esa-sceva/satcom-chunk-collection huggingface dataset:
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Segmentation Algorithms
#### 3.2.6 Active Contour Models: Snakes  
The general idea of active contour model is to modify a curve based on certain forces that push the boundaries of such curve until it reaches the border of the object of interest. Usually, the features are lines, edges, and/or object boundaries [34]. Typically, most snake-based algorithms follow the information about the gradient of the image, which means the curve will try to evolve towards the edges of the object. Nonetheless, if the object does not contain well defined edges, or it has been previously smoothed in order to reduce the noise, this algorithm might fail in finding the shape of the object. As a consequence, the outcome will be either a contour that is smaller or larger than the object's boundaries, or in the worst case, the contour will be reduced until it disappears.  
For the implementation, Matlab toolbox provides some functions for manipulating planar, closed splines to implement image or video segmentation by means of deformable (or active) contours. A previous version using a Python implementation was used [22]. In this case, in a Matlab version only a sigma value and a number of iterations are used as an input parameters. Contour topology is managed in a way that should allow changes similar to what can be observed with level sets (merging and splitting but no whole creation).
## 2 Materials and Methods
### Methods
#### 2.3.2 GPP Product Accuracy Assessment  
For the validation of the large-scale remote-sensing products, there is a lack of the more effective methods, and station data remain a crucial basis for the validating remote-sensing products [38; 39]. While many GPP products have been validated in previous studies within various regions, there has been limited validation of GPP products specific to China [40; 41]. Therefore, this article is based on the daily GPP observations from 11 flux sites in the ChinaFLUX observation network, further aggregated to an 8-day scale. To mitigate the impact of the data noise, geometric mismatch, and spatial heterogeneity on validation results, a 3 \(\times\) 3 pixel mean filter mask was generated using the site center coordinates. This mask was used to extract values from PML-V2 and MODIS remote-sensing products at corresponding locations and compare them to the site's values to demonstrate their reliability and acceptability for subsequent analysis. The accuracy evaluation metrics include the determination coefficient (\(R^{2}\)) and the root mean squared error (\(RMSE\)).  
The \(R^{2}\) can assess the goodness of fit of a regression line to the observed values. The range of the \(R^{2}\) values is from 0 to 1, and the closer \(R^{2}\) is to 1, the higher the goodness of fit of the linear regression line to the observed values. The formula for calculating \(R^{2}\) is as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{(x_{i}-P(x_{i}))^{2}}}{\sum_{i=1}^{n}{(x_{i}- \overline{x_{i}})^{2}}}, \tag{1}\]  
The \(RMSE\) can reflect the accuracy of a simulation and can assess the degree of deviation between simulated values and measured values. The formula for calculating \(RMSE\) is as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(P(x_{i})-\overline{x_{i}})^{2}}}{n}}, \tag{2}\]  
where \(P(x_{i})\) and \(x_{i}\), respectively, represent the product GPP value and the site value at the ith time point; \(n\) denotes the total number of time intervals; and \(\overline{x_{i}}\) represents the average value of site values.
## 3 Simulation Analysis and Method Comparison
### Effective Range of Frequency Detection  
For practical application, it is necessary to study the effective detection range of the coupled Lorenz system, including the amplitude and frequency of the tested signal. To this end, we obtained the bifurcation diagram of the sinusoidal signal \(A\) and the SNR curve with frequency variation using the same model and parameter settings used in Section 3.1, as shown in Figure 5.  
In Figure 5a, it can be seen that the running state of the Lorenz system varies between the multi-periodic state and the chaotic state with as the amplitude of the sinusoidal signal increases. The amplitude of the sinusoidal signal \(A=45\) is the critical point of the state transition of the chaotic system. Therefore, considering that only when the Lorenz system is running in the chaotic state can it have filtering characteristics, the detection range of the amplitude is from 0 to 45 when the Lorenz system uses the above parameters.  
