esa-sceva/satcom-chunk-collection huggingface dataset:
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 2 Optimization of Trusses
### Discrete Sizing Optimization  
The first ideas for the sizing optimization of trusses by considering available discrete dimensions were presented in the late 1960s [26]. However, as the main focus in the 1960s and 1970s was on the development of efficient algorithms for solving large-scale NLP problems, the intense progress in discrete optimization methods did not start until the 1980s. The first attempts of the discrete sizing optimization of trusses were based on linear programming, e.g., Templeman's algorithm [27; 28], followed by the solving of NLP problems with sequential linear programming (sequential linearization algorithm) [29; 30; 31].  
In addition, practically all the developed discrete optimization techniques were applied to the truss problems, e.g., the genetic algorithm [32; 33; 34], simulated annealing [35; 36], the penalty function method [37; 38] and neuron networks [39]. In some cases, multi-level approaches were presented where the continuous optimization was performed at the first level, and the obtained continuous solution was used as a starting point for the subsequent discrete optimization, e.g., [30; 31; 32; 40; 41].  
In most of the mentioned references, trusses were optimized by considering stress and deflection constraints and either without or by considering the simplified Euler buckling constraints. Cross-section areas were regarded as independent sizing variables, and a set of discrete values of the section areas (in some cases, based on the available sections) was defined. Taking this into account, researchers thus focused mainly on testing different optimization techniques, while the practical applicability of the obtained results was often kept in the background. One of the exceptions was presented in [42], where the trusses were optimized by considering the design constraints in accordance with Eurocode 3 [43] and by considering standard steel tubes as the cross-sections of elements.
## 3 Methodology
### Probability Matching Function  
To build a probability matching function, we consider some criteria proposed in the scientific and technical literature in relation to the comparative evaluation of the effectiveness of a UAV control system [14; 18; 19]:  
* \(p\) is the probability of reaching the target with one UAV. When comparing options according to this criterion, preference is given to the option for which this probability has the highest value.
* \(\omega=1/p\) is the average number of UAVs needed to reach the goal. This criterion allows indirectly evaluating the possible material costs for solving the target problem.
* \(n=\frac{\ln(1-W_{s})}{\ln(1-p)}\) is the required number of UAVs to achieve the goal with a given probability \(W_{s}\). This criterion relates the probability \(p\) of reaching the goal with one UAV with the value \(n\) of the required number of UAVs for a given probability of reaching the goal \(W_{s}\).  
As follows from the analysis of the criteria listed above, the third criterion bears the greatest information load, which allows for comparing the values \(n\) of the required number of UAVs for different options for a given probability of achieving the goal \(W_{s}\).  
In this case, the ratio of the required number of UAVs for two options can be represented as follows:  
\[\overline{n}=\frac{n_{1}}{n_{2}}=\frac{\ln(1-p_{2})}{\ln(1-p_{1})}. \tag{4}\]  
The choice of specific types of probability correspondence function \(s_{p}(p)\) is not a strictly formalized task; therefore, such a choice was made heuristically by analyzing various known functions.  
\[s_{p}(p)=\left\{\begin{array}{ll}\frac{\ln(1-p)}{\ln(1-W_{s})},&if\ p<W_{s}, \\ 1,&if\ W_{s}\leq p\leq 1,\end{array}\right. \tag{5}\]  
or  
\[s_{p}(p)=\left\{\begin{array}{ll}\frac{\ln(1-p)}{\ln(1-W_{s})},&if\ 0\leq p \leq p^{*},&+\frac{\frac{p-p^{*}}{1-p^{*}},&if\ p^{*}<p\leq 1,\\ \end{array}\right. \tag{6}\]where the values of \(p^{*}\) and \(W^{*}\) are determined by dependencies:  
\[p^{*}=1-\exp[-2],W^{*}=\frac{\ln(1-p^{*})}{\ln(1-W_{s})}.\]  
The function \(s_{p}(p)\) in the form of (4) makes it possible to estimate the degree of compliance of the value \(p\) with respect to the given efficiency \(W_{s}\) in reciprocal values of the required number of means \(n\).  
For example, if the condition \(p\geq W_{s}\) is satisfied for the considered application of the UAV, then it fully corresponds to the task, and for it, the probability matching function is \(s_{p}=1\). If \(p<W_{s}\), then \(<1\), and the smaller the value of \(p\), the smaller the value of the probability correspondence function.  
