esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Comparison of SMTs with Mine Tremors and Earthquakes  
The radiation energy of the SMTs was more than 10\({}^{5}\) J, The microseismic monitoring and surface separation detection results show that the source of the SMTs is located in the super-thick strata about 300 m above the coal seam, close to the surface. This is why the SMTs easily caused surface shaking. For example, before the occurrence of the \"8.20\" SMT, it increased from 0.24 m to 0.38 m (see Figure 7b), and before the \"12.20\" SMT, it increased from 0.76 m to 0.86 m (see Figure 7c). In addition, after the SMT occurred, the maximum surface settlement increased again. Different from the SMT, the radiation energy of mining tremors is about 10\({}^{4}\) J, which is triggered by the fracture of the rock layer under the super-thick strata. They do not cause ground shaking, and have little influence on the change of surface subsidence.  
By comparing and analyzing the distribution characteristics of SMPs of SMTs and mining tremors in Table 4, it can be seen that: The seismic moment of the SMT is greater than 10\({}^{9}\) N\(\cdot\)m, the apparent volume is greater than 10\({}^{7}\) m\({}^{3}\), and the stress drop and apparent stress are greater than 10\({}^{4}\) Pa. The four SMPs of the SMT are one to three orders of magnitude higher than those of the mining tremors, indicating that the rock burst strength of the SMT is the highest, the disturbance scale is the largest, and the stress adjustment at the source is the most significant.  
Relevant research shows that the seismic moment of SMTs is generally found in the range of 10\({}^{9}\)-10\({}^{12}\) N\(\cdot\)m, and the stress drop and apparent stress are in the range of 10\({}^{4}\)-10\({}^{6}\) Pa. The seismic moment, stress drop, and apparent stress of SMT calculated in this paper are generally located in the above ranges. The maximum seismic moment of the earthquake is more than 10\({}^{15}\) N\(\cdot\)m, and the stress drop and apparent stress are both greater than 10\({}^{6}\) Pa, which is significantly higher than that of the SMT.
## 3 Applications of the CDS for the Fiber Optic Link
### The Executive
#### 3.3.1 Basic Actions  
These actions include adjusting launch power and data rates in fixed or variable discrete steps. The executive will decide what actions to take based on the received BER sent through the main feedback channel. When there is a disturbance in a fiber optics communication system such as power fluctuation or reach change, and if there is no proper model in the perceptor library that is closest to the perturbed system, then the executive will become aware of it through the BER sent through internal feedback channel (in this case, the BER will be high). The executive will take basic actions such as lowering the data rate so that the BER is below the FEC threshold. When the desired goals are met, the executive would save the corresponding actions in the actions library and will load them for the similar situations in future.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 6 Discussion and Conclusions  
### Discussion  
The above results indicate that the occurrence of unexpected failures is sufficient to cause cascading failure across the whole cyber-physical supply network. The simulation results suggest several implications for managers to better deal with the mentioned failures and improve the robustness of the cyber-physical supply network.  
Firms should improve robustness by establishing proper capacity. The allocated capacity is greater than the needed load to better withstand possible failures in the cyber-physical supply network. The experimental results display that the cascading failure depends on a specific capacity range, this could reduce the influence of node failure. Managers should properly allocate the capacity to resist failure and reduce the probability of failure; however, firm capacity is limited by the cost. Additionally, as the load increases, the strengthened capacity may gradually lose its effectiveness in mitigating cascading failures. Therefore, assigning a proper capacity in the cyber-physical supply network can not only assist in reducing the size of cascading failure, but also reduce the cost of maintaining the capacity.  
The impact of cascading failures on the cyber-physical supply network should be comprehensively measured. Unlike the cascading failures in most physical supply networks resulting from overloading, this paper identified two kinds of cascading failure processes: underload cascading failure in the physical supply network and overload cascading failure in the cyber supply network. The proposed model can help managers to better understand the dynamic behaviors of cyber-physical supply networks during cascading failures. Therefore, to obtain relatively objective evaluation results, managers may need to measure the impact of cascading failures from different processes in the cyber-physical supply network.  