In Figure 5b, the curve is the corresponding SNR value of the synchronization error obtained by changing the frequency of the sine signal submerged in the tested signal. It can be seen that the SNR of the synchronization error is greatly improved by the chaotic system compared with that of the tested signal. The improvement effect is greater than 30 dB in the range of 0-200 Hz. This means that the accuracy of the traditional method can be greatly improve when the data preprocessed by this method is used for analysis.
# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 4 Discussion
### Uncertainty and Future Work  
In this study, the MODIS products GPP and ET were used to calculate the WUE for Central Asia from 2001 to 2021, with annual averages in the range of 2.584-3.607 gCkg\({}^{-1}\)H\({}_{2}\)O, similar to previous studies. However, the calculation of WUE in arid areas from MODIS data led to an underestimation [63; 64; 25; 65]. This result is inevitable due to the underestimation of ET from MODIS data in arid areas, which may lead to some uncertainty in the calculation results. Similarly, this study did not consider vegetation eaten by grazing animals, leading to a certain underestimation of GPP and ET. Uncertainty in the calculation of WUE based on MODIS is usually acceptable; for example, [PERSON] et al. validated the results of the MODIS calculation of WUE with the help of flux tower data (32 sites), with R\({}^{2}\) values ranging from 0.74 to 0.963 for different vegetation types [20]. Furthermore, the WUE of Central Asia in this study was calculated without considering desert, due to the sparse vegetation and exposed soil surface conditions in arid areas. The normalized difference vegetation index (NDVI) based on remote sensing data does not reflect surface vegetation conditions. The MODIS products GPP and ET, which are estimated based on NDVI, also deviate from the real values in deserts, so not considering deserts improves the reliability of the results to some extent [27]. Studies are increasingly using flux data in combination with remote sensing data to quantify research at regional to global scales. The main problem in Central Asia is the great lack of long-term flux tower data, and this study could only use MODIS data to calculate WUE. In addition, it was not possible to establish empirical relationships between WUE and each explanatory variable by training with rich data. Therefore, the network of dryland covariance flux tower observations needs to be expanded in the future to provide data support for the development of dryland-specific models [10]. Changes in carbon and water fluxes in dryland ecosystems are more specific and less predictable than in other ecosystems [66]. This study demonstrates the importance of the interaction between atmospheric and vegetation factors in driving WUE in Central Asia, and the increased awareness of climate change and the development of appropriate ecological restoration policies may facilitate the planting of environmentally adaptive vegetation and, thus, the maintenance and development of available water resources in Central Asia. Our results can be used to improve future climate change prediction ability, optimize water resources management, and safeguard food production.
## 4 Experimental Settings and Results
### Dataset  
Upon analyzing publicly accessible benchmark datasets, we have selected processed network traffic datasets from the newly created TON_IoT collection of datasets. In order to simulate the complexity and scalability of Industrial Internet of Things and Industry 4.0 networks, the Internet of Things lab developed a new testbed that can connect a large number of virtual machines, physical systems, hacking platforms, cloud and fog platforms, Internet of Things, and Industrial Internet of Things sensors [34; 35]. A summary of the main characteristics of the ToN_IoT network traffic dataset is presented in Table 2.  
Columns titled \"%\" represent the percentage of the records of the particular class in the pool of the corresponding dataset part. This dataset is heavily unbalanced, i.e., \"mitm\" attack records compose only 0.005 percent of the total count of all records. The original data was preprocessed using the following steps: unique values for each non-numeric column were extracted; then dictionary functionality was used to replace all string values with the corresponding numeric indices from the dictionaries. This method processed 27 columns in total. We did not make any further modifications. We have not tried to modify the data and artificially balance the dataset because, in real-life application scenarios, the learning process can only use the data that is physically \"on the wire,\" and this data is inherently unbalanced.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 3 Materials and Methods  
### Materials  
The influencing factors can be divided into two categories: internal factors related to the characteristics of RAP, and external factors related to the processing conditions. In this study, two types of materials (RAP from different old roads and artificial RAP produced in the laboratory) were used to evaluate the influence of preheating temperature and aging of RA binder, respectively, on the activation of RA binder.
