esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 4 Conclusions  
The dynamic changes in volatile flavor compounds and free amino acids (FAAs) in fish sauce during cooking were evaluated in the present study. The diversity and abundance of volatile compounds in all cooked fish sauce samples (B10, B30, B60, F10, F30, and F60) were affected. High temperatures during fish sauce cooking result in the consumption, production, and release of volatile components, which enhance the intensity and complexity of the aroma. Ten compounds were identified as key flavor compounds. Cooking temperature, rather than cooking time, has a greater impact on FAAs and key flavor compounds. The optimal thermal processing condition for fish sauce was found to be boiling for 30 min, based on the results for volatile flavor compounds and FAAs. Carefully selecting the cooking time and method is essential for maintaining the quality of fish sauce. Stir-frying should generally be avoided, and cooking time should be carefully optimized. These findings offer theoretical guidance for developing cooking and processing conditions for dishes and food products containing fish sauce. However, this study has certain limitations. Different raw materials should be combined with fish sauce during heating to assess the applicability of the current findings in food production and culinary applications.  
Methodology, J.-X.X. and X.-B.X.; Data curation, J.-X.X., X.Z., X.-Y.Q. and B.-F.F.; Writing--original draft, J.-X.X.; Supervision, J.-X.X., X.-B.X., S.-Z.C. and M.D.; Project administration, J.-X.X.; Conceptualization, M.D. and S.-Z.C.; Writing--review, and editing, Z.-Y.W., X.-B.X., S.-Z.C. and M.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by The National Key R&D Program of China (2021 YFD2100105).  
This study follows the Declaration of Helsinki and meets the ethical exemption requirements specified in China's Ethical-Review Measures for Life Sciences and Medical-Research Involving Humans, making it eligible for exemption from ethical review.  
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement:The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
Figure 5: Pearson correlation analysis between key volatile compounds and free amino acids during boiling (**A**) and stir-frying (**B**). Symbol “*” represents \(p<0.05\).
## 3 Results and Discussions
### Energy Dissipation  
Figure 9 describes the change curve of the cumulative hysteretic energy dissipation \(E_{\text{hyst}}\) of the model with loading displacement \(\Delta\) under horizontal lateral loading mode. As can be seen from Figure 9, when the horizontal displacement \(\Delta\leq 12\) mm, the TestA-2 is still in the elastic stage. The energy dissipation of each model is at a low level and the curve grows relatively modestly. With the increase in displacement amplitude, the concrete and reinforcement of TestA-2 and TestA-6 gradually enter the plastic working state and the energy dissipation curve shows a stable growth trend. The cumulative energy dissipation of TestA-6 is generally greater than that of TestA-2. The reason for this may be that in the multi-layer shell element model, five lateral horizontal loadings cause the distortion and extrusion of the shell element and more energy loss. The least energy dissipation exists when all nodes are loaded, which is due to the increase in loading points. The force on each loading point is small, but the resultant force of all loading points is approximately equal to that of the three or five loading points.  
The cumulative energy dissipation of TestA-8 is 12.74% of that of TestA-2. It can be seen from Figure 9 that for the cumulative energy dissipation at Node8, TestA-6 possesses the maximum value, whereas the value of TestA-2 is the minimum. Nevertheless, the cumulative energy dissipations of the two models at Node18 are basically equal. The bearing capacities of TestA-7 and TestA-8 decrease rapidly after the peak load and as a result, the energy dissipations are inadequate. Due to the poor deformation capacity, the total value is much lower than that of TestA-2, but under the same horizontal load level, their energy dissipation capacity is slightly greater than that of TestA-2. Compared with Figure 9,c, the energy dissipation capacity of Nodes of TestA-6 is 2.52 times that of Node18. In general, the local energy dissipation law of the models is consistent with those of the global one.