This study is not without limitations. First, our physical supply network model was constructed from Mergent Horizon. The data were verified to be accurate and enable us to build China's electric vehicle supply network. However, this dataset may not capture all the firms and relationships in the network. Second, our model concentrates on firms' short-term reactions to a failure, as we delete firms from the cyber-physical supply network and do not consider if and when the firms will recover to regular operation. Lastly, we did not consider the adaptive strategies of firms confronted by cascading failures. In spite of these limitations, our study is very useful because this study uncovers the cascade window for cyber-physical supply networks, and the parameter space can determine the occurrence and scale of the cascading failure.
## 4 Discussion
### Study Limitations and Areas for Improvement  
The study also presents several limitations regarding sample diversity, methodology, and scope, which should be acknowledged. First, the division of participants into machining and assembly work groups, while informative, may have limited the analysis of individual variability in movement asymmetry and well-being within each group, potentially overlooking subtle intra-group differences. Second, using cross-sectional data restricts the ability to make causal inferences regarding the impact of asymmetrical movements on long-term health outcomes, particularly musculoskeletal disorders. The reliance on inertial motion capture technology, though effective for movement tracking, may also not fully capture the complex postural shifts or micro-adjustments made by workers during tasks, thereby potentially underestimating certain ergonomic risks. Lastly, while the Motion Activity Index (MAI) offers a useful quantitative assessment of movement dynamics, it may not fully encompass other influential ergonomic factors, such as psychosocial stress or task variation, which could also contribute to well-being outcomes. Future studies would benefit from a longitudinal design, a broader range of ergonomic variables, and a more granular analysis of individual worker profiles to improve the generalisability and comprehensiveness of these findings.
## 3 Results and Discussion
### MI Runoff Simulation and HEC-HMS  
Figure 5 compares the runoff simulations using the ELM, SVR, LSTM, and HEC_HMS models. The line graph shows the runoff time series of each model during the calibration and validation periods (from January 2002 to October 2007). The blue vertical dashed line divides the time series into calibration and validation periods. Two parallel black dashed lines, including a blue circle area in the validation period (March 2007), indicate a better investigation of the model's performance. Scatter plots show the ability of the models to simulate river runoff during the validation period (Figure 6). The statistical measures revealed that the performance of the ML model was better than that of the HEC-HMS model. In addition, between the ML models, the LSTM model outperformed the SVR and ELM models (Table 3).  
The HEC-HMS results in Figures 5 and 6 show that, although the predicted peak flow discharge was still acceptable, the model could not fully capture the runoff patterns. Figure 6 shows that the HEC-HMS model mostly underestimated the river runoff values. This phenomenon can be clearly seen in the zoomed area of March 2007 in the line graph (Figure 5). The simulated values from the HEC-HMS were mainly below the observed values. Table 3 shows the model prediction accuracy during the calibration and validation stages. The RMSE, R, and NSE were 25.55, 0.82, and 0.62, respectively, in the validation period. According to the obtained scatter plots, all the applied ML models (ELM, SVR, and LSTM) were superior to the HEC-HMS model. A correlation coefficient (R) of 0.82 was achieved for the HEC-HMS model in validation while R values of at least 0.9 were observed for all the ML models.  
Based on the results listed in Table 3, the LSTM model outperformed the SVR and ELM model among the ML models in both the calibration and validation periods. Therefore, the accuracy ranking was LSTM > SVR > ELM > HEC-HMS for this selected basin on a daily scale. According to the scatter plots, the simulated values of LSTM when compared to those of SVR, ELM, and HEC-HMS revealed that this model was able to estimate the peak values precisely. The findings revealed that the LSTM model had a better generalization performance than the SVR and ELM models. It could be because it can figure out long-term connections between data series and illustrate a reliable flood predictionachievement. Using the structural risk minimization procedure, the SVR model exhibited a better generalization performance than the ELM model.  
Figure 5: Comparison results between observed and simulated daily streamflow during the calibration and validation periods for ML models. The blue vertical dashed line divides the time series into calibration and validation periods. A zoom-in of the blue circle area investigates the model’s performance in March 2007.  