## 3 Results and Discussion
### Usage Characteristics of the Three Terms  
The usage characteristics of the three terms were obtained by integrating information from the IMO documents and collected articles. Figure 6 shows the results. The connecting lines represent the former segments contained in the latter parts. Detailed information can be found in Sections 3.1-3.3.  
Human element, human factor, and human error have six segments, five segments, and five segments, respectively. Human error is easier to identify when compared with the other two terms because its five parts have distinct characteristics. Perception errors, decision errors, and execution errors are the three stages of errors when performing tasks. Individual errors are supplemental in case some personal errors are missing. Team management errors refer to errors in organizational management.  
Human element and human factor are more challenging to identify because there are intersections in their contents, especially those marked by red dotted boxes. Initially, the people factors (human element) and the personal factors (human factor) look alike, but little differences exist. People factors can be factors of a person, a group, or a team, whereas personal factors refer to factors of one person. However, group factors and team factors are also parts of the term human factor. Hence, distinguishing them from this viewpoint is challenging. The three parts (ship factors, external influences and environment, and working and living conditions) are easily identifiable factors. Therefore, if it is clear that a document will involve the above three parts, the term human element is suggested; otherwise, when facing ambiguous preconditions, the term human factor is preferred. The last two parts are shore-side management and organization on board, which have similar meanings to the organizational factors. Consequently, identifying the human element and the human factor from these two parts is impossible. Drive is a segment only for the human factor. Therefore, the human factor must be the first preference for documents focusing on the drive.  
Figure 6: The usage characteristics of the three terms.  
Hence, human error should be identified first. The human element and human factor should be determined according to different situations. If the documents face ambiguous preconditions, the term \"human factor\" should be the first choice; otherwise, \"human element\" is preferred. For documents exploring the drive, the term human factor is preferable. If the documents focus on people factors, shore-side management, or organization on board, the authors should decide which term they prefer to use.
# Effect of Fermentation Technology and Storage Time on the Quality of Salami-Type Sausages
## 3 Results and Discussion
### Influence of Fermentation Technology on the Microbiological Characteristics of Salami-Type Sausages
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fermentation Technology**} & \multicolumn{4}{c}{**Storage Time [Days]**} \\ \cline{2-5}  & **0** & **21** & **42** & **63** \\ \hline \multicolumn{5}{c}{Hardness [N]} \\ \hline Warm & 63.38 \(\pm\) 3.75 aB & 54.23 \(\pm\) 6.95 aA & 53.16 \(\pm\) 4.60 aA & 47.43 \(\pm\) 3.10 aA \\ Cold & 53.88 \(\pm\) 2.76 aA & 58.36 \(\pm\) 1.36 aB & 53.96 \(\pm\) 1.52 aA & 62.26 \(\pm\) 0.60 bB \\ \hline \multicolumn{5}{c}{Cohesiveness [-]} \\ \hline Warm & 0.48 \(\pm\) 0.02 aA & 0.51 \(\pm\) 0.02 aA & 0.51 \(\pm\) 0.01 aA & 0.48 \(\pm\) 0.03 aA \\ Cold & 0.54 \(\pm\) 0.03 aA & 0.52 \(\pm\) 0.02 aA & 0.53 \(\pm\) 0.01 aA & 0.52 \(\pm\) 0.01 aA \\ \hline \multicolumn{5}{c}{Springiness [-]} \\ \hline Warm & 0.63 \(\pm\) 0.05 aA & 0.56 \(\pm\) 0.04 aA & 0.55 \(\pm\) 0.03 aA & 0.53 \(\pm\) 0.03 aA \\ Cold & 0.67 \(\pm\) 0.02 aA & 0.68 \(\pm\) 0.01 aB & 0.69 \(\pm\) 0.02 aB & 0.70 \(\pm\) 0.02 aB \\ \hline \multicolumn{5}{c}{Chewiness [N]} \\ \hline