esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 MGTO for Solving Engineering-Optimization Problems
### The Pressure-Vessel Problem  
The pressure-vessel problem proposed by [PERSON] and [PERSON] ([PERSON] and [PERSON] (2007) aims to reduce the cost of the pressure vessel under the pressure requirements. Figure 6 is a schematic diagram of the pressure vessel. The parameters include the following details: the thickness of the shell (\(Ts\)), the thickness of the head (\(Th\)), the inner radius of the vessel (\(R\)) and the length of the cylindrical shape (\(L\)).  
Consider:  
\[\stackrel{{\rightarrow}}{{x}}=[x_{1}\ x_{2}\ x_{3}\ x_{4}]=[T_{s} \ T_{h}\ R\ L] (44)  
Minimize:  
\[f(\stackrel{{\rightarrow}}{{x}})=0.6224x_{1}x_{3}x_{4}+1.7781x_{2} x_{3}^{2}+3.1661x_{1}^{2}x_{4}+19.84x_{1}^{2}x_{3} \tag{45}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Optimal Values for Variables**} & \multirow{2}{*}{**Optimal**} \\ \cline{2-2} \cline{4-5}  & \(\mathbf{h}\) & \(L\) & \(t\) & \(\mathbf{B}\) & **Cost** \\ \hline MGTO & 0.2057 & 3.2531 & 9.0366 & 0.2057 & **1.6952** \\ GTO & 0.2059 & 3.2510 & 9.0331 & 0.2059 & 1.6958 \\ HS & 0.2442 & 6.2231 & 8.2915 & 0.2400 & 2.3807 \\ WOA & 0.205395 & 3.528467 & 9.004233 & 0.207241 & 1.735344 \\ GSA & 0.182129 & 3.856979 & 10.000 & 0.202376 & 1.87995 \\ MVO & 0.205463 & 3.473193 & 9.044502 & 0.205695 & 1.72645 \\ OBSCA & 0.230824 & 3.069152 & 8.988479 & 0.208795 & 1.722315 \\ PHSSA & 0.202369 & 3.544214 & 9.04821 & 0.205723 & 1.72802 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparative optimization results for the welded-beam design.  
Figure 6: Pressure-vessel-design problem.  
Constraints:  
\[g_{1}(\overrightarrow{x})=-x_{1}+0.0193x_{3}\leq 0 \tag{46}\]  
\[g_{2}(\overrightarrow{x})=-x_{3}+0.00954x_{3}\leq 0 \tag{47}\]  
\[g_{3}(\overrightarrow{x})=-\pi x_{3}^{2}x_{4}-\frac{4}{3}\pi x_{3}^{3}+1296000 \leq 0 \tag{48}\]  
\[g_{4}(\overrightarrow{x})=x_{4}-240\leq 0 \tag{49}\]  
Variable range:  
\[\left\{\begin{array}{l}0\leq x_{1}\leq 99\\ 0\leq x_{2}\leq 99\\ 10\leq x_{3}\leq 200\\ 10\leq x_{4}\leq 200\end{array}\right. \tag{50}\]  
From Table 8, we can see that the optimal solution of the function obtained by the MGTO is 5734.9131, while \(x\) is equal to 0.7424, 0.3702, 40.3196 and 200. The lowest cost calculated by the MGTO is superior to other optimization algorithms, including the GTO [41], HHO [24], SMA [54], WOA [44], GWO [6], MVO [12] and GA [15], indicating that the proposed model has the merits in solving the pressure-vessel problem.
# Dual-Circularly Polarized 60 GHz Beam-Steerable Antenna Array with 8 \(\times\) 8 Butler Matrix
## 3 Performance of the Proposed Antenna Array System  
### Simulated Results  
The simulated reflection coefficients of the proposed antenna array system with an \(8\times 8\) Butler matrix are shown in Figure 10(a). Over the frequency range of 55.3 GHz-64.9 GHz, the values of the reflection coefficients are lower than \(-10\) dB. Figure 10(b) shows the simulated mutual couplings between the input ports of the proposed antenna array system when port 4 is excited. The mutual couplings are less than \(-10\) dB over the frequency range of 57.5 GHz-63.2 GHz.  
