esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of the Safrole Extraction Method
#### 3.3.2 Optimal Conditions of Temperature and Extraction Time  
The best experimental conditions were obtained at 60 \({}^{\circ}\)C and 10 min of extraction time. The largest chromatographic area by the GC-FID method were obtained with these settings (Figure 3). The response surface regression analysis exhibited linear behavior for both parameters (\(z=1172.89 xy-1356.22x-22662.76y+38058.24\); where \(x\) is the temperature, and \(y\) is the extraction time) (\(R^{2}=0.996\); \(P<0.05\)).  
The SPME technique involves a multiphase equilibrium process. In general, extraction time and temperature are among the most critical factors affecting SPME, thus being the usual variables chosen for investigation and optimization [51]. Equilibrium time in the headspace is related to the extraction temperature, fiber coating thickness, and analyte diffusion coefficients [52]. The analyte is released from the matrix more efficiently when exposed to high temperatures. This phenomenon occurs due to the reaction kinetics, as the molecular kinetic energy of the analyte is directly proportional to the temperature rising [51].  
[PERSON] et al. [39] used HS-SPME-GC/FID with a 100-\(\upmu\)m PDMS fiber to remove volatile monoterepens from _Mentha pipertia_ L. (Lamiaceae) extract. The optimal extraction conditions for these compounds were found at 64 \({}^{\circ}\)C and 28.5 min extraction time. The optimal temperature determined by the authors is close to the temperature found in the present study. However, their exposure time was longer, probably because of the fiber coating they employed (100-\(\upmu\)m PDMS), which delivered a low performance in this research.  
Figure 3: Optimization of safrole extraction from cowpea beans by the headspace solid-phase microextraction method coupled to a gas chromatograph with a flame ionization detector, as a function of temperature and extraction time.  
Results found by [PERSON] et al. [50] corroborate the present investigation. Likewise, they observed that the most satisfactory settings for extracting aromatic compounds from cocoa were 60 \({}^{\circ}\)C and 15 min extraction time, employing a 50/30-\(\upmu\)m DVB/CAR/PDMS StableFlex fiber.
# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 3 Materials and Methods
### Support Vector Machine (SVM)  
For classification and aggregation, we use a supervised ML algorithm known as a support vector machine (SVM). SVM aims to identify the hyperplane in an n-dimensional space that can best separate the instances into different classes.  
An essential aspect of SVM is its capability to manage both linearly separable and non-linearly separable data through a method known as the kernel trick. This technique converts the original input space into a higher-dimensional space, potentially rendering the data points linearly separable. This transformation allows SVM to learn complex decision boundaries effectively within the original feature space.  
In the training phase, SVM aims to minimize a cost function, usually composed of the margin and the classification error. To solve this optimization problem, various methods, such as quadratic programming or convex optimization, are employed, depending on the specific configuration of the SVM algorithm.
###### Abstract  
The detection of archaeological structures in satellite images is beneficial for archaeologists since it allows quick identification of structures across large areas of land. To date, some methods have been proposed to solve this task; however, these methods do not give good results in areas with large amounts of vegetation, such as those found in the southeast of Mexico and Guatema. The method proposed in this paper works on satellite images obtained with SASPlanet. It uses two color spaces (RGB and HSL) and filters (Canny, Sobel, and Laplacian) jointly with supervised machine learning to improve the detection of archaeological structures in areas with a lot of vegetation. The method obtains an average performance of at least 93% on precision, recall, F1 score, and accuracy. Thus, our proposal is a very good option compared with traditional techniques for manual or semi-automatic detection of structures, identifying archaeological sites in a shorter time.  
image processing: computational learning; detection of Mayan structures +
Footnote †: journal: Applied  
0023
## 5 Experimental Validation
### Results and Discussion
#### 5.2.3 Discussion  
The experimental validation demonstrates that the optimized concave contact geometry successfully addresses the fundamental design challenge in ultra-high-pressure hydrogen solenoid valves. The 69.6% reduction in sealing pressure (from 5.6 \(\pm\) 0.7 MPa to 1.7 \(\pm\) 0.2 MPa) and the elimination of permanent deformation under 87.5 MPa conditions confirm the theoretical predictions based on Hertzian contact theory. These results validate the design methodology that identified the optimal Rp/Rs ratio of 1.22 through systematic optimization.  