Warm & 18.23 \(\pm\) 1.76 aA & 16.57 \(\pm\) 2.27 aA & 15.31 \(\pm\) 1.62 aA & 12.73 \(\pm\) 1.24 aA \\ Cold & 19.57 \(\pm\) 1.16 aA & 19.93 \(\pm\) 0.62 aA & 18.50 \(\pm\) 1.15 aA & 21.52 \(\pm\) 0.82 aB \\ \hline \multicolumn{5}{c}{Shear force [N]} \\ \hline Warm & 50.93 \(\pm\) 0.87 bB & 46.06 \(\pm\) 2.80 bA & 36.37 \(\pm\) 0.61 aA & 35.60 \(\pm\) 1.37 aA \\ Cold & 43.75 \(\pm\) 2.38 bA & 44.48 \(\pm\) 4.34 bA & 31.76 \(\pm\) 3.15 aA & 32.37 \(\pm\) 2.31 aA \\ \hline \multicolumn{5}{c}{Penetration force [N]} \\ \hline Warm & 50.93 \(\pm\) 0.87 bB & 45.08 \(\pm\) 1.89 aA & 43.18 \(\pm\) 3.75 aA & 44.02 \(\pm\) 1.01 aA \\ Cold & 42.00 \(\pm\) 2.20 aA & 41.05 \(\pm\) 5.28 aA & 42.77 \(\pm\) 1.62 aA & 46.05 \(\pm\) 1.50 aA \\ \hline \multicolumn{5}{c}{Different lowercase letters between mean values \(\pm\) standard deviations in the row indicate significant differences (grouping variable: storage time), \(p\leq 0.05\). Different uppercase letters between mean values \(\pm\) standard deviations in the column for a given parameter indicate significant differences (grouping variable: fermentation technology), \(p\leq 0.05\).} \\ \end{tabular}
\end{table} 
Table 6: Changes in the textural properties of salami-type sausages fermented with “warm” and “cold” technology, stored for 63 days. WF sausages on the last day of storage (Table 7) was most likely caused by biochemical transformations that led to unfavorable sensory changes. One of the main factors may have been the significant level of fat hydrolysis (see Table 3). [PERSON] et al. (2015) showed that the fermentation conditions significantly influence the biochemical changes observed in sausages during storage, thereby playing a pivotal role in shaping these products' sensory acceptability and desirability.
## 3 Overviews of the Articles Published in the Special Issue
### Fatigue  
[PERSON] and [PERSON] [Contribution 5] used various machine learning models, including artificial neural networks, random forest, and boosting models, to predict the concrete fatigue life when subjected to compression. Three data files were extracted from the original data: the original data, the data after removing outliers, and the average data after removing outliers. After removing the sustained concrete strength, which was considered as the seventh independent parameter in the original data, the use of six input parameters only (concrete compressive strength, ratio of height to width, shape, maximum stress level, stress ratio, frequency) resulted in the improvement of the determination coefficient in the boosting models. Finally, the results of the Permutation Feature Importance tests demonstrated that the maximum stress level and frequency are the most critical independent parameters, while the ratio of height to width and shape are very weak influencing parameters.  
[PERSON] and [PERSON] [Contribution 9] set out with a focus on assessing the bonding of carbon-fiber-reinforced polymer (CFRP) rods in ordinary concrete, as well as ultra-high-performance fiber-reinforced concrete (UHPFRC) under cyclic loads. This study aimed to develop an interface model between the CFRP rods and concrete matrix. It sought to clarify the bonding mechanism of CFRP rods in concrete structures. After fabricating two rectangular specimens which were connected through the CRFP rod, dynamic actuation tests subjecting the specimens to cyclic loads were simulated using the finite element program ABAQUS. Moreover, a constitutive model of the CFRP-concrete interface was formulated from the analytical results. Compared to the non-analytical interface element, the new analytical interface element resulted in a 10% improvement in displacement and a 42% improvement in pull-out force for the UHPFRC. Using their analytical model, the interaction between the CRRP rod and the concrete matrix can be more accurately predicted under cyclic loading.