Figure 6: The scatter plots indicate the accuracy evaluation in the validation period: (**a**) LSTM model; (**b**) SVR model; (**c**) ELM model; (**d**) HEC-HMS model.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## Image Rectification Using a Perspective Transformation Model
### Perspective Transformation Matrix's Solution and Image Correction
As shown in Figure 3, the rectangular structure of the building is measured with a red right angle triangle: (a) is photographed by a down-looking camera; (b) is captured by a back-looking camera with obvious deformation, and the rectangle becomes trapezoidal (an approximate parallelogram); (**c**) is obtained by rectification from (**b**) using Equation (10), and it can be seen that the trapezoid becomes an approximate rectangle, which is similar to (**a**). In the following, line matching will be conducted on the rectified images.
## 2 Results and Discussion
### Biological Activities of the Selected Compounds  
#### 2.2.1 Antimicrobial Evaluation  
Using the agar well diffusion method, the in vitro antimicrobial activity of the newly prepared derivatives was investigated against _Staphylococcus aureus_ and _Bacillus subtilis_ (Gram-positive bacteria), _Escherichia coli_ and _Proteus vulgaris_ (Gram-negative bacteria) as well as _Aspergillus fumigatus_ and _Candida albicans_ (fungal strains). The results of antimicrobial activity displayed variable in vitro antibacterial and antifungal activities (Table 1). However, compounds **10**, **10** and **13**d have no antimicrobial activities (Table 1).  
In addition, to identify the minimum inhibitory concentration (MIC), the antimicrobial activities of the most active compounds were tested (Table 2).  
The antifungal activity against the filamentous fungus Aspergillus fumigatus was in the following order: **7**, **6**, **10b**, **20**, **13c** and **13a**, respectively.  
On the other hand, the activity order against Candida albicans \"pathogenic yeast\" was **10b**, **20**, **7**, **13c**, **13a**, **6** and **16**, respectively (Tables 1 and 2). Thus, compared to the ketoconazole reference antifungal drug, these compounds showed lower activity against Aspergillus fumigatus and Candida albicans.  
Compound **13c** showed the highest activity against _Staphylococcus aureus_ (but showing activity lower than the standard drug ciprofloxacin) followed by compounds **13a**, **6**, **16**, **7**, **10b** and **20**, respectively. Likewise, compound **13c** exhibited the highest activity against _Bacillus subtilis_ followed by compounds **7**, **10b**, **21**, **13a**, **6**, **16**, **10e** and **10d**, respectively. Additionally, compound **7** showed the highest activity against _Proteus vulgaris_ than the standard drug, followed by compounds **6**, **16**, **13c**, **10b**, **13a**, **10e** and **20**, respectively. The activity order against _Escherichia coli_ was **13c**, **10e**, **7**, **16**, **20**, **6**, **10b** and **13a**, respectively, and showed lower sensitivity than the tested standard drugs (Tables 1 and 2).
## 2 Materials and Experimental Studies  
In the fabrication of composite tubes, an epoxy resin known as \"Sika CR 80\" alongside a hardening agent, Sika CH 80-2, was employed, both of which were supplied by Dost Kimya Company, Istanbul, Turkey. This was complemented by the use of carbon fiber woven fabrics possessing an area density of 200 g/m\({}^{2}\). For the creation of metal tubes within the study, Al 6063-T4 alloys were selected due to their specific material properties. Additionally, EPP foam, characterized by a density of 30 kg/m\({}^{3}\) and provided by Atermit Company, Sakarya, Turkey, was utilized in the construction process. The experimental setup involved the use of three distinct tube diameters to facilitate nested-tube combinations. These diameters were categorized as large (77 mm), medium (60 mm), and small (37 mm), respectively. The composite tubes themselves were meticulously crafted utilizing the hand lay-up technique. This method was performed on mandrels, employing brushes and rollers to ensure the uniform application and distribution of the resin and hardener components across the carbon fiber fabrics.  