This research establishes a new theoretical paradigm for extreme pressure sealing systems, fundamentally departing from the hard-to-hard sealing approaches that have dominated valve engineering. The systematic framework developed here transforms empirical design practices into predictive theoretical models, contributing to the fundamental understanding of how material asymmetry can be strategically leveraged for superior contact performance. The validation of non-conforming geometry principles in extreme pressure applications opens new avenues for high-pressure mechanical system design across various engineering disciplines, extending the scientific understanding of contact mechanics beyond traditional boundaries.  
The experimental findings align closely with the finite element analysis predictions, demonstrating that the concave contact geometry fundamentally alters stress distribution patterns compared to conventional rectangular designs. This consistency between theoretical, computational, and experimental results strengthens the validity of the proposed design approach and confirms that Hertzian contact principles can be effectively applied to resolve contradictory performance requirements in ultra-high-pressure applications.  
From a practical perspective, the ability to achieve sealing below the AIS-195 standard requirement of 2.0 MPa represents a significant advancement for hydrogen fuel cell vehicle applications. The improved sealing performance at low pressures, combined with enhanced structural integrity at ultra-high pressures, could contribute to improved safety margins and extended operational lifespans in hydrogen storage and control systems.  
Several limitations should be acknowledged. The experiments were conducted under controlled laboratory conditions at room temperature using nitrogen gas for safety considerations, which may not fully capture the behavior in actual hydrogen environments across the wide temperature ranges encountered in automotive applications. Additionally, the current study focused on immediate performance assessment rather than long-term durability characteristics under repeated pressure cycling. Despite these limitations, the substantial performance improvements demonstrated establish a solid foundation for the development of next-generation ultra-high-pressure hydrogen valve technologies.
## 2 Materials and Methods
### Practice Experience and Training Load  
Data related to players' practice experience (i.e., years of basketball practice) and training load (i.e., hours of practice per week) were recorded through a biosocial questionnaire. The questionnaire results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{_CA Groups_} & \multicolumn{3}{c}{_Effect of CA_} \\ \hline
**Variables** & **13 Years (\(n\) = 34)** & **14 Years (\(n\) = 138)** & \(t\) & \(p\) & \(\eta_{p}^{2}\) \\ \hline Practice experience (years) & \(5.1\pm 2.8\) & \(5.4\pm 2.5\) & \(-0.754\) & \(0.452\) & \(0.003\) \\ \hline Training load (h/week) & \(6.0\pm 1.5\) & \(6.3\pm 1.6\) & \(-1.020\) & \(0.309\) & \(0.006\) \\ \hline CA (years) & \(13.1\pm 0.4\) & \(13.9\pm 0.2\) & \(-11.561\) & \(<\)0.001 & \(0.641\) \\ Maturity offset (years) & \(-0.37\pm 0.78\) & \(0.67\pm 0.67\) & \(-7.829\) & \(<\)0.001 & \(0.265\) \\ APHV (years) & \(13.4\pm 0.6\) & \(13.3\pm 0.6\) & \(1.226\) & \(0.222\) & \(0.009\) \\ \hline MORPHOLOGY & & & & & \\ Body Mass (kg) & \(54.4\pm 11.9\) & \(61.0\pm 9.4\) & \(-3.465\) & \(0.001\) & \(0.066\) \\ Stature (cm) & \(166.9\pm 9.0\) & \(174.1\pm 8.2\) & \(-4.490\) & \(<\)0.001 & \(0.106\) \\ Arm span (cm) & \(170.0\pm 11.7\) & \(177.2\pm 9.3\) & \(-3.832\) & \(<\)0.001 & \(0.080\) \\ Hand span (cm) & \(21.4\pm 1.8\) & \(22.3\pm 1.5\) & \(-3.006\) & \(0.003\) & \(0.050\) \\ BMI (kg/m\({}^{2}\)) & \(19.3\pm 2.8\) & \(20.0\pm 2.1\) & \(-1.584\) & \(0.115\) & \(0.015\) \\ \%FM & \(16.7\pm 6.4\) & \(16.9\pm 5.5\) & \(-0.196\) & \(0.845\) & \(0.000\) \\ Lean mass (kg) & \(44.8\pm 7.5\) & \(50.3\pm 6.3\) & \(-4.403\) & \(<\)0.001 & \(0.102\) \\ \hline FITNESS & & & & & \\ V20-m (s) & \(3.42\pm 0.22\) & \(3.33\pm 0.23\) & \(2.180\) & \(0.031\) & \(0.027\) \\ TT (s) & \(10.6\pm 0.7\) & \(10.4\pm 0.7\) & \(1.363\) & \(0.175\) & \(0.011\) \\ CMJ Height (cm) & \(28.4\pm 4.7\) & \(30.3\pm 5.1\) & \(-2.001\) & \(0.047\) & \(0.023\) \\ CMJ Power (w) & \(640\pm 152\) & \(739\pm 135\) & \(-3.726\) & \(<\)0.001 & \(0.075\) \\ CMJ-S Height (cm) & \(33.8\pm 4.7\) & \(36.1\pm 5.7\) & \(-2.228\) & \(0.027\) & \(0.028\) \\ CMJ-S Power (w) & \(698\pm 163\) & \(806\pm 143\) & \(-3.856\) & \(<\)0.001 & \(0.080\) \\ SUM HG (kgf) & \(64.4\pm 17.4\) & \(72.4\pm 13.8\) & \(-2.497\) & \(0.016\) & \(0.046\) \\ MBT (m) & \(4.49\pm 0.71\) & \(5.09\pm 0.73\) & \(-4.305\) & \(<\)0.001 & \(0.098\) \\ Seat and reach (cm) & \(-1.8\pm 7.6\) & \(-1.1\pm 7.9\) & \(-0.447\) & \(0.655\) & \(0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics (mean and standard deviation) and results of independent samples \(t\)-tests to evaluate the effect of chronological age groups in U14 years basketball players’ characteristics.
_Article_  
**Enhancement of Computational Efficiency in Seeking Liveness-Enforcing Supervisors for Advanced Flexible Manufacturing Systems with Deadlock States**  
**[PERSON] 1,2,*\({}^{\text{\textregistered}}\), [PERSON] 1, [PERSON] 3 and [PERSON] 1**  
1 Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan; [EMAIL_ADDRESS] (C.-W.T.); [EMAIL_ADDRESS] (J.-C.H.)  
2 Mathematics and Physics Division, General Education Center, Air Force Academy, Kaohsiung 82047, Taiwan  
3 Department of Computer Science & Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] or [EMAIL_ADDRESS]; Tel.: +886-7-626-8846  
Received: 25 February 2020; Accepted: 7 April 2020; Published: 10 April 2020  
**Featured Application: In the paper, we further propose two novel concepts called Pre Idle Transitions (PIT) and Pre Idle Places (PIP) for the IMFFP-2. Once PIT or PIP is identified from a deadlock Petri nets model, one can bypass all PIP under the process of solving MFFP, and the computational time can hence be shortened.**  
**Abstract:** In industry 4.0, all kinds of intelligent workstations are designed for use in manufacturing industries. Among them, flexible manufacturing systems (FMSs) use smart robots to achieve their production capacity under the condition of a high degree of resources sharing. As a result, deadlock states usually appear unexpectedly. For solving the damage deadlock problem, many pioneers have proposed new policies. However, it is very difficult to make systems maximally permissive even if their policies can solve the deadlock problem of FMSs. According to our survey, the Maximal number of Forbidding First Bad Marking (FBM) Problems (MFFP) seems to be the best technology to obtain systems' maximally permissive states in the existing literature. More importantly, the number of added control places (CP) is the smallest among the existing research works. However, when the complexity of a flexible manufacturing system increases, the computational burden rises rapidly. To reduce computational cost, we define a new concept named Pre Idle Places (PIP) to enhance the computational efficiency in Seeking Liveness-Enforcing Supervisors. We can bypass all PIP once they can be identified from a deadlock system under the process of solving MFFP. According to the data showed in three classical examples, our proposed Improved MFFP is better than conventional MFFP in terms of computational efficiency with the same controllers.  
