esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification
### Simulation Verification of the Np/2 Sensors-Based Schemes  
In this subsection, all simulations are carried out on the pre-built SIMULINK model of a \(75\) kW four-phase \(8/6\) SRM. The baseline scheme for the four-phase SRM is illustrated in Figure 11, where the sampled phase currents could be derived by (8) irrespective of the switching states of the lower switches.  
In all simulations in this subsection, the VPC in [6] is adopted for the phase current regulation; the PWM modulators are configured and the phase current sampling points are arranged according to Figure 10. The average phase voltage is predicted by  
\[V_{A,k+0.5,k+1.5}=\frac{3}{2}\left[\frac{i_{A,k+1.5}^{*}+i_{A,k}}{2}R_{\text{ s}}+\frac{\lambda_{A,k+1.5}-\lambda_{A,k}}{1.5T_{\text{p}}}\right]-\frac{V_{A,k,k+0.5}} {2} \tag{26}\]  
where the subscript \(k\) indicates the present time instant at an overflow of the PWM counter; the subscripts \(k+0.5\) and \(k+1.5\) indicate the time instants \(0.5\,T_{\text{p}}\) and \(1.5\,\text{Tp}\) after the instant \(k\), respectively; \(i_{A}^{*}\) denotes the phase A current reference; \(\lambda_{A}\) denotes the phase flux linkage; \(R_{\text{s}}\) denotes the phase resistance; \(V_{A,k,k+0.5}\) and \(V_{A,k+0.5,k+1.5}\) denote the average voltages during \([k,k+0.5]\) and \([k+0.5,k+1.5]\), respectively.  
The Block diagram of the simulated SRM drive system using the proposal is shown in Figure 12, where \(i^{*}\) is the current reference; \(U_{\text{L}}\) is the lower limit for the average phase voltages; \(\omega\) is the angular speed. The block diagram for the baseline scheme is similar  
Figure 11: The baseline current detection scheme with two multiplexed hall effect sensors for the four-phase SRM.  
Figure 10: PWM configuration and current sampling timing. (a) Positive average phase voltage. (b) Negative average phase voltage.  
to the one in Figure 12. Notable differences between the two diagrams are the phase current detection scheme and the value of the limit \(U_{\rm L}\). Using the baseline scheme, \(U_{\rm L}\) is fixed to \(-U_{\rm dc}\).  
In all simulations with the four-phase SRM, the dc-link voltage \(U_{\rm dc}\) is set to 513 V; the PWM frequency is set to 5 kHz; the rotor angles of 30\({}^{\circ}\) and 60\({}^{\circ}\) mean the unaligned and aligned positions, respectively. For the simulations with the proposal, \(U_{\rm L}\) is set to \(-482\) V if the phase is in the ECR and to \(-U_{\rm dc}\) otherwise.  
Simulations at 100 rpm (\(i^{\star}\) = 60 A), 2000 rpm (\(i^{\star}\) = 20 A), and \(-1500\) rpm (\(i^{\star}\) = 30 A) are conducted, and the actual phase currents (\(i_{\rm A}\) and \(i_{\rm C}\)), the phase currents used by the VPC (\(i_{\rm AS}\) and \(i_{\rm CS}\)), the current detected by the hall effect sensor HS\({}_{1}\) (\(i_{\rm HSI}\)), as well as the drive signals (\(DS_{1}\), \(DS_{2}\), \(DS_{5}\), and \(DS_{6}\)) are presented in the following three subsections. In the fourth subsection, the maximum torque-speed characteristics are obtained.