## 4 Results
When the computational times for conducting CFD simulations and constructing the surrogate model are compared (approximately 4 h vs. less than a minute when the computational times are measured using a 32 GB RAM and 16-core computer), applying ANN is a highly efficient way of solving the BWB shape optimization problem. Figure 15 demonstrates the pressure on the surfaces of the baseline and optimal models. For each figure, the left half of the plot presents the pressure on the upper surface, while the right half shows this on the lower surface. Among the baseline models, Baseline model 3 shows the widest high-pressure region on the rear of the lower surface compared with the others, which is the one factor that increases the lift at Baseline model 3. When the pressure distribution of the optimal model is analyzed, the aerodynamic advantage of Baseline model 3 is also investigated for the optimal model. Although the configuration of the optimal model is the most like that of Baseline model 2, the optimal model has a wider high pressure area, especially in the lower surface, compared to Baseline model 2's case. Rather, the optimal model's pressure distribution is similar to that of Baseline model 3, which  
Figure 14: Flow chart of the iterative optimization process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization Iteration** & \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & (_L/D_)\({ANN}\) & (_L/D_)\({}_{CFD}\) & **Error (\%)** \\ \hline
1 & \(-0.2499\) & 0.7907 & 6.7128 & 6.6567 & 0.8432 \\
2 & \(-0.2499\) & 0.8047 & 6.7110 & 6.6816 & 0.4386 \\
3 & \(-0.2498\) & 0.7853 & 6.6935 & 6.6812 & 0.1847 \\
4 & \(-0.2500\) & 0.7792 & 6.6784 & 6.6958 & 0.2600 \\
5 & \(-0.2499 shows the highest lift-to-drag ratio among the baseline models. This phenomenon indicates that the flattening-based morphing method generates the optimal shape by finding the weights that can retain the beneficial aerodynamic characteristic of the baseline models. To identify this feature more specifically, when the pressure coefficients of Baseline model 2 and the optimal model are compared at the symmetry plane, most of the lift increment at the optimal model is caused by the high pressure on the rear of the lower surface, which is the same phenomenon detected in Baseline model 3 (see Figure 16).
## 2 Materials and Methods
### Algorithm Validation  
The algorithm was tested with the 12-behavior validation set described above. In the trials where the number of strokes were incorrectly identified by our algorithm, we estimated the propagated error in 37 features typically used in studies to characterize fine motor control [1; 2; 6; 8; 25; 26]. These features, listed in Table 1, were categorized as temporal, kinematic, or spatial and were quantified using descriptive statistics (average, standard deviation, maximum, and minimum). The algorithm was further tested on the clinical dataset to confirm that the number of strokes identified by the algorithm was equal to the number of pen lifts observed during the recorded trial.  
For the temporal features, in-air time was the time that the pen was lifted from the screen between brushstrokes while the on-screen time was the time that elapsed between the first and final coordinates of the stroke, which included both pauses and movement time [8]. Subsequently, the in-air time/on-surface ratio was the total in-air time divided by the calculated total on-surface time [1]. Stop time and movement time were defined,  
Figure 2: (**a**) A photo of the original tracing on the tablet. (**b**) Raw data prior to preprocessing. Points circled in red are examples of noisy points. (**c**) Detected brushstrokes after applying the segmentation algorithm. Each stroke is represented by a different color and red points indicate identified spurious points. (**d**) The final reconstructed image after removing noisy points.  
respectively, as the durations over which the pen remained in contact with the screen while stationary and in motion [6].  
Spatial features such as the brushstroke height and width were defined as the length of the strokes along the x- and y-axes, respectively, while the stroke length was the total Euclidean distance of the brushstroke [6; 15]. The distance between brushstrokes was the Euclidean distance between the end point of one stroke and the starting location of the next [2].  
The kinematic features such as the velocity and acceleration were computed as instantaneous values between x- and y-coordinates across all brushstrokes [1]. Fluency was defined as the number of inversions in the vector sum of velocity or, equivalently, the number of zero-crossings in the corresponding acceleration signal [26].  
