esa-sceva/satcom-chunk-collection huggingface dataset:
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 4 Results
### Heuristic Methods Benchmark Results
#### 4.3.2 Fitness Function Results  
The following table presents a statistical analysis of the \(FF_{\text{Optimized}}\) outcomes, illustrated using a standardized boxplot, using the six-number summary, which includes the minimum, maximum, average, median, and the first and third quartiles.  
The respective boxplot charts are presented in Figure 10.  
As a simple visual analysis of \(FF_{\text{Optimized}}\), the average comparison plot is presented in Figure 11 for the tested methods, illustrating that the Cultural Algorithm (CA) method achieves the best performance, while the Alienor method demonstrates the poorest performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Min**} & \multicolumn{2}{c}{**First**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Median**} & \multicolumn{2}{c}{**Third**} & \multirow{2}{*}{**Max**} \\  & & **Quartile** & & & & **Quartile** & \\ \hline EA & 18,726 & 23,724 & 29,066 & 27,695 & 32,871 & 51,851 \\ ES & 5162 & 6232 & 6662 & 6645 & 6757 & 10,187 \\ GA & 3308 & 3657 & 4366 & 4052 & 4883 & 7361 \\ MLR & 9398 & 10,814 & 11,508 & 11,712 & 12,243 & 14,056 \\ SA & 42,995 & 65,592 & 130,000 & 115,482 & 166,155 & 314,441 \\ TS & 1670 & 4512 & 10,621 & 10,380 & 16,517 & 27,323 \\ Alienor & 357,668 & 409,377 & 421,568 & 425,558 & 436,008 & 463,916 \\ Noising & 6806 & 10,068 & 11,532 & 11,458 & 12,334 & 19,368 \\ CA & 2461 & 3238 & 3563 & 3517 & 3932 & 4554 \\ PSO & 6189 & 6337 & 6658 & 6547 & 6663 & 9262 \\ ACU & 6524 & 7082 & 8470 & 7497 & 9071 & 17,983 \\ GRASP & 62,661 & 64,961 & 66,187 & 65,806 & 66,697 & 74,723 \\ ACO & 7777 & 10,593 & 27,902 & 16,559 & 35,533 & 106,079 \\ DE & 7225 & 11,304 & 16,997 & 14,220 & 23,328 & 34,641 \\ AIS & 2927 & 3981 & 4799 & 4504 & 5403 & 8427 \\ HS & 11,673 & 14,714 & 17,576 & 16,398 & 18,233 & 35,853 \\ EDA & 12,510 & 13,492 & 14,750 & 13,816 & 14,996 & 22,981 \\ CE & 6097 & 6485 & 7315 & 6992 & 7737 & 11,201 \\ ABC & 7589 & 8647 & 10,551 & 10,609 & 12,609 & 13,858 \\ CS & 9536 & 14,487 & 16,654 & 17,313 & 18,121 & 26,563 \\ APSO & 6365 & 6517 & 7326 & 6847 & 7337 & 12,862 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Heuristic method boxplot statistical elements: fitness function \(FF_{\text{Optimized}}\).  
Figure 10: Boxplot chart—fitness function \(FF_{\text{Optimized}}\).
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 4 System Analysis and Simulation Results
### Constellation Comparison  
In order to more intuitively display the function of the frequency offset estimation module, we simulated the constellation of the demodulated signal. The constellation diagram without the frequency offset estimation module is shown in Figure 8a. Since FFO destroys the orthogonality between subcarriers, we can observe that FFO caused ICI and rotation of constellation. More seriously, the rotation of the constellation leads directly to a decision error in demapping. Thus, we also simulated the BER of the corresponding constellation. The numerical results show that the BER of Figure 8a  
Figure 6: Error rate curve with low resolution.  
Figure 7: Mean-square error of frequency offset estimation.  
was 0.6615 when the system did not perform carrier synchronization. Through the above analysis, it can be concluded that the performance loss caused by the CFO to the OFDM system was very huge. When a frequency offset estimation module with 3-bit ADCs was added to the system, the constellation diagram was as shown in Figure 8.  