## 3 Results and Discussion
### Analysis of Kerr Taper Angle  
Figure 6 shows the Kerr taper angles obtained in abrasive waterjet profile cutting of AISI 304L, where the experiment ranged from 0.825\({}^{\circ}\) to 1.550\({}^{\circ}\) for 4 mm, 1.092\({}^{\circ}\) to 1.575\({}^{\circ}\) for 8 mm, and 1.235\({}^{\circ}\) to 1.660\({}^{\circ}\) for 12 mm material thicknesses with traverse speed levels of 90, 120, and 150 mm/min. Gradual machining with a low level of traverse speed of 90 mm/min achieved the smallest kerf taper angle value of 0.825\({}^{\circ}\) for 4 mm, 1.092\({}^{\circ}\) for 8 mm, and 1.235\({}^{\circ}\) for 12 mm material thicknesses. For materials such as stainless steel, a disparity in taper cut is due to deformation-induced from ductile material during machining operations [25]. The formation of kerf taper inherent in AWJM is due to the changing conditions at the interface. Kerf tapering has been observed at the entrance and exit of the jet, initiated by low energy abrasive particles suspended at the exterior of the coherent jet [38]. It has been noted in findings by [PERSON] et al. [39] that kerf taper correlates with traverse speed and material thickness.  
In this research study, the values of KTA were visibly higher at 8 and 12 mm thickness than 4 mm AISI 304L. The results indicate that kerf geometry inaccuracies within machined AISI 304L can be recognised at a higher or increasing traverse speed. Initially, these abrasive particles have high kinetic energy and gradually decrease along with the cutting operation; thus, as material thickness increases, the kinetic energy continuously reduces, causing a higher tapering angle [14]. With the feature of abrasive particles, a lower traverse speed increased the influence of cohesion on metal material to create kerf taper angles.
## 4 Experimental Results and Analysis
### Tool Formal Security Validation  
To conduct a rigorous security assessment of our proposed elliptic curve cryptography (ECC)-based authentication scheme, we leveraged two well-established formal security verification tools: AVISPA (Automated Validation of Internet Security Protocols and Applications) and ProVerif. These tools facilitate an automated and exhaustive cryptographic protocol analysis against a diverse range of adversarial models, including active attackers capable of intercepting, modifying, and injecting messages into the communication channel.  
Unlike conventional ECC-based authentication schemes, our approach integrates a parameterized contraction mechanism \((\alpha,\beta)\) alongside modular arithmetic and factorization techniques to enhance security and computational efficiency. The verification process within AVISPA and ProVerif was tailored to encapsulate these modifications, ensuring that our model accurately reflects the cryptographic operations employed in real-world deployment scenarios.
## 5 Conclusions  
The mechanistic model was developed and successfully validated with the experimental results with the overall average deviation in terms of RMSE of 2.16%. This model was used to predict the performance of the modified ETSC system and evaluate the feasibility  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target** & & \\ \hline Consumption & & 2482 L/day \\ Operating temperature & & 60 \({}^{\circ}\)C \\ \hline Design & Standard ETSC & Modified ETSC \\ \hline Heating medium & Palm oil & Palm oil \\ Mass flow rate & 0.18 kg/s & 0.18 kg/s \\ Inlet temperature & 70 \({}^{\circ}\)C & 70 \({}^{\circ}\)C \\ Outlet temperature & 80 \({}^{\circ}\)C & 80 \({}^{\circ}\)C \\ Production rate & 27 MWh /year & 27 MWh /year \\ Number of tubes & 300 tubes (Area = 45 m\({}^{2}\)) & 240 tubes (Area = 36 m\({}^{2}\)) \\ Arrangement & 3 parallel sets of 100-tube & 3 parallel sets of 80-tube \\ Capital investment & \$16,000 & \$13,000 \\ Breakeven & 5.06 years & 4.09 years \\ \hline \hline \end{tabular}
\end{table}
Table 3: The design parameters of the ETCS applied for the water heating system in a food and beverage industry.  