When the algorithm incorrectly identified the number of brushstrokes in the validation dataset, we computed the absolute propagated errors in the values of the features in Table 1, namely,  
\[Error=\frac{|f_{err}-f_{0}|}{f_{0}}\times 100\% \tag{6}\]  
where \(f_{err}\) is the value of a given feature computed with the erroneously detected number of brushstrokes while \(f_{0}\) is the same feature computed with the actual number of brushstrokes.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 3 Improved Hadamard Matrix  
The measurement matrix is a key technology in compressive sensing processing. Different measurement matrices will affect the actual reconstruction of the target estimate, it is very important to design a compressed sampling matrix. This part mainly focuses on the process of designing such a deterministic measurement matrix \(\Phi\) and explains how this design can reduce redundant data introduced in the array.  
There are many construction methods for the deterministic measurement matrix \(\Phi\), but the premise is that the restricted isometry property (RIP) or irrelevance needs to be satisfied as much as possible, that is, it is assumed that the array received signal conforms to a specific sparsity condition, then the original data can be recovered with a small amount of sampled data accurately or with a high probability.  
The deterministic measurement matrix \(\Phi\) constructed in this paper is a compression zeroing measurement matrix based on sequential partial Hadamard [23].
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 2 Micro-Supercapacitor Configuration  
A conventional EDLC supercapacitor has a symmetric configuration with current collectors at the sides, as well as the electrode substrate and electrolyte, which are separated on both sides by the ionic conductor separator. For applications such as wearable power sources (where flexibility or stretchability may be required) and on-chip micro-electronics, micro-supercapacitors are needed. In particular, thin film supercapacitors have very fast physical and chemical processes during charge storage due to the short ion transport distances [49]. These micro-supercapacitors can be fully charged or discharged within seconds. They have a long cycle lifetime, and they provide ultrahigh areal power density. The configuration of micro-supercapacitors with thin film electrodes can be divided into two main groups: (i) stacking (or sandwich) configuration and (ii) in-plane interdigitated configuration (Figure 3). Still, fibre shaped micro-supercapacitors have also been described in studies, and they could be considered as a third type, based on the device dimension [50].  
The overall performance of the supercapacitor is strongly determined by the structural design of the electrode [51]. Carbon-based supercapacitors are the most common EDLC supercapacitors because of their high thermal and electrochemical stability, low cost, good electrical conductivity, controlled porosity, and appropriate active sites [52]. To achieve a supercapacitor with high capacitance, carbon electrodes need to have a controlled pore structure, a large specific surface area, and adequate surface functional groups [53]. The electrolytes in the supercapacitor are also important. Notably, to achieve a supercapacitor with high operating voltage, the carbon materials with modified surface chemistry need to be combined with electrolytes with good chemical and electrochemical stability for good kinetic stability [54]. Moreover, a high energy supercapacitor also requires appropriate cell design and engineering [55].  
Figure 3: Schematic of the types of configurations of micro-supercapacitors: stacking (**left**) and in-plane (**right**).
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 4 Experiment Results and Discussion
### Case Study  
The sensing terminal subsystem is installed in a research vessel, as shown in Figure 8. The fishing vessel has a total length of 44 m and a width of 8 m, with a maximum speed of 12 knots and a displacement of 700 t. Monitoring cameras are installed mid-mast, facing the vessel's forward direction to provide comprehensive coverage. The power supply equipment is located inside the wheelhouse. The sensing terminal subsystem is positioned 12 m above the sea surface and is mounted on a mast bracket arm. The surveillance camera is fixed to the platform located on the lower side of the mast.  
Figure 9 illustrates the navigation routes of the research vessel on 11 May 2024, between 09:00 and 15:00. The vessel's speed seems to have been 13 knots, and the total distance traveled by the vessel is approximately 142 km.  
Figure 8: Equipment installation in a research vessel.  
Figure 7: The fuzzy control surface of the dangerous score.