**Keywords:** Petri nets; Flexible manufacturing system; Discrete event dynamic systems; Liveness-Enforcing Supervisors; Deadlock Prevention
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 5 Implementation and Results
### Adoption of Hyperledger Fabric Blockchain Technology
#### 5.1.4. Smart Contract Definition  
The smart contract is a program that defines the structure and possible actions already agreed to by the organization before deploying the blockchain network. The smart contract contains the data that must exist or is optional. It also defines the interaction rules between the different peers. The execution of a smart contract starts a new transaction that, when it is endorsed by the required peers, will be added to the ledger of each peer. Figure 4 presents an example of a smart contract structure that contains the details of the recipient's request and the possible actions that the peer could determine.  
It also specifies the peers required to endorse the transaction. The smart contract may re-use several APIs to create, modify, or delete an object from the peer ledger. The main blockchain remains, and after the transaction is finished, all peers' ledgers synchronize with the main blockchain.  
In Figure 4, we use four methods to verify the caller's identity and update the required information or reject it. A 'get' method often denotes a request to obtain details regarding an object's current status. In this example, the get query asks for the caller's identity credentials. The recipient provides the different identity parameters according to the information provided by the caller. The'verify' method returns the peers' decisions about the validity of the query and the information integrity that identifies the caller. An 'update'method creates a new object containing an updated version of an existing one in the ledger world state. A'reject' method represents the rejection of the transaction since the required endorsements are not satisfied.  
Listing 2 shows an example written in JavaScript of a smart contract filled by the recipient requesting an update of the client's bank account phone number. The orderer serves as a collector of endorsements from the peers. The blockchain network will create a new block only if the different involved peers endorse the transaction.  
Since smart contracts are the only piece of code that communicates with the blockchain network, it is necessary to exercise security software testing techniques [31; 32] to ensure that they are bug-free before deploying them.  
```
asyncUpdatePhoneNumber(clientID,ClientAccountNumber,newPhoneNumber, changeReason,requestType){ constquery={ clientID:18, ClientAccountNumber:Bank001122, newphoneNumber:5483790102, changeReason:\"Ichangemyoldphonenumber\", requestType:\"phoneCall ]; returnctx.stub.putState(clientID,Buffer.from(JSON.stringify(query))); }
```  
Listing 2: Sample of smart contract for requesting bank phone number update.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 4 Discussion
### Results from the Applied 2k Factorial Analysis  
Table 2 provides the parameter values used for the analysis of routing strategies for ad hoc IoST networks presented in this work. The factors are as referenced. Figure 3 presents 2k factorial analysis results obtained with the proactive algorithm OLSR and the reactive algorithm AODV. This figure shows that density (e5) is the more relevant factor in the two approaches. This outcome can be explained because this factor is closely related to network connectivity, availability and quality of routing paths, and the PDR.  
The NRT and HMP (e13) combination shows the second weight overall; thus, this is the most relevant combination. NRT defines the time a node is stored in the neighbors table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Name** & **Factor Name** & **Low (\(-\))** & **High (\(+\))** \\ \hline e1 & Neighbor’s refresh time (NRT) & 2 \(\times\) (HMP) & 3 \(\times\) HMP \\ e2 & Number of Retries (RET) & 2 & 7 \\ e3 & Hello message period (HMP) & 0.1 s (AODV) & 1 s (AODV) \\ e4 & Node behavior (NBH) & 0\% of unreachable nodes & 20\% of unreachable nodes \\ e5 & Density & 20 nodes & 240 nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulator parameters.  
Figure 3: Analyzed factors and their combinations according to the 2k factorial analysis with proactive (OLSR) and reactive (AODV) routing approaches.  
The freshness of routing information depends on NRT and HMP; thus, its combination captures the dynamic of the network topology in terms of the links currently available. Note that the third-highest weight in Figure 3 belongs to HMP; hence, it is the second most relevant individual factor. Considering this, and the NRT and HMP combination weight, it can be stated that the dynamic of the network topology in ad hoc IoST networking must be properly addressed when designing routing protocols for this kind of network. The density and HMP combination are relevant as well (fourth weight in Figure 3), which further reinforces our previous statement.