Figure 9: The simulated manifold temperatures corresponding to standard tube and modified tube.  
of applying the ETSC in a small-scale industry. The stainless-steel scrubbers could improve the performance of the evacuated tube solar thermal absorber excellently, as it could increase the overall heat transfer coefficient by 10 times of the standard tube. The average temperature of modified tube tip (160.32 \({}^{\circ}\)C) was approximately 20 \({}^{\circ}\)C more than standard tube tip. The 20-modified ETSC provided 34.96% increase in efficiency compared with 20-standard ETSC in the same climatic and operating condition. The modified ETSC system was theoretically applied to generate the hot water in the food and beverage industry. This system could produce thermal energy of approximately 27 MWh/year and reduce the total number of tubes by 20% compared with standard ETSC system in the same production rate which could save the capital investment for USD 3000 which could shorten the breakeven of this system by almost 1 year.  
Conceptualization, R.S. and W.C.-a.; methodology, R.S. and W.C.-a.; software, R.S. and A.A.; validation, R.S.; formal analysis, R.S.; investigation, W.C.-a.; resources, P.P.; data curation, S.S.; writing--original draft preparation, R.S.; writing--review and editing, A.A. and W.C.-a.; visualization, R.S.; supervision, W.W., A.A., and W.C.-a.; project administration, W.C.-a. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Mongkut's Institute of Technology Ladkrabang, grant number 2564-02-01-011.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank King Mongkut's Institute of Technology Ladkrabang for the financial support [grant number 2564-02-01-011] and sincerely thank to RANO TECH CO., LTD for supplying the evacuated tube solar collector in the experiment.  
The authors declare no conflict of interest.
# Modification of Insulating Properties of Surfaces of Dielectric High-Voltage Devices Using Plasma
## 4 Precursors and Surface Treatment  
To shape the nanosurfaces of insulator samples, a separate plasma discharge or the insertion of precursors into the gases entering the plasma chamber are applicable; the precursors can be inorganic-based or organic-based [42]. To minimally predict the influence and impact of the plasma discharge on the surface of a dielectric material, it is useful to have a basic numerical model of the precursor and to analyze its electrohydrodynamic properties.  
One of the precursors considered is benzene; benzene-based molecules are also usable. Benzene and its derivatives can be geometrically modeled in a simplified way as toroids,or toroids augmented with the probability space of the excursion of the envelope atoms (carbon C and hydrogen H) and the bonds between them [42].  
With the produced and utilized samples, the plasma discharge and precursor application was performed. Subsequently, the changes in the electrical properties of such treated surfaces were systematically measured and evaluated for repeatability (type A and B uncertainties) and then compared with untreated samples [43].  
The evaluation of the numerical model was based on the results outlined in [42]. The outcomes of the analysis were compared with the experimental evaluations; for example, the frequency spectrum of the benzene model for the Z parameter (impedance parameter) is non-primitive, according to [42]. Such impedances interact with the electromagnetic field of the chamber and its excited frequency characteristic. The mutual interactions and the radiating EMG field change the physico-chemical properties of the precursor and form an output nanosurface on the exposed part of the dielectric surface of the sample.  
To predict the formation of new bonds on the dielectric-polymer surface via precursors, we need to know the precursors' electromagnetic properties. Substances based on benzene molecules embody the elementary types of precursors used. When these interact with the plasma discharge, new bonds are formed on the surface to be treated; the treated surface then changes its parameters. For our purposes, the surface strength was monitored. Knowing the results of the analysis of the models allowed us to design an effective measurement method for diagnosing the discharge and the condition of the treated surface.
# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
## 3 NRH Recommendation Model
### User Profile Construction
#### 3.2.2 User Dynamic Preference Portraits
The user dynamic preference portrait vector \(\mathbf{U}\) can be obtained according to Equation (16) as follows:  
\[U=o_{u}^{1}+o_{u}^{2}+\cdots o_{u}^{H}, \tag{17}\]  
The final image \(v\) of the item is obtained by fusing the two item representations \(\mathbf{v_{1}}\) and \(\mathbf{v_{2}}\), i.e.,  
\[v=g(v_{1},v_{2}), \tag{18}\]  
Here, the fusion function \(g(x)\) is chosen as the linear regression function \(g=wx+b\), where the loss function min\(\mathcal{L}\) of this modular model is \[ \begin{array}{l}\min\mathcal{L}=\mathcal{L}_{RS}+\mathcal{L}_{KG}+\mathcal{L}_{REG}= \sum\limits_{(u,\varphi)\in Y}F(\hat{y}_{uv},y_{uv})+\frac{\lambda_{2}}{2}\sum \limits_{r\in\mathcal{R}_{1}}\left\|I_{r}-E_{1}{}^{T}\mathrm{R}_{1}\mathrm{E} _{1}\right\|_{2}^{2}+\\ \frac{\lambda_{2}^{\prime}}{2}\sum\limits_{(h_{ij},r_{i},t_{j})\in S_{n}^{k}} \left(1-\cos(t_{i},\,(r_{i}+h_{i}))+\frac{\lambda_{1}}{2}\left(\|V_{1}\|_{2}^{ 2}+\|\mathrm{E}_{1}\|_{2}^{2}+\sum\limits_{r\in\mathcal{R}_{1}}\|R_{1}\|_{2}^{ 2}+\|V_{2}\|_{2}^{2}+\|\mathrm{E}_{2}\|_{2}^{2}+\sum\limits_{r\in\mathcal{R}_{ 2}}\|r_{i}\|_{2}^{2}\right)\end{array}  \tag{19}\]  
where \(F(\hat{y}_{uv},y_{uv})\) denotes the cross-entropy function; and \(V_{1,2}\), \(E_{1,2}\), and \(\mathbf{R_{1,2}}\) denote the item and knowledge graph entities and relationships under the two item representations, respectively. The loss function consists of three parts, where \(\mathcal{L}_{RS}\) denotes the value of the cross-entropy loss function between the predicted probability and the true value, \(\mathcal{L}_{KG}\) denotes the item error in the knowledge graph, and \(\mathcal{L}_{REG}\) is the L2 regularization to prevent overfitting of the model.
## 5 Result and Discussion  
The performance evaluation of the proposed framework for determining the level of chlorine substance is focused on obtaining accurate classification on metrics such as recall, precision, and F-score, which are perhaps the most prominent in the classification algorithm. This is done so that the level of chlorine substance can be accurately determined. One way to evaluate how well a machine learning framework performs its job is to look at how accurately it makes optimistic predictions. This particular metric is referred to as precision. The ratio of the number of accurate forecasts, also known as true positives, to the maximum population of precise predictions, also known as pessimistic predictions, is called precision. This total number considers the number of correct predictions and false positives. Another essential measure is recalled, which refers to the portion of a class's samples that a model adequately predicts. It is defined as the proportion of samples.  
One of the most common metrics in machine learning algorithms is the F1 score. This score is the modulation index of the provided accuracy and recall values. The proposed water quality algorithm has proven to be effective in comparison with KNN and SVM, where recall is 90%, the precision value is 92%, and the F-score value is 89%, respectively, as mentioned in Figure 5.  
The thresholds are the separate probability cut-offs used in binary classification to discriminate between the two groups. By using the idea of probability, it finds out how well a model distinguishes between the paths. The proposed model ROC is persuaded towards an actual positive rate with AUC values of 0.9108, which is effective compared to SVM (Support Vector Machine) and KNN (K-Nearest Neighbor), respectively, in Figure 5. The region underneath the receiver operating characteristic (RoC) curve, often known as the coefficient of determination (AUC), is an additional standard measure as mentioned in Figure 6.  
Figure 5: Performance analysis—Recall, Precision, and F-score.  
