esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In order to strengthen the impingement cooling heat transfer in the end wall cavity of the gas turbine guide vane, its structure and parameters have constantly been improved. This study investigates the effects of the impingement hole diameter, the impingement hole number, and the height, spacing and shape of pin fins on the impingement cooling. The key findings of this research are as follows.  
1. The larger the \(Re\), the larger the average Nusselt number of the upper and lower surfaces, and the average Nusselt number of the lower surface is larger than that of the upper surface.
2. With the increase in the impingement hole diameter, the average Nusselt numbers of the upper and lower surfaces are both increased, and the minimum \(Nu\) of the lower surface is reduced, where may produce local ablation. The higher the number of impingement holes, the larger the average Nusselt number of the lower surface, the more uniform the \(Nu\) distribution. It is recommended to use 14 impingement holes with a hole diameter of 7.2 mm.
3. The geometric parameters of the pin fin have less effect on heat transfer and pressure drop. Compared with the pin fin height is 24 mm, the average Nusselt number of the lower surface only improved by 11.2% when the pin fin height is 3 mm. The average Nusselt number of the lower surface with the circular pin fin is larger than that of the elliptical pin fin, and its flow resistance is smaller. It is recommended to use the circular pin fin with a height of 3 mm and spacing of 25.8 mm.
4. The jet flow in cavity is closely related to the heat transfer. The heat transfer near the impingement holes is better, while the heat transfer in the vortex area is poor. Due to the turbulence effect of the pin fins, the air flow turbulence intensity increases rapidly, and heat transfer is enhanced.  
Figure 24: Entropy generation distribution with pin fins on the lower surface at \(Re=2\times 10^{4}\).  
**Author Contributions:** Conceptualization, L.Z.; methodology, L.Z. and G.C.; software, G.C. and K.F.; validation, L.Z., G.C. and K.F.; formal analysis, G.C. and K.F.; investigation, G.C., K.F. and Y.J.; data curation, Y.J. and Z.Z.; writing--original draft preparation, G.C. and L.Z.; writing--review and editing, L.Z. and G.C.; visualization, Z.Z.; supervision, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 3 Technical Methods
### Optimization Solution  
#### 3.3.1 Scene Lightweighting  
Rendering 3D scenes requires abundant computational resources (e.g., memory and CPU usage). However, compared with stand-alone programs, web programs can only access limited computational resources; thus, lightweight 3D scenes are necessary.  
In this study, a virtual evacuation drill program was developed based on a popular graphics engine, Unity [45], and then released to the WebGL version for online applications. The Unity engine defines \"static\" objects, which do not move during the entire process of scene rendering. These static objects can be pre-processed to achieve a high rendering efficiency.  
Lightmap baking, which is most commonly applied to static objects, was used to pre-process the static objects in this study. A lightmap is a data structure used in lightmapping, a form of surface caching in which the brightness of surfaces in a virtual scene is pre-calculated and stored in texture maps for later use. By using a lightmap solution, lighting effects such as global illumination can be provided at a relatively low computational cost.  
The indoor objects (e.g., doors and walls) in the virtual evacuation drill are typical static objects. First, the properties of these indoor objects were defined as static in Unity. Then, the lightmaps of these static objects were baked in advance for rendering efficiency in the virtual drill.