## 2 Materials and Methods
### Features Extraction and Classification Methods
#### 2.3.2 Convolutional Neural Network (CNN)  
CNN is a type of deep feedforward artificial neural network widely used to analyze visual images. Compared with the traditional artificial neural network, the neurons of the CNN and the neurons of the next layer are not fully connected, but locally connected. Furthermore, the parameters of the convolution kernel are weight shared. In general, CNN has the following advantages in  
Figure 7: The structure of proposed features extraction method.  
image processing: (1) Shared convolution kernel, fast speed when dealing with high-dimensional data; (2) Model training replaces manual features extraction and improves stability; (3) Excellent classification effect.  
The structure of CNN is various with different application scenarios, but several modules are similar in these models. For example, when dealing with classification problems, the typical models are AlexNet [27], GoogLeNet [38], ResNet [39], and the three models all include features extraction layer and features mapping classification layer. Features extraction layer includes a plurality of convolution layers, activation layers, and pooling layers. The purpose of convolution operation is to extract features, and activation layers enhance the nonlinearity of both the decision function and the whole neural network, while the pooling layer reduce the size of data space continuously, which can control over-fitting. In the features mapping classification layer, the loss function is applied to punish the difference between predicted and actual results. In addition, in order to improve the generalization ability of the network and prevent over-fitting, we apply the dropout layer in the structure and add regularization constraints to the loss function.  
As illustrated in Figure 7, a multi-layer filter structure is utilized in the proposed method. The parameters of each filter are determined in the training process for features extraction. To summarize, three convolutional layers of different sizes, Rectified Linear Units (ReLU) activation function, and maximizing pooling layer are applied in the proposed model. Moreover, the fully connected layer is utilized for features mapping and classification. Detailed parameter settings of CNN and corresponding experimental results are discussed in Section 3.
## 5 Analysis of the Effectiveness of the Circulating Water  
### The Thermal Simulation without Considering the Circulating Water  
In order to further understand the characteristics of the water circulation technology, a thermal simulation was conducted for a reference case without considering the circulating water. All the parameters were kept the same as in Section 4.2 except for the convective heat transfer coefficient function of the top surface. The convective heat transfer coefficient function for the top surface was revised and set at a constant value of \(25.9\mathrm{kJ}/\left(\mathrm{m}^{2}\cdot\mathrm{h}\cdot\mathrm{{}^{\circ}C}\right)\) (taken same as for the side surface). The results from the thermal simulation without considering the circulating water are compared in Figure 15 with the results from the thermal simulation performed in Section 4.5 with the consideration of the circulating water. The symbol \"#\" as in A1# in Figure 15 is used to refer to the simulation without considering the circulating water while the notation without the symbol refers to the simulation with consideration of the circulating water.  
As shown in Figure 15a for the test results FEM A1 and FEM A1#, while the temperature curves overlapped during the first 0-35 h, the two curves were widely different after 35 h. For the simulation without considering the circulating water, the temperature was set at 55 \({}^{\circ}\)C. At 254 h, the temperature of the FEM A1# was greater than that of the FEM A1 by approximately 10 \({}^{\circ}\)C. This clearly indicates that the cooling rate was significantly slower for the case without water circulation. Consequently, the temperature difference between the foundation surface and the ambient temperature exceeded the safe limit of 20 \({}^{\circ}\)C, and thus became vulnerable to induce cracks on the surface of the mass concrete foundation.  
As shown in Figure 15b for the test results FEM B3 and FEM B3#, the two temperature curves overlapped. This was because the temperature of the bottom surface of the foundation was not affected despite the availability of the circulating water on the upper surface. As shown in Figure 15c, for the test results FEM C2 and FEM C2#, the trends of the temperature rise were identical during 0-30 h. After 30 h, the temperature of the FEM C2# was higher than that of the FEM C2 by approximately 5 \({}^{\circ}\)C. This was because the heat of the concrete surface was exchanged quickly by the circulating water in the case of FEM C2, and hence, the heat of the central concrete flowed effectively to the upper concrete which had a relatively lower temperature. This demonstrates the effectiveness of the circulating water in providing cooling effect to the middle part, in addition to the upper part, of the mass concrete foundation.