The correspondence function in the form of (4) is continuous, but has an indifference segment (constant and equal to 1) on the interval \(p\in[W_{s},1]\). The correspondence function in the form of (5) is continuous and has a continuous first derivative. Figure 1 shows the probability correspondence functions \(s_{p}(p)\) for the above Formulas (4) and (5).
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 9 Conclusions  
In this paper, we propose decentralized data distribution supported by a hierarchical and multi-tier network structure. The proposed solution combines local and decentralized management with centralized decisions to efficiently use the available network resources and meet the requirements of Industry 4.0 applications. Contrary to the recent theoretical and abstract simulative comparisons, we analyze and compare the characteristics of four protocol stacks for data distribution in wireless industrial networks, built mainly from proposed standards, and we evaluate the performance of the four methods via a detailed simulation analysis. The building blocks that we used in order to compose the protocol stacks of the different industrial data distribution methods (both centralized and decentralized) are WirelessHART, RPL and proxy selection. We implemented the methods in OMNeT++ and we evaluated their performance via a detailed simulation analysis. We demonstrated that the careful selection of a limited set of proxies for data caching in the network can lead to increased data delivery success rate and low data access latency. Finally, we demonstrated two test cases in a smart factory context. First, we showed the collaboration between robotic elements and wireless data services. Second, we showed the integration with an industrial fog node which controls the shop-floor devices. We reported selected results in much larger scales, obtained via simulations.  
Conceptualization, T.P.R. and M.C.; Data curation, A.F. and E.P.; Formal analysis, T.P.R. and A.P.; Funding acquisition, A.P. and M.C.; Investigation, A.F. and E.P.; Methodology, E.P.; Project administration, T.P.R., A.P. and M.C.; Software, A.F.; Supervision, T.P.R. and A.P.; Validation, T.P.R. and E.P.; Visualization, A.F.; Writing--original draft, A.F., E.P. and M.C.; Writing--review & editing, T.P.R. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Indoor AR Positioning Techniques
The experiment utilized a program from the ImageDetection library within ARKit [35]. This program, upon successful detection, returns values corresponding to the distance between the observation point and the image's center, as well as the angle relative to the normal vector. Instances where these return values were unobtainable are designated as \"unrecognized\". Table 1 presents the results of the image-tracking experiment conducted under the above conditions. When the marker board measures approximately 50 cm by 50 cm, and the camera's position is within an effective angle of 45\({}^{\circ}\) relative to the marker board's normal vectors and at a distance ranging from 30 cm to 75 cm, the accuracy of the obtained angle and distance measurements can be maintained under 1.0\({}^{\circ}\) and 1.0 cm, respectively. The angle and distance measurements allow for the determination of the current position from local coordinate values based on the marker. Subsequently, the local coordinates are transformed into global coordinate values with the marker board's image size, normal vector, and coordinates, which are retrieved from the database containing global location reference markers (Figure 1, A4). Ultimately, the current position is visualized as the initial position on the floor map displayed on the smartphone's screen (Figure 1, B1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **0\({}^{\circ}\) (Front)** & **15\({}^{\circ}\)** & **30\({}^{\circ}\)** & **45\({}^{\circ}\)** & **60\({}^{\circ}\)** \\ \hline
15 cm & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
30 cm & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\times\) \\
45 cm & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\times\) \\
60 cm & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigtriangle\) & \(\times\) \\
75 cm & \(\bigcirc\) & \(\bigcirc\) & \(\bigtriangle\) & \(\times\) & \(\times\) \\
90 cm & \(\bigtriangle\) & \(\bigtriangle\) & \(\bigtriangle\) & \(\times\) & \(\times\) \\
105 cm & \(\bigtriangle\) & \(\bigtriangle\) & \(\times\) & \(\times\) & \(\times\) \\
120 cm & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular} \({}^{\circ}\): Recognizable; errors are less than 1 cm and 1\({}^{\circ}\). \(\bigtriangle\): Recognizable; errors are more than 1 cm or 1\({}^{\circ}\). \(\times\): Unrecognizable.  
\end{table} 
Table 1: Performance evaluation of smartphone position and angle measurement using the smartphone’s camera.