The fusion performance within a range of 10 to 30 m evaluation and estimation of the suggested model is Centered on Perception, Precision, Accuracy, and Matthew Correlation Coefficient (MCC). In addition, we calculate the F-Curve, the P-Curve, the R-Curve, and the PR-Curve. Ultimately, we analyze the performance comparison based on metrics such as training dataset vs. box loss, obj loss, cls loss, and other similar metrics. The recommended approach's accuracy may now be evaluated by dividing the number of actual positive (+VE) forecasts by the total number of optimistic (+VE) predictions, as seen in Figure 7.
## 2 Proposed Antenna with SIW Feeding Network
Figure 4: Simulated and measured reflection coefficients of the proposed antenna: (**a**) TE\({}_{10}\) mode; (**b**) TE\({}_{20}\) mode. Figure 5: Simulated and measured mutual couplings of the proposed antenna. Figure 6: Simulated and measured efficiency of the proposed antenna: (**a**) TE\({}_{10}\) mode; (**b**) TE\({}_{20}\) mode. Figure 8 shows measured and simulated maximum gains in the upper hemisphere of the radiation patterns for the TE\({}_{10}\) and TE\({}_{20}\) modes according to the frequency. At 4.85 GHz, a measured maximum gain of 2.73 dBi for the TE\({}_{10}\) mode is observed at \(\theta=0^{\circ}\), and a measured maximum gain of \(-3.9\) dBi for the TE\({}_{20}\) mode is observed at \(\theta=45^{\circ}\). Figure 9a presents the simulated and measured 2D radiation patterns for the TE\({}_{10}\) mode where \(\Sigma\) is defined as the amplitude of the sum pattern in the \(zx\)-plane at 4.85 GHz. Similarly, Figure 9b is for the TE\({}_{20}\) mode, and \(\Delta\) is defined as the amplitude of the difference pattern. The measured gains at the bore sight (\(\theta=0^{\circ}\)) in the TE\({}_{10}\) and TE\({}_{20}\) modes are 2.73 dBi and \(-12.3\) dBi, respectively, which shows a difference of more than 15 dB. These results verify that the proposed antenna with the multi-mode SIW feeder is suitable for monopulse systems that require large gain difference between the sum and difference patterns. \begin{table}
\begin{tabular}{c c c c} \hline
**Part** & **Available Power** & **Dielectric Loss** & **Conduction Loss** & **Efficiency** \\ \hline TE\({}_{10}\) mode & 1 W & 0.59 W & 0.03 W & 38\% \\ TE\({}_{20}\) mode & 1 W & 0.675 W & 0.035 W & 29\% \\ \hline \end{tabular}
\end{table} 
Table 2: Losses and efficiencies of the proposed antenna. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(p_{1}\) & 62.8 mm & \(s_{1}\) & 1.35 mm \\ \(p_{2}\) & 20.25 mm & \(s_{2}\) & 2.7 mm \\ \(h_{1}\) & 8.64 mm & \(l_{1}\) & 37.8 mm \\ \(h_{2}\) & 2.16 mm & \(l_{2}\) & 37.8 mm \\ \(g\) & 5.5 mm & \(l_{u1}\) & 10.8 mm \\ \(w_{1}\) & 25 mm & \(l_{u2}\) & 31.7 mm \\ \(w_{2}\) & 62.8 mm & \(w_{b}\) & 8.5 mm \\ \(l_{1}\) & 58.05 mm & \(l_{b}\) & 5.67 mm \\ \(l_{2}\) & 58.05 mm & \(m_{1}\) & 6.1 mm \\ \(w_{u1}\) & 5.94 mm & \(m_{2}\) & 29.4 mm \\ \(w_{u2}\) & 4.32 mm & \(l_{s1}\) & 20.6 mm \\ \(d_{1}\) & 1.35 mm & \(l_{s2}\) & 4.05 mm \\ \(d_{2}\) & 2.7 mm & \(w_{s1}\) & 0.7 mm \\ \(b_{1}\) & 3 mm & \(b_{2}\) & 2 mm \\ \hline \end{tabular}
\end{table} 
Table 3: Design parameters of the antenna using Duroid substrate. Figure 7: Efficiency comparisons by the materials of the substrate.