## 4 Angle Selection and Ray Categorization
### Node Orientation
Furthermore, reflected ray from obstacles will not be able to fulfill all three constraints satisfied by the surface reflection, i.e., having a depth \(z_{T}\approx z_{p}\), experiencing a phase shift of \(\pi\), and having a direction close to one found by using the natural forces of buoyancy and gravity. Finally, if neither bottom nor surface reflections are observed, nodes rely on the already-found axis using the natural forces of buoyancy and gravity. In such a case, errors will be introduced in identifying the location of a LEP as discussed in Section 4.8. Figure 5: Conceptual 2D representation of the layered model with refraction on the edge of each layer. Only rays orthogonal to both underwater layers and sea surface/bottom as well as nearby obstacles will reflect to the node. The final refinement of the vertical direction is done by exploiting the capability of the transducer to steer a beam, where the underlying assumption is that the steering angle is smaller than \(\psi/2\). Since \(n_{a}\) knows that the \(z\)-axis lies within the width of the selected beam, it steers the chosen beam towards and away from an arbitrary selected \(x\)-axis that is perpendicular to \(z_{i}\) until the reflected version of the transmitted beam is no longer received. The node then notes both final angles from which a reflection was received while steering away and towards the \(x\)-axis. Then, the node concludes the direction of the \(z\)-axis, also referred as the vertical axis, as the angle bisecting the range of angles it has noted. An example is given in Figure 6 where \(beam\) 1 represents the beam containing the surface reflected ray and thus selects \(z_{i}\) as its initial candidate to the positive \(z\)-axis. Node \(n_{a}\) then transmits \(beam\) 2 and \(beam\) 3 that have a deviation less than \(\psi/2\) from \(z_{i}\) and waits for their reflection. Since \(beam\) 3 does not contain the surface reflected ray, its reflection will never reach \(n_{a}\) and will be timed out. Then, bisecting the angle formed by the furthest edges of \(beam\) 1 and \(beam\) 2, \(n_{a}\) will conclude that the final direction to the surface is \(z_{f}\). Finally, \(n_{a}\) selects an \(x\)-axis and \(y\)-axis randomly that are perpendicular to each other and to \(z_{f}\). Once the positive \(z\)-direction is finalized, the node searches for another reflection in the candidate list that has an angle deviation of \(180\pm\frac{\psi}{2}\) signifying a bottom reflection. If the entry is found, the node confirms its positive \(z\)-axis and forks a negative axis that is \(180^{\circ}\) from the finalized positive one. Then the node utilizes the bottom reflected signal in order to determine the phase shift that such a signal has experienced \((\overline{p_{bd}})\) and store this value for future use. On the other hand, if none of the entries in the candidate list match the bottom reflected signal, the node regards the distance to the bottom as infinity.
## 1 Introduction
In addition, gray relational analysis (GRA) [17], a normalization evaluation technique, is extended to solve the complicated multi-performance optimization, whose theory is based on the random uncertainty of small samples developed into an evaluation technique to solve certain complex problems [18]. In the field of risk management of reliability and security, failure modes and effects analysis (FMEA) has been an important approach to find the risk priority number (RPN) based on four indicators, which comprise the severity of the failure effect (S), the probability of occurrence of the failure mode (O), the probability of failure detection (D), and the expected cost (E) [19]. As noted previously, based on the HFACS model and FMEA approach, the BWM and GRA are the important MCDM methods, which are used by this article to calculate quantitative RPN research for evaluating the risk of picking and material-handling accidents in warehouse facilities. The objectives of this research are as follows:  
* To collect the critical risks of picking and material-handling accidents in the warehouse facility by the relevant literature and interviewees of a case study,
* To classify the critical risks based on the HFACS model of five levels of human negligence,
* To calculate the weights of four critical indicators (O, S, D, and EC) by the BWM approach, and
* To rank the critical risks of picking and material-handling accidents in a warehouse facility through GRA for providing decision-makers with a reference. This research article comprises six sections. Apart from the introduction in section one, the second section reviews the theoretical literature, the third section explains the research methods, the fourth section presents the results, the fifth section is further discussions, and the last section provides conclusions to this research article.
# Design and Implementation of Attitude Stabilization System for Marine Satellite Tracking Antenna
## 2 Introduction of Attitude Stabilization System
### Diagram of the Attitude Stabilization System  
The diagram of the attitude stabilization system of elevation part is shown in Figure 2.  
Figure 2: Diagram of the attitude stabilization system of elevation part.  
In Figure 2, there are two control loops, the inner loop and the outer loop. The inner loop is the velocity loop, whose main function is to counteract the velocity disturbance from ships. Most of the disturbance is counteracted by the inner loop and the main focus is put on the design of the velocity controller. The velocity controller is designed based on \(\mathcal{H}_{\infty}\) loop shaping. The disturbance that cannot be counteracted by the inner loop is left to the outer loop. The outer loop is the position loop and the position controller is a simple PI controller, whose design procedure is not stated.