The fabrication of large-diameter tubes involved a process wherein the tubes were constructed by winding them around a two-piece polymer mold featuring a bolt-nut connection mechanism. Conversely, the production of medium- and small-diameter tubes was achieved by winding fibers around cylindrical cardboard tubes. To ensure that no adhesion occurred between the resin and the cardboard tubes, the latter were meticulously covered with tape. Following the initial construction phase, the composites were kept at room temperature for a duration of 24 h, subsequent to which a post-curing process was conducted in an oven maintained at a temperature of 60 \({}^{\circ}\)C for 4 h. For the purpose of performing lateral crushing tests, the tubes were uniformly trimmed to a height 50 mm. A wetting process was employed to facilitate the separation of the cardboard from the composites, thus allowing for the isolation of medium- and small-diameter composites from their cardboard encasements. The adhesion of the tubes to one another was achieved through the application of an epoxy-based two-component adhesive, specifically Araldite 2011. In order to construct the nested-tube structures, these were installed within foam blocks measuring 130 mm by 115 mm. The configurations of these nested structures were varied, encompassing both double- and triple-tube combinations along with their foamed variants, as illustrated in Figure 1. To elucidate the crushing behavior of these nested structures, compression tests were conducted utilizing an MTS 809 (Istanbul, Turkey) brand universal testing device, with the tests being performed at a velocity of 6 mm/min.
## 3 Results and Discussion
(**d–f**) Contact angles of water on a (**d**) native copper foil, (**e**) an h-BN monolayer on copper foils without ECP, and (**f**) that with ECP, respectively. water. Recently, the gas-barrier performance of a wafer-scale single-crystal h-BN monolayer on PET was reported, and was 0.60 g m\({}^{-2}\) day\({}^{-1}\)[36]. A single-crystal h-BN monolayer is ideal for the gas barrier application since it can minimize the diffusion of gas molecules through the grain boundary. However, the synthesis of a single-crystalline h-BN monolayer is very expensive and not generic yet. Thus, our approach to use poly-crystalline h-BN monolayers composed of large grains as gas barriers is more realistic since they are more economical and can be readily mass produced. The water permeability of samples can be obtained from Equation (1) [37]  
\[P^{\circ}=P\left(\frac{4d^{2}}{\pi Dt}\right)^{0.5}\sum_{n=0}^{\infty}exp\left[ \frac{-d^{2}}{4 Dt}\big{(}2n+1\big{)}^{2}\right] \tag{1}\]  
Here, J and J\({}_{s}\) are the water vapor molar flux at time t and at steady state, respectively, d is the sample thickness, P is the permeability, D is the diffusivity, S is the solubility, and P\({}^{0}\) = Jd, P = J\({}_{s}\)d = SD. The permeability, diffusivity, and solubility of all samples were determined from the best fitting of [PERSON]'s second law of diffusion in Figure 4b. Table 1 shows the estimated permeability, diffusivity and solubility of bare PET, a h-BN monolayer without ECP on PET, a h-BN monolayer with ECP on PET, and graphene on PET, respectively. The permeabilities were 7.430 \(\times\) 10\({}^{-14}\), 5.36 \(\times\) 10\({}^{-14}\), 5.14 \(\times\) 10\({}^{-14}\), and 4.88 \(\times\) 10\({}^{-14}\) mols\({}^{-1}\) m\({}^{-1}\) atm\({}^{-1}\), respectively. Interestingly, the decreased permeability was not correlated to the diffusivity; the diffusivity of all samples was nearly identical. Meanwhile, the solubilities of the h-BN monolayer without ECP on PET, the h-BN monolayer with ECP on PET, and graphene on PET were decreased by 25.4%, 26.4%, and 27.0%, respectively, compared to the solubility of bare PET. These results indicate that when adopting 2D material as a gas barrier, the reduction of permeability is caused by reduced solubility rather than the diffusion path blocking effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Bare PET** & **h-BN Without ECP/PET** & **h-BN with ECP/PET** & **Graphene/PET** \\ \hline Permeability & & & & \\
[\(\times\)10\({}^{-14}\) mol s\({}^{-1}\) m\({}^{-1}\) atm\({}^{-1}\)] & 7.43 & 5.36 & 5.14 & 4.88 \\ \hline Diffusivity & & & & \\
[\(\times\)10\({}^{-14}\) m\({}^{2}\) s\({}^{-1}\)] & 0.30 & 0.28 & 0.28 & 0.27 \\ \hline Solubility & & & & \\
[mol m\({}^{-3}\) atm\({}^{-1}\)] & 24.8 & 18.5 & 18.2 & 18.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated permeability, diffusivity and solubility of bare PET, h-BN monolayer without ECP on PET, h-BN monolayer with ECP on PET, and graphene on PET. Figure 4: (**a**) WVTR and (**b**) water vapor permeability of pure PET, an h-BN monolayer without ECP on PET, an h-BN monolayer with ECP on PET, graphene on PET.