Figure 12 shows the simulated gain patterns in the \(xz\)-plane and the three-dimensional radiation patterns at 60 GHz of the proposed antenna array system when ports 2, 4, 5, and 7 are excited. At 60 GHz, the maximum gains are 9.39 dBi, 10.67 dBi, 10.63 dBi, and 9.38 dBi when the input ports 2, 4, 5, and 7 are excited, respectively. The beam-steering angles are \(-38^{\circ}\), \(-11^{\circ}\), \(+11^{\circ}\), and \(+39^{\circ}\), respectively. At 57 GHz, the maximum gains and beam-steering angles for ports 2, 4, 5, and 7 are 7.51 dBi at \(-44^{\circ}\), 9.37 dBi at \(-14^{\circ}\), 9.33 dBi at \(+14^{\circ}\), and 7.45 dBi at \(+45^{\circ}\), respectively. At 62 GHz, the maximum gains and beam-steering angles for ports 2, 4, 5, and 7 are 8.61 dBi at \(-37^{\circ}\), 10.01 dBi at \(-11^{\circ}\), 9.87 dBi at \(+11^{\circ}\), and 8.53 dBi at \(+37^{\circ}\), respectively.  
Figure 11(a) shows the simulated surface current distributions of the patch antenna array. It can be seen that the circularly rotating surface currents are formed on the patch conductors by the \(\pm 90^{\circ}\) phase difference between two inputs of the designed dual-fed patch antenna. The RHCP is formed when  
Figure 11: Simulated results of the proposed antenna array system: (**a**) reflection coefficient and (**b**) mutual coupling.  
Figure 10: (**a**) Simulated reflection coefficient for various length of the parasitic stacked patch. (**b**) Simulated gain pattern of the patch antenna array.  
four ports from port 1 to port 4 are excited and LHCP is formed when four ports from port 5 to port 8 are excited. The simulated axial ratios of the proposed antenna array system for the excitation of ports 2, 4, 5, and 7 of the Butler matrix are shown in Figure 13b. The axial ratios are less than 3 dB over the frequency range of 54.9 GHz-61.5 GHz. The operating modes of the proposed antenna array system for selecting each input port are listed in Table 2.  
Figure 12: Simulated gain patterns of the proposed antenna array system with the excitation of (**a**) port 2, (**b**) port 4, (**c**) port 5, and (**d**) port 7.  
Figure 13: Simulated current distributions and the axial ratio: (**a**) surface current distributions of the patch antenna array and (**b**) axial ratio of the proposed antenna array system.
# Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
## 3 Results and Discussion
### Results of Hyper-Parameters Tuning
Similarly, in the Cubist regression model, which generally has two tweaking parameters that can be fine-tuned [32], _committees_ and _neighbors_, the tuning parameters are established as follows: the optimum hyper-parameter _committees_ are selected in the scope of {1,5,10,50,75,100} and the optimum hyper-parameter _neighbors_ is selected in the scope of {0,1,3,5,7,9}. To tune the Cubist model over different values of _neighbors_ and _committees_, the train function in the _caret_ package [50] can be employed to optimize the previously mentioned parameters. In this study, a smaller value of RMSE indicates better optimization results of the Cubist regression model. The relationship between _committees_ and _neighbors_ is shown in Figure 4b. The error does not sharply decrease after the parameter _committees_ attain a value of 10. The finally determined values for the Cubist regression model (Figure 4b) were _committees_ = 75 and _neighbors_ = 1. The RMSE, R\({}^{2}\) and MAE of the Cubist model for the training set are 0.1400, 0.9576 and 0.0856, respectively. In addition, the RMSE, R\({}^{2}\) and MAE of the predicted values  
Figure 3: Illustration of the total procedure for the evaluation of the shear strength of RFM using the proposed methods. using the linear regression (LR) method [56] are 0.2177, 0.8931 and 0.1317, respectively, for the training data. The RMSE, R\({}^{2}\) and MAE of the predicted values using ANN method [20] (delay = 0.1, size = 27) are 0.1875, 0.9168 and 0.1134, respectively, for the training data. The findings reveal that the Cubist model has the ability to capture high nonlinearity and incorporate many parameters. Additionally, Figure 5 shows the result of a comparison of the performance of random forests, Cubist regression, ANN and linear regression algorithms with RMSE, R\({}^{2}\) and MAE using a fivefold CV phrase with the training set of R\({}^{2}\), RMSE and MAE with its variance, it can be easily observed that the Cubist model yields better performance than Random forests regression model. Compared with the random forests, the difference between lower and upper quartiles in the Cubist model is smaller, while in respect of the variance of accuracies, the random forests model tends to perform better in different iterations.