## 3 Results
### Correlations between Carbon Fluxes and Environmental Factors
Understanding these interactions is crucial for comprehending the impacts of environmental change on ecosystems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trend** & **Site** & **GPP\(\leftrightarrow\)NEP** & **GPP Growth Rate (\%)** & **NEP Growth Rate (\%)** \\ \hline \multirow{8}{*}{Consistent and significant trends} & FI-Hyy & 0.79 \(\ast\) & 7.7 & 4.1 \\  & DE-Tha & 0.77 \(\ast\) & 2.0 & 4.2 \\  & US-GLE & 0.67 \(\ast\) & 4.3 & 1.5 \\  & CZ-BK1 & 0.51 \(\ast\) & 3.6 & 2.0 \\  & US-NR1 & 0.51 \(\ast\) & 0.3 & 1.1 \\  & CA-TP3 & 0.49 \(\ast\) & 4.3 & 6.2 \\ \hline \multirow{8}{*}{Consistent but not significant trends} & DE-Obe & 0.47 & 0.02 & 1.1 \\  & FI-Let & 0.39 & \(-\)0.03 & \(-\)2.8 \\ \cline{1-1}  & IT-Ren & 0.36 & 3.8 & 2.7 \\ \cline{1-1}  & US-Ho2 & 0.26 & 1.8 & 0.2 \\ \cline{1-1}  & FI-Sod & 0.25 & 0.2 & 3.6 \\ \cline{1-1}  & CA-Man & 0.23 & 5.3 & 0.2 \\ \cline{1-1}  & RU-Fyo & 0.19 & 3.4 & 1.3 \\ \hline Decoupled & CA-Obs & \(-\)0.25 & 2.0 & \(-\)4.9 \\ \hline \hline \end{tabular}  
* Trend at 1% significance level or 99% confidence level.
* Trend at 5% significance level or 95% confidence level.  
\end{table} 
Table 2: Correlations between annual GPP and NEP, and average annual growth rates. The trends in GPP and NEP were significantly positively correlated in this forest ecosystem when the following four conditions were satisfied at the same time: (1) \(\mathrm{P_{MAM}}\) was significantly negatively correlated with \(\mathrm{SW_{MAM}}\) (\(\rho<-0.5\), \(p<0.05\)) (Table 4); (2) \(\mathrm{P_{MAM}}\) was also significantly negatively correlated with \(\mathrm{VPD_{MAM}}\) (\(\rho<-0.5\), \(p<0.05\)) (Table 4); (3) \(\mathrm{TA_{JJA}}\) was significantly positively correlated with \(\mathrm{VPD_{JJA}}\) (\(\rho>0.7\), \(p<0.05\)) (Table 5); and (4) \(\mathrm{SW_{SON}}\) was significantly positively correlated with \(\mathrm{VPD_{SON}}\) (\(\rho>0.7\), \(p<0.05\)) (Table 6).
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 3 Materials and Method
### Feature Extraction  
In this study, we extracted Mel-frequency cepstral coefficient (MFCC) features and pitch features from raw audios. A total of 141 values were extracted, and Table 1 describes their details.  
1. Pitch features: Pitch features are important elements extracted from speech signals that provide information about the fundamental frequency (F0) and tonal characteristics of the human voice. They are extracted using autocorrelation, cepstral analysis, andwavelet transform. By analyzing these features, SER systems can better understand and interpret the emotional nuances and linguistic characteristics of spoken language; MFCC features: MFCC features capture the essential characteristics of speech signals, and ignore redundant or less important information. They mimic how human auditory systems process sounds by converting their frequency spectrum into a representation that's easier for computers to understand.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 3 Traditional PSSS-Based Time Synchronization Algorithm
### PSSS-Based Time Domain Block Cross-Correlation Algorithm  
Due to the poor performance of the cross-correlation algorithm in a large frequency offset scenario, the literature [23] proposes the block cross-correlation algorithm, which reduces the length of the effective sliding window by dividing the received signal and the local pilot into several blocks to reduce the cumulative effect of frequency deviation. Figure 5 shows the algorithm flow. In the figure, \({}^{\star\star\star}\) represents the conjugate operation and \(r\)' is the conjugate of the received signal, \(p\) represents the local pilot, and \(M\) is the number of blocks for the received signal and the local pilot. Each sequence is operated separately, and the modulus squared is superimposed.  