In Figure 8, we can clearly see that the constellation points were well distributed in the polar coordinate system compared to Figure 8. Due to the addition of the frequency offset estimation module, the system could appropriately overcome the performance degradation caused by CFO. Therefore, from the comparison of the simulation results in Figure 8, we illustrate that it is feasible to perform frequency offset estimation at low resolution.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 2 Related Work
### The Target Node Positioning Algorithm  
The target node positioning algorithm can be built in several ways. A triangle centroid positioning (TCP) algorithm based on the distance or relative angle information between the target node and the anchor node has been proposed in the existing documents [21; 22; 23]. Since the node distribution characteristics are not fully considered, the problems in the TCP algorithm are twofold: the target node deviating active locating area and the significant positioning error. The weighted centroid positioning (WCP) algorithm has been presented in the existing literature [24; 25; 26; 27] by introducing a weight factor, which is related to the distance estimation. Nevertheless, the positioning error will be significantly enlarged if there is a significant deviation in the distance estimating process of the WCP algorithm. A fingerprint database positioning algorithm has been constructed to improve the positioning accuracy in the existing literature [28; 29; 30] by collecting the positioning samples in advance. The positioning algorithm highly relies on fingerprint database data and the positioning accuracy will become weak if the environment changes. A positioning algorithm based on the neural network has been put forward in the literature where [31; 32] the input of the algorithm is the value of RSSI and the output is the distance between nodes. Due to simple learning rules, the output of the neural network cannot be correct when the data is not sufficient. A positioning algorithm based on Bounding-Box has been proposed in the existing literature [33; 34]. The number of given anchor nodes determines the positioning accuracy of the algorithm and the positioning accuracy is not high if the number of anchor nodes is not enough.  
Aiming to address the above problems of low positioning accuracy, several experts have presented the solution of cooperative positioning. A cooperative positioning method fusing inertial-measurement unit and UWB ranging measurement is presented in [35] and a cooperative positioning method combining the inertial-measurement unit with long-range WiFi RSS and short-range UWB ranging measurements is proposed in [36] which significantly improves positioning accuracy. However, cooperative positioning methods need additional equipment. For positioning accuracy and low cost, an iterative positioning algorithm based on distance correction is proposed through correcting the estimated distance between the given anchor node and the target node and constraining the target node in the sub-triangular positioning area of the iterative positioning model in this paper. The algorithm calculates the target node coordinates iteratively and improves positioning accuracy effectively.
## 3 Results and Discussion
### Prediction Results of SVR  
The \(\xi\)--SVR model using a Gaussian radial basis function (RBF) kernel was established in this paper with parameters of penalty factor \(C\), kernel function parameter \(g\), and parameter \(\xi\) controlling the number of support vectors. \(C\) and \(g\) greatly affect the accuracy of the SVR model for coal quantitative analysis. The size of \(C\) represents the degree of punishment to the sample when exceeding the empirical error. The larger the value \(C\) is, the higher the degree of punishment will be. However, if the value of \(C\) is too large, the overfitting state will occur. Whereas the too small value of \(C\) will lead to the underfitting state. The size of \(g\) determines the feature space of kernel function and can be used to characterize the complexity of the subspace distribution of sample data. If the value of \(g\) is too small, the model will be relatively complex, and the generalization ability cannot be guaranteed. If \(g\) is too large, the accuracy of the model is low.  
Among the optimization algorithms of \(C\) and \(g\), the particle swarm optimization algorithm (PSO) does not need any derivative operation on the solution function and can overcome the non-convergence problem of the traditional optimization algorithms due to the ill condition or singular gradient matrix. Compared with other algorithms, a particle swarm optimization algorithm retains the global search strategy for population, and its unique memory enables it to dynamically track the current search situation and adjust its search strategy. Since the velocity-displacement model adopted by the particle swarm optimization algorithm is simple to operate, it is used in this paper to optimize the parameters of the SVR model [30; 31]. The initial parameters of the particle swarm optimization algorithm were set as \(\mathrm{c_{1}}=1.5\), \(\mathrm{c_{2}}=1.7\), particle population as 20 and iteration time as 100. Based on the input variables and the optimal model parameters sought by the particle swarm optimization algorithm, the quantitative SVR model of ash content, volatile matter and calorific value under different data set partitioning methods was established. The results are shown in Figure 3 and Table 4.  