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
## 2 Methodology
### Basic Equations and Definitions  
The spectral \(\tau_{\text{AOD}}\) is guided by Angstrom exponent (\(\alpha\)), as given by power-law equation [34].  
\[\tau_{\text{AOD}}=\ \beta\lambda^{-\alpha} \tag{1}\]  
In logarithmic format can be written as:  
\[\ln\ \tau_{\text{AOD}}(\lambda)=\ \ln\ \beta-\alpha\ \ln\ \lambda \tag{2}\]  
This gives the values of Angstrom parameters \(\alpha\) and \(\beta\)  
The Angstrom exponent \(\alpha\) can be further defined from the spectral AOD(\(\lambda\)) as  
\[\alpha=-\frac{\text{d}\ln\tau_{\text{AOD}}(\lambda)}{\text{d}\ \ln\lambda}=- \frac{\ln\left(\frac{\tau_{\text{AOD}}(\text{at}\ \lambda_{2})}{\tau_{\text{AOD}}(\text{at}\ \lambda_{1})}\right)}{\ln\left(\frac{\lambda_{2}}{ \lambda_{1}}\right)} \tag{3}\]  
The deviation of observed data from the linear fit line can be tested by using a second-order polynomial fit, with coefficients of the polynomial fit \(\alpha_{2}\),\(\alpha_{1}\), and \(\alpha_{0}\), as defined in previous studies [17; 18; 19].  
\[\ln\ \tau_{\text{AOD}}(\lambda)=\ \alpha_{2}(\ln\lambda)^{2}+\alpha_{1}(\ln \lambda)+\ \alpha_{0} \tag{4}\]  
We define \(\alpha\), at wavelength (\(\lambda\)) and this can also be found in previous articles [17; 18].  
\[\alpha\), at \(\alpha\) = \(\frac{\text{d}\alpha}{\text{d}\ \ln\lambda}=\ -\frac{\text{d}}{\text{d}\ \ln\lambda}\left[\frac{\text{d}\ \ln\ \tau_{\text{AOD}}(\lambda)}{\text{d}\ \ln\lambda}\ \right]=\ -2\alpha_{2}\) (5)  
The aerosol absorption characteristics can be exhibited by using SSA, aerosol absorption optical depth (AAOD) and AAE, and are related as below [2; 3; 35],  
\[\text{AAOD}(\lambda)=\text{AOD}(\lambda)\ [1-\text{SSA}(\lambda)] \tag{6}\]  
and,  
\[\text{AAOD}=\text{K}\lambda^{-\text{AAE}} \tag{7}\]
# Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization
## 4 Empirical Study
### Threats to Validity  
#### 4.5.1 Test Suite  
There are two threats related to the test suite used in our empirical study. First is the size of the test suite. In our empirical study, we treated all test cases as one single test suite for fault localization purposes as in previous empirical study. This has been referred to as the \"universe\" plan in benchmarks. As mentioned in [PERSON] et al. [13], the performance of a risk evaluation formula \"may\" be dependent on the actual number of test cases used in the test suite. Hence, readers should not over-generalize our results without further research. Second, it is related to the composition of test cases in a test suite. Intuitively speaking, different test cases may have different fault detection capabilities. Hence, the performance of the same risk evaluation formula may be different if two different test suites having the same number of test cases are used. It may be more interesting to investigate the diversity of test cases in a test suite to detect different types of faults in our future work.
## 4 Proposed Framework
### EdgeGuard Framework
#### 4.1.2 Local Model Upload  
In EdgeGuard, we collect model updates from our medical devices, represented as \(\{\Delta w_{1},\Delta w_{2},\ldots,\Delta w_{N}\}\). These updates are added to our blockchain, forming a series of connected blocks \(\{B_{1},B_{2},\ldots,B_{N}\}\). Each block has two parts: a body and a header. The body, shown in Equation (27), contains the model updates and calculation times, as follows:  
\[\text{Body}_{i}=\{(\Delta w_{k}^{l},T_{\text{local}}^{i})|\forall k,l,i\} \tag{27}\]  
Here, \(\Delta w_{k}^{l}\) denotes the update from device \(k\) for training round \(l\), and \(T_{\text{local}}^{i}\) denotes how long device \(i\) takes to compute. The header, given by Equation (28), is like an information tag, as follows:  
\[\text{Header}_{i}=\{P_{\text{prev}},\lambda,\text{PoW}\} \tag{28}\]  
\(P_{\text{prev}}\) links to the previous block, \(\lambda\) denotes how fast we make blocks, and PoW is proof that the block is legit. We also track the block size using Equation (29):  
\[B_{\text{size}}=h+\delta m\cdot N_{D} \tag{29}\]  
This depends on the header size \(h\), the size of each update \(\delta m\), and the number of devices \(N_{D}\). This setup helps us keep our medical AI updates organized and secure, balancing technical precision with practical application in our IoMT network [21].