# Damping Ratio of Sand Containing Fine Particles in Cyclic Triaxial Liquefaction Tests
## 5 Conclusions  
In this study, the influence of fine particles on the evolution of damping ratio during the liquefaction process was investigated by performing three sets of undrained cyclic triaxial tests on various sand-fines mixtures with the density index of sand matrix equal to 0.00. The obtained results can be used to draw the following conclusions:  
1. For test specimens containing fine particles reconstituted with the dry tamping method, the addition of fines provides a negative contribution to the global soil structure; consequently, the liquefaction potential rises significantly. From the microscopic viewpoint, this weakening effect can be attributed to the fact that the presence of fine particles can possibly break a part of strong force chains which bear the main external loading.
2. Although the liquefaction potential is indeed affected by the presence of fine particles, the evolution of the damping ratio remains shear strain-dependent, regardless of the fines contents. This is due to the similar positive influence of fine particles on the relative sliding between coarse sand grains during both the loading and unloading stages. Thus, the damping ratio manifests itself invariant on the macroscopic scale in triaxial tests.  
Methodology, Z.Z.; formal analysis, J.L.; writing-original draft, Z.Z., J.Z. and H.Z.; supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.  
The financial supports provided by the National Natural Science Foundation of China (Grant Nos. 41931286 and 41672348) are deeply acknowledged.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions and privacy.  
The authors declare no conflict of interest.  
Figure 11: Schematic of soil fabrics in the process of (**a**) loading and (**b**) unloading.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 5 Experiment Simulation
#### Simulation Results
This is consistent with the relationship shown in Figure 3. It is because the slower the energy consumption of the sensor, the longer the node lifetime. Furthermore, the LEACH randomly selects nodes as CHs with a certain probability in each round. This seems to be able to delay the death process of the elected CH nodes. However, if the elected CHs are far from the cluster center, the transmission energy consumption of CMs will increase. Although randomly selecting CHs in each round can alleviate the energy hole problem, it will increase the energy consumption of the entire WSN, thereby shortening the lifetime. Figure 4: Convergence behaviour of the proposed algorithm. Figure 7 shows that the data satisfaction per unit energy consumption of different algorithms. The greater the acquisition time, the higher the data satisfaction, but the corresponding energy consumption is also greater. Therefore, the data satisfaction per unit energy is calculated to represent the comprehensive performance of the algorithm, that is, to improve the data acquisition satisfaction as much as possible when the energy consumption is the same. It can be seen from the Figure 6 that the algorithm proposed in this paper can not only reduce the energy consumption of the WSN but also increases the satisfaction of data acquisition. When formulating the optimization problem, we aim to maximize the satisfaction of data collection and minimize the energy consumption of the whole network. Less transmission energy consumption contributes to greater data  
Figure 5: Residual energy of all nodes versus number of rounds. Figure 6: Number of nodes alive versus number of rounds. acquisition satisfaction in the acquisition stage. Therefore, similar to the trend of energy consumption, Relax-CVX obtained the highest data satisfaction, followed by the ATSAO. Figure 8 shows the time consumed by different algorithms for each round, which reflects the complexity of the algorithms. It can be seen from Figure 8 that Reak-CVX consumes the most time per round, and the time consumed by the ATSAO in this paper is similar to that consumed by GREEDY. Compared with Relax-CVX, the time complexity of them is significantly reduced. Therefore, the ATSAO reduces the running time on the basis of ensuring the performance, which is of great significance for sensor nodes with limited energy. Figure 8: The time spent running each round. Figure 7: Data satisfaction per unit energy consumption.