Figure 3: The prediction results of ash content, volatile matter and calorific value of coal by SVR based on (**a1–a3**) RS; (**b1–b3**) KS; (**c1–c3**) SPX.  
It can be seen from Figure 3 and Table 4 that the prediction results of the SVR model with KS and SPXY partitioning methods are better than those with the RS method. The \(R^{2}\) of the SVR model based on the SPXY partitioning method of ash content is 0.9955, _RMSE_ is 2.1142, and _MRE_ is 9.82%. The \(R^{2}\) of the volatile matter is 0.9999, _RMSE_ is 1.0895 and _MRE_ is 2.19%, The \(R^{2}\) of the calorific value is 0.9999, _RMSE_ is 1.1252 and _MRE_ is 3.87%.
## 2 Materials and Methods
### Toxicity Tests
#### 2.3.5 Paramecium caudatum  
To assess the sublethal toxicity of the four BCs to _Paramecium caudatum_, we performed the protocol of [PERSON] et al. [51], with slight modifications. Briefly, this test starts with the placement of five _P. caudatum_ organisms in a well of a 24-well polystyrene plate, with a negative control (EPA medium) and five dilutions of the biochar elutriate (100%, 50%, 25%, 12.5%, and 6.25%) in a final volume of 2 mL. We add Sonneborn medium [46] at 1 g L\({}^{-1}\) at the start of the test. Then, the plate was placed in a bioclimatic chamber with a 16:8 light:darkness cycle at 25 \({}^{\circ}\)C for 96 h. At the end of the 96 h exposuretime the total number of organisms was counted in each well to obtain the percentage of inhibition of the population applying the following formula:  
\[\%\mathrm{I}=\frac{Nc-Nt}{Nc}\star 100 \tag{1}\]  
with:  
\(N=\) total number of _P. caudatum_ organisms alive after 96-h  
\(t=\) treatment  
\(c=\) control
## 5 Simulations and Discussions
### MSE Evaluations  
Considering the bias phenomenon, the performance of the proposed algorithm is evaluated using the MSE criterion (Equation (25)). We simulate the scenario with different bandwidths and different fractional delays (Table 1) to obtain the following results, as shown in Figure 5. It should be noted that in this scenario, a variable fractional delay filter (VFD) is implemented, and its delay accuracy will also have an impact on the simulation system. The VFD parameters used in this simulation are: normalized passband frequency \(0.9\tau\), time-delay accuracy \(\leq 2\times 10^{-7}(/T)\), and frequency response ripple \(\leq 0.2\) dB. From the simulation, we can see that the MSE of the algorithm has a CRLB error of \(\leq\)1 dB when the SNR is \(\geq\)15 dB.  
Figure 4: \(var(\tau_{0})\) simulations with various bandwidths and time delays. (**a**) \(BT=1/2,N=16\),384, \(P=11,\theta=1\); (**b**) \(BT=1/4,N=16\),384, \(P=11,\theta=1\).
###### Abstract  
In this study, the temperature distribution of the Methane-Air premixed flame was measured. In order to enhance the measurement accuracy of the CT-TDLAS (Computed tomography-tunable diode laser absorption spectroscopy), the SMART (simultaneous multiplicative algebraic reconstruction technique) algorithm has been adopted. Further, the SLOS (summation of line of sight) and the CSLOs (corrective summation of line of sight) methods have been adopted to increase measurement accuracies. It has been verified that the relative error for the temperatures measured by the thermocouples and calculated by the CT-TDLAS was about 10%.  