## 3 Methodology  
### Integrated Mahalanobis Classification System (IMCS)  
The current authors have recently proposed the IMCS algorithm [17], which is an extension of the earlier proposed two-stage MCS approach [16] for improving classification performance. The IMCS approach incorporates the decision-making process directly into the feature selection process, thus avoiding the need for extra user-dependent inputs and enhancing classification performance. The framework of IMCS is presented in Figure 1.  
The procedure of IMCS can be briefly summarized as follows. The original dataset, comprising \(n\) samples with \(p\) variables, is firstly randomly split into training and testing sets. In the training stage, the optimal feature subset can be screened out under the objective to maximize the classification accuracy \(max(\frac{TP+TN}{TP+FP+TN+FN})\) (_TP_: true positive; _TN_: true negative; _FP_: false positive; _FN_: false negative) in MD space in the training dataset using the binary particle swarm optimizer (BPSO) algorithm [23; 24]. A PSO-based classifier is introduced to accomplish classification decision threshold optimization, updating itself simultaneously with the BPSO-based feature selection process at each iteration. In the case of multidimensional MD space, alternative decision boundary searching algorithms such as support vector machine (SVM) [25; 26], random forest [27; 28], and neural network (NN) [29; 30] can be easily considered in the IMCS algorithm. Finally, the optimal features and classification threshold extracted from the training dataset are used to make predictions on the testing dataset. In order to avoid overfitting for sparse training datasets, the cross-validation of k-folders is considered. The downside of this fully integrated MCS approach is the higher computational effort needed compared to two-stage MCS [16].  
Figure 1: The schematic of the integrated Mahalanobis classification system (IMCS) algorithm.
## 2 Materials and Methods  
### Characteristics of Contaminated Soil  
The soil used for the pot experiment was taken from an abandoned arsenic slag smelter in southern China, located in Zhongshan County, Guangxi Province (24.46\({}^{\circ}\) N, 111.28\({}^{\circ}\) E, 173 m a.s.l) (Figure 1). The soil of the sampling site was contaminated with heavy metals but host to lots of wild plants belonging to different families. A list of wild plants along with their families and the uses of those plants is provided in Table 1.  
Figure 1: Location of study site and sampling spot—Zhongshan County, Guangxi Zhuang Autonomous Region, China. Map created using ArcGIS Pro 3.0.0 software ([[https://www.esri.com/zh-cn/arcgis/products/arcgis-pro/overview](https://www.esri.com/zh-cn/arcgis/products/arcgis-pro/overview)]([https://www.esri.com/zh-cn/arcgis/products/arcgis-pro/overview](https://www.esri.com/zh-cn/arcgis/products/arcgis-pro/overview)) and accessed date at 20 March 2023).  
The sampling method of soil samples was as described by [PERSON] [18]. A total of 30 soil samples were taken from 0-20 cm of surface soil using a stainless steel shovel. These samples were mixed well and placed in a clean sample bag. Before use, the soil was air-dried and passed through a 2 mm sieve. The contents of soil pH, organic matter (SOM), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and cation exchange capacity (CEC) were studied. The results in Table 2 showed that the average pH in the study area was 6.25, and the soil was weakly acidic. The contents of AN and AP are rich, but the contents of SOM, AK and CEC are low, reaching the levels of I, I, IV, IV and IV, respectively, in the nutrient classification standard of the second national soil survey [19]. The buffered DTPA solution was used to extract the bioavailable heavy metals, and inductively coupled plasma mass spectrometry was used to determine the content of heavy metals. In order to assess the degree of heavy metal contamination in soil, the Nemerow method, an important pollution indicator method, was used. The Pi values of various heavy metal elements were calculated according to the Nemerow method, and then the soil pollution degree was graded according to the Pi value [20]. The results are shown in Table 3. After calculation and comparison, it was found that the pollution of Cd and As in soil was relatively serious.