## 2 Materials and Methods
### Flux Data Processing  
The data collection process involved compiling daily summary logs containing half-hour flux measurements, which were subsequently stored as text files with tab-delimited  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Instrument** & **Model** & **Manufacturer** & **Installation Height** \\ \hline Soil heat flux & Heat flux plates & HFP01 SC & \begin{tabular}{c} Hukseflux, \\ Delft, The Netherlands \\ \end{tabular} & \(-\)0.08 m \\ \hline Soil temperature & Thermocouples & TCAV, Type E & \begin{tabular}{c} Campbell Sci. Inst., \\ Logan, UT, USA \\ \end{tabular} & \(-\)0.02 \& \(-\)0.04 m \\ \hline Soil moisture & \
\begin{tabular}{c} Volumetric moisture \\ sensors \\ \end{tabular} & CS650 &
\begin{tabular}{c} Campbell Sci. Inst., \\ Logan, UT, USA \\ \end{tabular} & \(-\)0.06 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study Site/Location** & \multicolumn{2}{c}{**Recording Periods**} \\ \hline  & **Year** & **Months** \\ \hline ES-LTs & 2017 & July–September \\
39.0424\({}^{\circ}\) N, \(-\)2.0890\({}^{\circ}\) W & 2018 & May–October \\ \hline ES-TzM & 2020 & July–September \\
39.2660\({}^{\circ}\) N, \(-\)1.9397\({}^{\circ}\) W & 2021 & April–October \\
2022 & April–October \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data collection periods per year for both study sites, LTs and TzM.  
formatting. To address data gaps and partition NEE into ecosystem respiration (Reco) and GPP, we employed the REddyProc(r) online tool [64] developed by the Max Planck Institute for Biogeochemistry, which uses the standard EC processing methods based on the REddyProc R Package. The flux measurements were filtered using the Ustar-based methods in conjunction with Moving Point Test threshold estimation algorithms [65]. Subsequently, for NEE partitioning, we utilized daily-time based algorithms [66], which use the light response curve to fit the daytime NEE, including the temperature sensitivity of respiration and the vapor pressure deficit (VPD) limitation of photosynthesis. For comprehensive details on data filtering, gap filling, and partitioning techniques, please refer to the REddyProc website [67]. Following the convention for NEE data presentation, negative flux values indicate net CO\({}_{2}\) sequestration (flux from atmosphere to ecosystem), while positive values indicate net CO\({}_{2}\) release (flux from ecosystem to atmosphere). The values were maintained within the threshold \(\pm 50\) umol C m\({}^{-2}\) s\({}^{-1}\)[68; 69]. Daily cumulative values for NEE, GPP, and ET were computed by integrating hourly values using R free software (R version 4.3.0). eWUE was determined by means of the indirect technique, dividing GPP by ET [44; 45]. The seasonal dynamics were explored as monthly cumulative values (NEE and GPP; g C m\({}^{-2}\)) and mean monthly values for eWUE (g C kg\({}^{-1}\) H\({}_{2}\)O).
# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 4 Experiment and Analysis
### Model Comparison  
The proposed model, denoted as ConvFM, was compared with the previously methods: TransE, ConvE, ConvKB, DisMuit, CompIEX, and KB-GAT.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 2 Preliminaries
### Introduction to MPC
Another option would be to use Trusted Execution Environments (TEEs) [36], such as Intel's Software Guard Extensions (SGX) ([[https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/software-guard-extensions.html](https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/software-guard-extensions.html)]([https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/software-guard-extensions.html](https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/software-guard-extensions.html)) (accessed on 30 June 2025))), AMD Secure Encrypted Virtualization (SEV) ([[https://www.amd.com/en/developer/sev.html](https://www.amd.com/en/developer/sev.html)]([https://www.amd.com/en/developer/sev.html](https://www.amd.com/en/developer/sev.html)) (accessed on 30 June 2025)), ARM TrustZone ([[https://www.arm.com/technologies/trustzone-for-cortex-m](https://www.arm.com/technologies/trustzone-for-cortex-m)]([https://www.arm.com/technologies/trustzone-for-cortex-m](https://www.arm.com/technologies/trustzone-for-cortex-m)) (accessed on 30 June 2025)), or IBM Secure Service Container ([[https://www.ibm.com/docs/en/systems-hardware/linux/3932](https://www.ibm.com/docs/en/systems-hardware/linux/3932)]([https://www.ibm.com/docs/en/systems-hardware/linux/3932](https://www.ibm.com/docs/en/systems-hardware/linux/3932)) -LA2?topic=container-secure-service-technology-deploying-appliances (accessed on 6 August 2025)). These offer secure enclaves for isolated data processing and are often used to protect sensitive computations from untrusted system components. However, they rely on a centralized hardware root of trust, requiring all parties to place faith in the security and integrity of a single chip vendor, contradicting our goal of avoiding a central trusted entity--even though, admittedly, their role would be different from that in the case of plaintext computations by a central authority. However, TEEs have been repeatedly shown to be vulnerable to side-channel and hardware-level attacks. Finally, functional encryption (FE) [37] allows users to compute specific functions on encrypted data without revealing the data itself. While powerful in theory, FE is still largely impractical for complex, real-world applications like collaborative logistics optimization due to limited expressiveness and a lack of efficient general-purpose implementations. Additionally, FE typically assumes a central authority to manage keys and functions, conflicting with the decentralized trust requirements of multi-party collaboration.