According to Figure 5, the cross  
By reducing the cumulative effect offset frequency offset, the block cross-correlation algorithm can effectively resist the huge frequency offset. And in theory, the larger the number of blocks, the better the anti-frequency offset performance. However, as the number of blocks increases, the noise is also superimposed, resulting in poor performance of the algorithm under the condition of a low SNR. Consequently, the relative height of the correlation peaks decreases, and peak misalignment appears.
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 4 Experiments and Computational Results
### Comparative Analysis of EMU Utilization Under Fixed and Flexible Routing Plans  
To demonstrate the practical relevance and effectiveness of the proposed multi-day flexible EMU routing optimization model, a case study was conducted on the Beijing-Zhangjiakou high-speed railway network. The input data consisted of 130 scheduled train services on weekdays and 140 on weekends. Two planning scenarios were defined for comparison:  
* **Scenario 1**: A fixed routing plan with constrained EMU assignments;
* **Scenario 2**: A flexible routing plan based on the proposed optimization method.  
Analysis of EMU Utilization  
Figure 9 illustrates the Gantt chart for the fixed routing scenario (Scenario 1), while Figure 10 presents the results under the flexible routing scenario (Scenario 2). Both scenarios were applied to the same six-day high-speed railway timetable covering weekday and weekend services.  
Figure 8: Network structure.  
Under Scenario 1, a total of 43 EMUs were required to complete all train operations. This included 22 high-speed CR400 BF and 21 medium-speed CRH3A units.  
In contrast, Scenario 2 accomplished the same operational tasks using only 42 EMUs, comprising 22 CR400 BF and 20 CRH3A units. This represents a net reduction of one CRH3A unit while maintaining full timetable coverage, thereby demonstrating the proposed model's effectiveness in reducing the required fleet size and operational costs.  
To address this issue, we compared the fixed and flexible multi-day circulation plans in terms of operational performance across different EMU types. As shown in Table 5, we analyzed several key indicators including average cumulative mileage, average deadheading mileage, and the average number of first-level maintenance cycles per EMU over the six-day horizon.  
Under the flexible circulation plan, both CRH3A and CR400 BF EMUs exhibited higher average cumulative mileage compared to those in the fixed plan. Moreover, the average deadheading mileage was significantly reduced by 14.8% for CRH3A units and 16.4% for CR400 BF units. This improvement is primarily attributed to better depot allocation flexibility and the more balanced distribution of inspection tasks.  
Figure 10: The Gantt chart for the flexible routing plan.  
Figure 9: The Gantt chart for the fixed routing plan.  
Additionally, under the flexible plan, the maintenance assignments were more evenly distributed among EMUs, indicating higher resource utilization and better alignment of maintenance cycles with actual train usage patterns. This balanced distribution helps improve operational realism and robustness, ensuring that maintenance schedules match operational demands. Overall, these results confirm that the proposed model effectively reduces the required number of EMUs and non-revenue mileage while enhancing fleet utilization, operational robustness, and overall efficiency in large-scale, real-world high-speed railway networks.
## 4 Experiment and Discussion
### Quantitative Analysis
#### 4.4.1 Undocumented Instruction Mining Time Analysis  
Measuring the mining time metrics of our tools is essential because we need to confirm that our method implementation is feasible in time. For instance, brute-force mining of undocumented instructions, although theoretically feasible, cannot be applied to practical scenarios due to the excessive time cost. Hence, time is an important metric for the algorithm.  
Figure 6 shows the number of mining instructions changing with the running time of DSPUIM on three experimental DSPs: TMS320C6748@456 MHz, TMS320 DM6437@594 MHz, and TMS320C6678@1000 MHz. As can be seen in Figure 6, the slopes of the three curves varied little during the search. The reason was that the main frequency of the DSP was fixed, and the number of instructions tested per unit of time was also fixed. Therefore, we can conclude that the tool mining time \(T_{m}\) for the same model of DSP chip was positively related to the number of search instructions \(C_{I}\), as shown in Formula (4). \(E\) represents the error parameter, and \(\alpha\) is a proportional coefficient that is affected by parameters such as the test code and the main frequency of the DSP processor. Furthermore, by conducting more than five undocumented instruction mining experiments on each of the 14 DSP chips, it was found that the DSPUIM tool will mine the undocumented instructions of various DSP processor models in less than five minutes.  
\[T_{m}=\alpha C_{I}+E \tag{4}\]  
Figure 6: Count of instructions to be tested and mining time of DSPUIM.