# ODIN IVR-Interactive Solution for Emergency Calls Handling
## 3 Method
### Requirements and Specifications  
The requirements and specifications of an IVR/chatbot solution for emergency systems differ from the requirements of corporate chatbots [31]. On the one hand, critical deadline fitting is the most important feature of such a system. Another aspect that needs to be taken into consideration is 100% call handling: no emergency can be left unsolved, even if the situation is not urgent or it is a fake call. On the other hand, another important aspect of emergency systems is that their resources are limited; therefore, such a system must handle multiple parallel emergency calls with a minimum of computing resources. Furthermore, the system's availability must be considered both in terms of coverage and availability time (24/7). By coverage, we understand the percentage of end users that can use the system. This percentage is strictly related to the network operators' data coverage. Table 1 summarizes the specifications and requirements of the chatbot/IVR solution tailored to emergencies as agreed to by the Romanian institution responsible for tackling emergency calls within the framing of the ODIN 112 project. As can be observed, in terms of functionality, the solution needs to be oriented toward low resource consumption. For instance, the CPU utilization of the system must be less than 70%, and the amount of RAM consumed must be less than 2 GB. The maximum time to treat an emergency is two minutes.
# Optimization of Chinese Chive Juice as a Functional Feed Additive
## 3 Results  
### Antibacterial Stability of CC Juice under Heat Treatment  
The fresh CC juice displayed broad-spectrum antibacterial activities against a wide range of pathogens except _L. garvieae_ and _V. harveyi_, hence, these two strains were excluded from further experiments in this study (Table 1). Considering that CC drying is an essential prerequisite to producing powdered products, the effects of different heat treatment (60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C for 3 h) on the antibacterial activity of CC juice were examined (Table 2). We observed that the antibacterial activities of CC juice were significantly decreased following the increase in heating temperature (\(p<0.01\)) and time (\(p<0.01\)). However, the interactive effects of heating temperature and time did not significantly impact the antibacterial effects of CC against _E. coli_, _P. agglomerans_, _H. sonmus_ and _Burkholderia_ sp., but showed a decreasing trend against _S. iniae_ and _S. gallinarum_. The antibacterial activity of CC juice was partially impaired by the heat treatments, but was still effective after 3 h of heating at 80 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c} \hline
**Pathogens** & **Antibacterial Activity (1)** \\ \hline _Clostridium perfringens_ Type E & ++ \\ _Streptococcus iniae_ & +++ \\ _Lactococcus garvieae_ & - \\ Enterotoxigenic _E. coli_ O157:H7 & ++ \\ _Pantoea agglomerans_ & ++ \\ _Haemophilus parsuis_ & ++ \\ _Haemophilus sommus_ & +++ \\ _Burkholderia_ sp. & +++ \\ _Salmonella gallinarum_ & ++ \\ _Salmonella pullorum_ & +++ \\ _Edwardsiella tarda_ & +++ \\ _Photobacterium damselae_ subsp. _damselae_ & +++ \\ _Vibrio ichthyoenteri_ & ++ \\ _Vibrio harveyi_ & + \\ \hline \end{tabular} \({}^{(1)}\) Antibacterial activity (clear zone diameter): -, Not detected; +, 0–10 mm; ++, 10–20 mm; +++, 20–30 mm.  
\end{table}
Table 1: Antibacterial activity of the fresh Chinese chive (CC) juice against pathogens.