## 2 Literature Review  
### Company Business Scope Identification using BI  
Studies of business intelligence (BI) as a research field are well documented in the literature. Each branch of a United States government agency conducts business analysis using its own methods and metrics, which are unknown and meaningless to other branches. The Standard Industrial Classification system (SIC) [1] was put forward to offer standardized and meaningful ways to measure business scopes. SIC codes are four-digit numerical representations of major businesses and industries. SIC codes are assigned based on common characteristics shared in the products, services, production, and the delivery system of a business. SIC codes have been available since 1939, but are being replaced by North American Industry Classification System (NAICS) codes [2]. The Global Industry Classifications Standard (GICS) system [3], jointly developed by Standard & Poor's (S&P) and Morgan Stanley Capital International (MSCI), is gaining popularity among financial practitioners, while the Fama and French [4] algorithm was devised by academics. Global Industry Classifications Standard (GICS) classifications are significantly better at explaining stock return co-movements, as well as cross-sectional variations in valuation-multiples, forecasted and realized growth rates, R&D expenditures, and various key financial ratios. The advantages of GICS are consistent from year to year and are most pronounced among large firms. The International Standard Industrial Classification of All Economic Activities (ISIC), which was established and updated by United Nations (UN), is a widely adapted version of GICS at present.  
Google Finance [5] approved and adopted a document which defines the company's business scopes. When users open a certain link of stocks in the NASDAQ or NYSE, it will find a column of \"Similar Stocks\", created and updated by Google Finance through company's news, annual reports, and financial reports. Some important business scopes are identified, including the retailing, finance and banking, manufacturing, agriculture, information technology, energy, transport, and medical sectors. These identification labels were believed to help investors choose stocks or make decisions with necessary consideration of the company's background.  
However, among the existing classification systems, the standards provided by Google are slightly rough, and thus some industries cannot find corresponding classifications under this system. In addition, since the ISIC version is not updated, the existing ISIC lacks timeliness and the identification result is not satisfactory when it is used alone. By analyzing existing studies on the company's business scopes identification among BI, the authors believe that there is a need to provide ISIC improvement based on the current U.S. market.
# Quantitative Model for the Value of the 3D Product Model Use in Production Processes
## 3 Results
### Faster New Product/Model Roll Out to Production  
In order to have cohesiveness between the varying sections of this paper, the average profitability per day (\(P_{D_{m}}\)) calculated in the prior section, at the 60 s takt time, will be used throughout the paper. Note, that although the prior section demonstrated a decrease in takt time by implementing the 3D product model in the production process and such an effect would increase the incremental profitability demonstrated here, that is not beingtaken into account. Such effects are called interaction factors between variables and can be considered in future research.  
By having the 3D product model used in production processes, an automotive manufacturer was able to reduce the time before the production line was fully operational after introducing a new product/model from what had traditionally been 4 weeks of equivalent lost production time down to 2 weeks of equivalent lost production time, ultimately increasing the uptime of the line by 10 days. With the increase in uptime following the new product/model roll out (\(U_{m}\)) of 10 days, and with 0.2 new product/model roll outs per year (\(E\)) (or in other words, a new product/model roll out once every five years) then the incremental profitability on the line is USD 14.4 million per year (\(P_{U_{m}}\)). Using the 3D product model in the production process to generate faster new product/model roll outs results in an increase in profitability of 0.8%.