# Effect of Thermal Boundary Condition on Tilting Pad Journal Bearing Behavior
## 3 Simulation Results and Discussion
### Bearing Static and Dynamic Behavior
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model No.** & **Rotor Spin Speed (rpm)** & **T\({}_{\text{Pad}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{Journal}}\) (\({}^{\circ}\)C)** & **T\({}_{\text{Lube}}\) (\({}^{\circ}\)C)** & **Line Style** \\ \hline
1 & 1000 & \(0,20,40,60,80\) & 50 & 50 & \\
2 & 3600 & \(0,20,40,60,80\) & 50 & 50 & \\
3 & 1000 & 50 & \(0,20,40,60,80\) & 50 & \\
4 & 3600 & 50 & \(0,20,40,60,80\) & 50 & \\
5 & 1000 & 50 & \(50\) & \(0,20,40,60,80\) & \\
6 & 3600 & 50 & 50 & \(0,20,40,60,80\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Running conditions. \(\text{T}_{\text{Lube}}=80\)\({}^{\circ}\text{C}\) at 3600 rpm, the bearing is softened by the increased lubricant supply temperature. In this case, the opposite scenario is possible--that the increased lubricant temperature decreases the fluid viscosity, leading to the decreased bearing stiffness. Unlike the bearing stiffness coefficient, the damping coefficient tends to be complex as shown in Figure 10c,d. When the boundary temperature of the journal is increased, the damping coefficient increases in most cases. However, an increase in the pad boundary temperature decreases the damping coefficient in most cases. In particular, when the pad boundary temperature (\(\text{T}_{\text{Pad}}\)) is 80 \({}^{\circ}\text{C}\), the reduction rate is remarkable. The change of the damping coefficient according to the lubricant supply temperature (\(\text{T}_{\text{Lube}}\)) shows different patterns depending on the direction (\(x\) or \(y\)). The damping coefficient in the horizontal direction (\(C_{xx}\)) increases with the increase in the supply temperature of the lubricant, and then decreases significantly when the supply temperature becomes 80 \({}^{\circ}\text{C}\). However, it can be seen that the damping coefficient in the vertical direction (\(C_{yy}\)) shows the opposite tendency depending on the journal spin speed. Figure 9: Bearing static characteristics: (**a**) eccentricity ratio; (**b**) attitude angle; (**c**) tilt angle of pad no.5; (**d**) non-dimensional pivot deformation.
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.3 Safety and Efficiency
The anticipated approaches outperformed base cases with 10-450% boosts and 0-26% declines when considering various performance metrics. Most tests employed a single intersection with simplified traffic circumstances to validate the suggested methodologies.
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 5 Our Presented HM-ACOPSO Algorithm
### Communication Range Adjustment  
During the initial round, sensors own the same initial energy, and the network is energy-balanced. As the number of rounds increases, the network becomes energy-heterogeneous due to the uneven deployment and heavy burden of forwarding. The clustering addresses the problem of energy unbalancing within the cluster, whereas, the unbalanced energy of the inter-cluster is still unsolved. In this paper, we adopt the method of sensor communication range adjustment to handle the unbalanced energy between different clusters. The CHs commonly consumes the most energy during the transmission with the mobile agent, and we only consider adjusting the communication range of the CHs. The communication range of each CHs is dynamic and is changed according to Formula (18).  
\[R_{CH_{i}}=(1-c\frac{E_{Max}-E_{CH_{i}}+\varepsilon}{E_{Max}-E_{Min}+ \varepsilon})R \tag{18}\]  
where \(E_{Max}\) denotes the maximal residual energy of CHs and \(E_{Min}\) denotes the minimal residual energy of CHs. \(c\) is a regulatory factor between 0 and 1, and it usually is set as 0.5. \(\varepsilon\) is a very small constant to avoid the denominator to be zero. The sample after communication range adjustment is shown as Figure 8.