# Experimental Study on Repairing Corroded Cracks by Electrophoretic Deposition
## 1 Introduction
explored the influence of concentration of repair solution, temperature [42], type of epoxy resin [43], and type of anode material [44] on the repair effect and the corresponding influence mechanism. It was pointed out that, when the solution concentration was 0.05-0.2 mol\(\cdot\)L\({}^{-1}\), the higher the solution concentration, the faster the repair effect was. In addition, when the solution temperature was controlled at 20-30 \({}^{\circ}\)C and a graphite plate was selected as anode material, the repair effect was relatively better. Reasonable repair time can not only guarantee the repair effect, but also control the repair cost, but the impact of repair time on carbonation resistance and water resistance of repaired concrete is not clear, and there is also a lack of relevant research on the basic repair principle of electrophoretic deposition method. Therefore, this study mainly adopts accelerated carbonation test and water absorption test to explore the influence of repair time on carbonation resistance and water resistance of repaired concrete, and these were combined with SEM, TG-DSC analysis, and other testing methods to explain the repair principle. The results of this investigation could lay a strong foundation for the application of this method to engineering tasks.
## 3 Positron Emission Tomography & Tracers for the Detection of AD
### Fluorodeoxyglucose (FDG)  
FDG (2-[18]F-fluoro-2-deoxy-D-glucose) is a glucose analog that does not undergo glycolysis and hence accumulates in the brain at the same rate that glucose would be metabolized. FDG has been routinely used for decades to monitor brain glucose metabolism as a reliable proxy of synaptic activity [30]. It is well known that changes in FDG uptake precede the structural atrophy in neurodegenerative disorders that may be visualized by MR or CT imaging [31; 32]. AD patients exhibit progressive and usually bilateral glucose hypometabolism compared to healthy controls. As seen in Figure 1, this occurs specifically within the parietal and temporal cortices, but also in the frontal and posterior cingulate cortices and the precunei. Metabolism is mostly preserved in the primary sensorimotor cortex, basal ganglia, thalamus, brainstem, and primary visual cortex. The degree and size of the area of distribution of hypometabolism also correlate with the estimated severity of AD dementia and decreased scores on clinical tests of cognitive function. MCI patients also show heterogeneous patterns of hypometabolism throughout the brain, in keeping with the fact that MCI patients may eventually develop non-AD dementias, though most MCI patients show hypometabolism in the medial temporal cortex; however, this is also a common finding in elderly healthy controls [20,32]. Hypometabolism within the posterior cingulate cortex, inferior parietal lobe, and precunei is common in prodromal AD patients. Progressive pariototemporal hypometabolism is also found in asymptomatic carriers of the allele _APOE-e4_, a gene that significantly increases the lifetime risk of developing AD [33]. Because glucose metabolism is spared in the posterior cingulate cortex and the precunei in non-AD DSDs, FDG-PET is also useful in their differential diagnosis [34]. FDG-PET has also been used for monitoring outcomes in many AD therapeutics, specifically by tracking changes in regionalized or global tracer uptake [35, 36, 37, 38].
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 6 Results and Discussion
### Support Vector Machine  
To evaluate the performance of the SVM model, it is important to emphasize the significant computational cost and time required to train the classifier. In this case, 325 h were needed to complete the process of running and validating the results of the five tests. Regarding the confusion matrix, it can be noticed at first glance that in both cases of PCA application, undefined values were obtained for the _Precision_ and _F1 Score_ metrics. This was because the SVM model failed to make any error predictions.  
Therefore, although a high percentage of _Accuracy_ was achieved (97.61%), the model was not performing the classification correctly, and the error cost was very high. In the case of the _Precision_ metric, the best results were obtained by not applying any dimensionality reduction technique to the dataset (89.32%), as it could be observed that in the Univariate Feature Selection technique or _SelectKBest()_, efficiency decreased as the number of configured features was reduced (65.25%). On the other hand, the _Recall_ metric showed low values in all tests, indicating that the proportion of errors that were correctly identified was very small.  
With the analysis presented, it can be conclusively determined that the SVM was not sufficiently efficient to meet the objective of detecting and predicting errors. Regarding the K-fold cross-validation method, similar accuracy values were obtained, just as with the RF. We deem it essential to delineate the limitations of the SVM model, as this will substantiate our rationale for eschewing its application in error detection within SGs. The findings elucidate the inherent unsuitability of the SVM model for this particular task, thereby offering critical insights that can inform a more judicious model selection in analogous applications or use cases.