# Deep-Learning Software Reliability Model Using SRGM as Activation Function
## 3 Software Reliability Model  
### NHPP Software Reliability Growth Model  
Software reliability is the ability of software to successfully perform its intended function for the intended duration under given conditions of use. The reliability function for evaluating software reliability is shown in Equation (1), which is the probability that the software will operate for at least \(t\) hours. \[R(t)=P(T>t)=\int_{t}^{\infty}f(u)du \tag{1}\]  
The probability density function \(f(t)\) assumes that the time to failure of the software is a random variable \(T\). To measure the reliability function \(R(t)\), the mean number of failures is assumed to follow an exponential distribution with parameter \(\lambda\). The parameter \(\lambda\) follows the NHPP, a function that changes over time. The model is derived by Equation (2), where \(N(t)\) is the number of failures that have occurred by time \(t\), and \(n\) is the number of failures. \[\Pr\{N(t)=n\}=\frac{\{m(t)\}^{n}}{n!}e^{-m(t)},n=0,1,2,\ldots,\ t\geq 0 \tag{2}\]  
where a mean value function \(m(t)\) is the integral of \(\lambda(t)\), the intensity function, which is the number of failures at time \(t\), from time 0 to time \(t\), which is the \(m(t)\) from 0 to time \(t\) and follows Equation (3). The intensity function \(\lambda(t)\) is the number of instantaneous failures at time \(t\). \[m(t)=\int_{0}^{t}\lambda(s)ds \tag{3}\]The \(m(t)\) is used to develop a software reliability model through a differential equation, with \(a(t)\) representing the number of failures and \(b(t)\) representing the failure detection rate. It is expressed as Equation (4), which is the most basic SRGM. \[\frac{dm(t)}{dt}=b(t)[a(t)-m(t)] \tag{4}\]  
When Equation (4) is multiplied by \(\eta\), it is defined as a SRGM with assumptions about an uncertain operating environment added. When the \(m(t)\) is multiplied once more, it is defined as a SRGM which assumes that software failures occur dependently. By solving each differential equation with respect to \(m(t)\), one can derive the SRGM that satisfies each condition. The \(a(t)\), \(b(t)\), and \(\eta\) are expressed in variable shape. The functions contain a number of parameters with meanings such as number of software failures, expected number of failures, failure detection rate, etc. The parameters included in each model can have different meanings for the same parameter, so please refer to the references for each model. The software models developed on this basis are as follows: Models 1-9 are SRGMs obtained through the basic form of differential equations, Models 10-12 are SRGMs assuming an uncertain operating environment, Models 13 and 14 are SRGMs assuming a dependent failure, and Model 15 is the SRGM assuming a dependent failure in an uncertain operating environment. Table 1 shows the descriptions for the 15 models.
## 4 Results and Discussion
### Risk Assessment of Soil's Heavy Metals
#### 4.4.3 Evaluation Results of the Risk Assessment Code Method  
The evaluation results of the risk assessment code method for soil heavy metal risk assessment in the study area are presented in Table 9 and Figure 7. The average RAC index reveals that heavy metals in the soil of the study area are ranked as follows: Zn > Cd > Cu > Ni > Pb > As > Hg > Cr, indicating a significant pollution risk from As, Hg, and Cr. Cd, Cu, Ni, and Pb pose slight pollution risks while Zn poses a moderate pollution risk and represents the highest ecological environmental threat.
## 3 Results
### Accuracy Comparison  
Top-1 and Top-5 accuracies are standard metrics for evaluating image classification models, reflecting their ability to identify the correct class or include it among the top five predictions, respectively. These metrics are critical for assessing the effectiveness of hardware-optimized models in balancing computational efficiency and performance.  
For VGG16, the FPGA implementation achieved a Top-1 accuracy of 89.54% and Top-5 accuracy of 99.54%, closely aligning with the CPU implementation's 89.69% (Top-1) and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**FPGA**} & \multicolumn{2}{c}{**CPU**} \\ \cline{2-5}  & **VGG16** & **VGG19** & **VGG16** & **VGG19** \\ \hline Top-1 Accuracy & 89.54\% & 87.46\% & 89.69\% & 88.04\% \\ Top-5 Accuracy & 99.54\% & 98.97\% & 99.54\% & 98.97\% \\ Inference Latency (ms/frame) & 0.652 & 0.846 & 4.75 & 5.59 \\ Model size (MB) & 60.1 & 81.4 & 180 & 244 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The measured results of the proposed design implemented on FPGA and CPU.  
99.54% (Top-5). Similarly, VGG19 on FPGA attained a Top-1 accuracy of 87.46% and Top-5 accuracy of 98.97%, compared to the CPU's 88.04% (Top-1) and 98.97% (Top-5). These results indicate minimal accuracy degradation due to 8-bit quantization, a key optimization for reducing computational and memory demands. The confusion matrices (Figure 4) further validate these metrics, demonstrating consistent classification performance across all 10 CIFAR-10 classes in our design. This highlights that the quantization process preserves critical model characteristics.  
These results demonstrate the effectiveness of FPGA deployment in maintaining competitive accuracy, with the added benefits of reduced inference latency and resource utilization in our work. This balance between accuracy and efficiency positions our proposed design as a compelling choice for computer vision applications in which real-time image classification and decision-making are required while resources are constrained. In addition, while VGG19 is a larger network than VGG16, our results suggest that increasing network size does not guarantee improved accuracy. The optimal model size depends on factors like input image resolution and the specific task at hand.