## 1 Introduction
Therefore, the main objective of this research is twofold: first, to proactively predict potential pallet collapse incidents by analysing data from past incidents and the logistics chain; and, second, to provide practical recommendations that can prevent such collapses, thereby minimizing financial and human risks. The glass industry was chosen as the application case because of its fragile products, but the findings apply to other industries that face similar risks. Thus, this study differentiates itself by addressing the specific contributing factors to pallet instability, which previous research has neglected. To ensure a rigorous and systematic approach, this research employed the CRoss-Industry Standard Process for Data Mining (CRISP-DM) framework to structure the investigation and analysis. The use of this framework aided in ensuring that the study is both comprehensive and methodologically sound. In summary, this study makes significant contributions to the literature by focusing on the often-neglected issue of pallet stability, particularly within the glass industry. It provides data-driven predictions and strategies specifically aimed at mitigating the risks of pallet collapse, thereby addressing gaps left by previous generalized approaches in the field. Additionally, the study offers a methodological framework that is not limited to the glass industry but can be applied to other sectors facing similar challenges, broadening the overall impact of the research. The remainder of the article is organized as follows: Section 2 presents a comprehensive literature review to situate this study within the existing body of knowledge. Section 3 outlines the developed methodology, detailing the data collection process, the used analysis techniques, and the employed machine learning models. Section 4 discusses the findings and interprets their implications for both theory and practice. Finally, Section 5 concludes the article, summarizing the key outcomes and suggesting avenues for future research.
## 4 Experimental Verification  
The correctness of the above theory is tested by experiments. In this experiment, a cantilever beam with a mass \(m=0.5\) kg at the end, as shown in Figure 12, is used to simulate a single-degree-of-freedom system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Level** & **5\%** & **10\%** & **15\%** \\ \hline \(f_{1}\) & 1.43\% & 2.02\% & 3.72\% \\ \(f_{2}\) & 2.48\% & 3.28\% & 4.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Load identification errors with different noise levels.  
Figure 11: \(f_{2}\) load identification with different noise levels.  
Figure 10: \(f_{1}\) load identification with different noise levels.  
According to the knowledge of mechanics of materials, the equivalent stiffness of cantilever beam to mass block in the loading direction is  
\[k=\frac{3 EI}{L^{3}} \tag{22}\]  
where \(I\) is the moment of inertia of the beam section.  
Based on Equation (22), the equivalent stiffness of spring \(k=64\) N/m. The displacement and velocity signals of the mass block are collected by laser sensors, and the force signals are collected by force sensor and loaded by a vibration exciter. The sampling frequency of this experiment is 1024, and the time length of each micro segment is 0.01 s. The sampling duration is 20 s, and the applied excitation is \(f=10\sin(10\pi t)\). Table 3 shows the equipment details for the identification experiment.  
The identification results are as follows.  
It can be seen from the identification results in Figure 13 that there are still some errors between the identification results and the actual load. Through the error analysis of the identification results, it is found that the maximum error occurs at t = 4.6 s, and the error reaches 33%. The position with larger error is mainly concentrated in the extreme value of the actual load. The main reason for the errors is the experimental conditions--not only the noise in the environment but also the boundary condition of the cantilever beam cannot  
\begin{table}
\begin{tabular}{c c c c} \hline
**Length/m** & **Width/m** & **Thickness/m** & **Elastic Modulus/GPa** \\ \hline
0.5 & 0.02 & 0.002 & 200 \\ \hline \end{tabular}
\end{table}
Table 2: The parameters of the cantilever beam.  
Figure 12: Test-state diagram.  
be idealized in the experiment. However, through global error statistics, it can be found that the mean-square error is about 5.78%. From this perspective, it can be considered that the experiment has verified the accuracy and the feasibility of the proposed dynamic-load identification method.