## 4 Discussion
(2024) [64], compared ML algorithms to estimate water stress in maize crops, obtaining that CCCI was an important predictor variable. Finally, the Normalized Difference Vegetation Index (NDVI), while more general in application, supports overall vegetation classification and health monitoring. By combining these indices, a more accurate understanding of _Polylepis_ conditions is achievable, aiding in reliable classification by isolating forest areas with specific moisture and health characteristics. In RFE, we selected the iteration with the highest values of Accuracy and Kappa as the optimal number of variables, even if the differences from other variable sets were not statistically significant. This approach was chosen to maximize the inclusion of variables, allowing for a broader analysis of their relevance in identifying _Polylepis_ stands. By prioritizing the highest value, we aimed to capture a comprehensive range of contributing variables, providing a more robust understanding of their relative importance in the classification model. This method enabled a more thorough interpretation of each variable's role, ultimately supporting a refined and potentially more effective identification of _Polylepis_ ecosystems. The ability to detect smaller _Polylepis_ stands was constrained by the spatial resolution of S2, which impacts the final result. While Sentinel-2 provides valuable data for large-scale vegetation analysis due to its high spatial resolution and multispectral bands, its 10 to 20 m resolution poses challenges when it comes to identifying and accurately mapping smaller or fragmented _Polylepis_ patches. These limitations can lead to an underestimation of the distribution and extent of these smaller stands. Future endeavors will prioritize ongoing expert data validation to augment the dataset and acquire additional samples for broader coverage of the study area. Furthermore, there will be an exploration of advanced techniques like Neural Networks applied to high spatial resolution imagery such as PS, aiming to enhance classification reliability in remote sensing images.
## 2 Food Safety Management Systems around the World
### Managerial Systematic Approach to Food Safety
#### 2.1.6 Rewording the Guidelines Related to Microbiological Standards  
Academics and researchers generally concur that food quality can be viewed from two perspectives: objective and subjective. The objective aspect pertains to the physical characteristics of the product and involves quality control and food technology. On the other hand, the subjective aspect pertains to consumer's assessments and evaluations of the perceived quality characteristics of the product. Although food quality is a multifaceted and intricate concept, it is intrinsically linked to food safety. Food safety is the most crucial factor in ensuring that consumers purchase products that meet their safety expectations, and it is therefore subject to regulation. To ensure the quality and safety of food products, it is crucial to have monitoring systems in place at critical stages of the productions process. The use of accurate and quick analysis methods is essential to ensure that the product meets quality and safety standards and complies with labeling requirements. The prompt detection of spoilage agents, including bacteria, pathogens, and other microbial contaminants during food production and processing, is essential to minimize spoilage and ensure a safe food supply.  
If the test results fail to meet the required criteria, food producers must follow the measures outlined by regulations. When repeated testing results are consistently satisfactory, food production companies must take immediate action to prevent the occurrence of microbial hazards. Companies must ensure that their products meet the specific criteria set for each type of food, without exception, depending on their characteristics [6].  
As an example, _Cronobacter_ spp., an opportunistic bacterium, can be found in various food types. While powdered infant formula (PIF) is the primary source of _Cronobacter_-related illnesses [22; 23], the bacteria have also been detected in other foods such as fruits, vegetables, cereals, spices, starches, dry foods, and meat products. Several studies have investigated the prevalence and molecular characteristics of _Cronobacter_ in different food sources, including studies conducted in Korea on poultry and vegetable products (published in 2019) and on powdered infant formula (published in 2012), a study in China on spices (published in 2018), and a study in England and Wales on clinical cases of _Cronobacter sakazkii_ in infants (published in 2016). These studies contribute to our understanding of the occurrence and potential risks associated with _Cronobacter_ in various food items [56; 57; 58; 59; 60; 61].  
Table 1 captures the main microorganisms of concern, toxins, and metabolites found in specific food categories [6].