## Materials and Methods
### Identification and Quantitation Analysis  
Aroma compounds detected by GC-MS and GC-O analysis were firstly identified by comparing their mass spectra with NIST 14 Library database (MS) and matching the odor descriptions (O) with standard compounds. The linear retention indices (RIs) for each volatile compound on the TB-WAX and TP-5 columns were calculated and compared to references values. A homologous series of C6\(-\)C30 \(n\)-alkanes were applied to determine the RIs of aroma compounds. The standard reference compounds (STD) were injected into the GC-MS to further verify the odorants.  
Quantitative analysis for important aroma active compounds (with FD \(\geq\) 8) was performed by diluting stock solutions to construct standard curves [28]. A certain amount of douchi sample (1 \(\upmu\)L) was injected in GC-MS, and selective ion mode (SIM) was applied in quadrupole mass spectrometry. Five mixed standard solutions with different concentration ranges of 20 key aroma compounds were prepared, then each stock solution was serially diluted to six different concentrations and spiked with internal standard (1.68 \(\upmu\)L). The calibration equation of each odorant was obtained by plotting the response ration of standard compounds and 2-methyl-3-heptanone against their concentrations. Analysis was repeated in triplicate.
## 4 Discussion
### Future Development Direction of BCG/SCG Technology  
Search of literature of BCG/SCG technology in the past 20 years in the field of medicine is synthesized and presented in Figure 6, in which, 37.16% of the researches were related to cardiovascular health, 9.84% were related to application of sleep monitoring; compared with this, it accounted for 28.96% in health management research, in which, 9.29% were related to the research of sleep health management. The directions of future development of BCG and SCG are the application of sleep health management, cardiovascular monitoring and diagnosis, and respiratory testing.
## 4 Data Processing
### Creating 3D Models from Photogrammetry
**Mesh calculation:** Face count 30,000,000  
This parameter specifies the maximum number of triangles in a polygonal mesh. A higher count provides a more detailed model surface but increases the computational requirements. 5. **Alignment Accuracy**  
Root Mean Square Error (RMSE) is a widely used metric for assessing the accuracy of predictive models. An overview of the RMSE parameter for all three phases is shown in Table 3.  
Table 4 contains all the important parameters of the photogrammetric calculation; the interesting aspect here is the difference in the number of points and the bias between Phases 1 and 2 and Phase 3.
## 4 Smart Contract Vulnerability Detection Tools
### Dynamic Analysis  
By running smart contracts and watching their behavior, dynamic analysis uses smart contracts to find vulnerabilities. It differs from the static analysis approach, which examines the code without executing the contract, and the dynamic analysis method, which tracks the contract's behavior as it is being executed.  
The primary steps for applying dynamic analysis to find smart contract vulnerabilities as follows:  
1. Creating test cases. The dynamic analysis technique necessitates testing the smart contract, so it is necessary to create a set of test cases that cover as many scenarios as feasible. Examples of test cases include boundary cases, anomalous inputs, and normal inputs.
2. Run the test cases. Run the test cases and keep an eye on how the smart contract behaves, either locally or on the test network. You can check the smart contract for state changes, function calls, transactions, and exceptions, while it is being executed using tools or manual techniques.
3. Examine the test outcomes.  
Examine the test findings to look for any weaknesses. If vulnerabilities are discovered, they must be examined and patched, and the test cases must be run again to verify the effectiveness of the fix.  
A dynamic analysis-based security vulnerability analysis method called Contract Fuzzer [5] is used to detect security flaws in Ethernet smart contracts by creating ambiguous test cases, which are based on the smart contract ABI specification. Contract Fuzzer configures the EVM, and its behavior was recorded as the smart contract was being executed. By analyzing the logs, vulnerabilities are found.  
Reguard [6] is a fuzzy test analyzer for smart contracts that dynamically detects reentrant vulnerabilities by fuzzing smart contracts by iteratively generating random and multiple test cases to follow the contract throughout execution.