they appear to have become an alternative to chemical preservatives and additives. LAB naturally presented in food products as a non-starter ingredient provides a broad spectrum of antimicrobial activity, ensuring food preservation during processing (i.e., traditional fermentation) and storage as well. LAB strains are well-known and widely used in food manufacturing due to their probiotic characteristics, human health benefits, sensory attributes as well as their ability to produce antibacterial chemicals. The appropriate LAB strains selected for various food matrices can be employed as a starter ingredient for purposes of food processing or as an additive to prevent food spoilage. To accomplish the desired effect, the challenge is to keep the viable LAB strains in adequate numbers (\(>\)10\({}^{6}\) log/CFU) [10].  
According to [PERSON] et al., LAB produce antimicrobials that can be classified into three groups: (a) bacteriocins; (b) organic acids; and (c) other small molecules, i.e., diacetyl, hydrogen peroxide [5]. As a result of fermentation, bacteria produce organic acids such as lactic (_Lactobacillus_), acetic (_Acetobacter aceti_), and propionic acids (_Propionibacterium_ spp.), inducing the rapid acidification of the food matrix. LAB is acid-tolerant and the autoacidification of food serves as an antibacterial agent that effectively counteracts many competitive non-acidophile microorganisms, including putrefractive and pathogenic bacteria [2; 11].
## 3 Results
Examining Figure 7 and analyzing the \(TT\) behavior for all analyzed combinations, it is evident that a smaller \(TT\) value generally corresponds to a smaller \(\varepsilon_{RMS}\). An illustration of the estimated dynamic \(GC\) value for the minimum \(TT\) value is given in Figure 2:0 Figure 9 shows the optimum window size considering \(TT\) for different \(M_{GC}\) and respective \(TT\). In this case, it can also be seen that as the order increases beyond 11, the optimum window size also increases as the order \(M_{GC}\) increases. Contrary to Figure 6, \(TT\) does not monotonically increase with increasing order \(M_{GC}\). Considering Figures 6 and 9, the window size \(M_{GC}\) needs to be optimized according to both the error \(\varepsilon_{RMS}\) and the transition time \(TT\). We propose the criterion of the product of both \(\varepsilon_{RMS}\) and \(TT\). The product with respect to different window sizes and orders \(M_{GC}\) is shown in Figure 2:1  
Figure 2:2 Dynamic \(GC\) values estimated using sliding window analysis with a window size of 400 ms, a regression order of 12, and a minimum \(TT\) of 0.021 s.  
Figure 2:3 Optimum window size in terms of the minimal \(TT\) with respect to selected regression order \(M_{GC}\) (blue asterisks) and corresponding minimal \(TT\) (red triangles). An illustration of the estimated dynamic \(GC\) value for the minimum product \(TT\) and \(\varepsilon_{RMS}\) value is given in Figure 2:4 It can be observed that \(TT\) predominantly influences dynamic \(GC\) behavior (due to the similarity between Figures 8 and 11). This can be explained by the small variations in \(\varepsilon_{RMS}\) for the optimum window size after order 9. Finally, the optimum window size considering the \(TT\) and \(\varepsilon_{RMS}\) product is presented in Figure 2:5  
Figure 2:6 Dynamic GC estimated using sliding window analysis with a window size of 400 ms, a regression order of 12, and a minimal product of \(TT\) and \(\varepsilon_{RMS}\) equaling 0.02. Figure 2:7 Product of \(TT\) and \(\varepsilon_{RMS}\) for window sizes ranging from minimal to 3000 ms with a step of 25 ms, and for different regression orders from 5 to 35.