Methane-Air premixed flame; computed tomography-tunable diode laser absorption spectroscopy; temperature; concentration; burner; exhaust gas Article Measurement Enhancement on Two-Dimensional Temperature Distribution of Methane-Air Premixed Flame Using SMART Algorithm in CT-TDLAS
## 4 Case Study
### Results Analysis
The RF performs better in terms of the error stability, with an MAE and MAPE of 14.0925 and 0.85%, respectively, lower than those of the SVR, thanks to its ensemble learning mechanism's robustness to noise and outliers. The R\({}^{2}\) and RMSE of the SVR are 0.99913 and 28.6886, respectively, slightly better than those of the RF, indicating a strong ability to fit the overall trend of the data. However, its MAE is significantly higher, indicating that there are many large error outliers in the prediction results, which may be related to the sensitivity of the kernel function to high-dimensional features and overfitting to local extrema. The prediction results of both methods are inferior to those of the WACNN model, reflecting the limitations of traditional methods at feature automatic extraction and complex pattern capture, especially when dealing with high-dimensional or implicit spatial correlation data, where the limitations are more pronounced. Among the compared models, the CNN model and its variants have higher prediction accuracy. Compared to models, such as the LSTM, it has significant improvements, especially for basic statistical models like PLS. Although the improvement of the WACNN is relatively small compared to the CNN, at ports with high container throughput, even a small improvement can have a significant impact due to its large base. On the other hand, even though the CNN model achieves high prediction accuracy, the WACNN can still achieve performance breakthroughs through weight allocation, demonstrating the potential space for model design.
## 3 Materials and Methods  
### Flowchart of the Dual-Driven Simulation Model  
The flowchart of the proposed DDSM is presented in Figure 1. The initial data needed for the model are the demand and supply of the transportation network. The transportation demand data consist of trip chains from LBS data, and the actual traffic flow is obtained via extracting the travel chain information. The supply data of the transportation network includes the road network topology and public transportation hubs, which together constitute the facility foundation for transportation operations. Given the initial data, the development of DDSM includes three major steps: model initialization, model calibration, and LBS missing path completion. Model initialization is a preparatory process that begins with selecting the study area, followed by filtering and processing the aforementioned raw data according to the spatial-temporal coverage, ultimately completing model construction. Model calibration is the process of enhancing the credibility and predictive capability of simulation models using LBS data. The calibration goal is to align the outputs of simulation models as closely as possible with observed data, primarily through supply calibration (identifying and calibrating the incorrect values of road speed limits and capacities) and demand adjustment (selecting key road segments for demand adjustment based on the error between actual and simulated traffic conditions, and subsequently scaling the OD demand of passing these road segments) to minimize the discrepancies between simulated and observed traffic attributes. This process helps to improve the fitting degree of the model to the actual traffic conditions. A completion operation is performed to address the missing path issue in the LBS data. Specifically, the calibrated model is used to generate optional driving paths for LBS trips with missing paths. The reliability of each path is then evaluated based on relevant indicators such as travel time, and the best path is selected for completion. After completion, the differences between simulated and actual traffic attributes are reassessed. If the path completion deteriorates the calibration results (with the new error exceeding a preset threshold), the calibration process is revisited, and demand adjustment is performed again. Finally, a performance check is conducted on the completed data to analyze the accuracy and effectiveness of the completion results.
## 6 Performance Evaluations
### Redirect Always Algorithm (R4)  
In this variation of the routing protocol, the DRN source node sends its DRN data message to its parent DDN. While the message is being forwarded through the intermediate DRNs, if it reaches a broken link then the following steps are taken. The DRN determines if this data message has already been redirected. This is determined by checking the redirected flag that resides in the message. If the redirected flag is already set then the message is dropped and a negative acknowledgement is sent back to the source. Otherwise, the source can be informed of the redirection process by sending a short redirection message with the redirected message ID back to the source. The source will then re-send the data message in the opposite direction and update its database with the fact that this direction to reach the DDN is not functional. Furthermore, in order to make the protocol more efficient the entire data message is not sent back to the source since the source already has a copy of the data message.  
Only a short redirection message with the redirected message ID is sufficient to be sent back to the source. Additionally, the redirection message also informs the other nodes on that side that there is a \"dead end\" in this direction and data needs to be transmitted in the other direction even if the number of hops to reach the other nearest DRN is larger. In that case, each DRN that receives this message will check the redirected flag, and if it is set, then it will continue to forward the message in the same direction. However, in order to prevent looping, if another broken link is encountered in the opposite direction the redirected message cannot be redirected again. In that case, the message is simply dropped.
## 7 Conclusions  
This paper presented a low-power CMOS receiver with a complex continuous-time delta-sigma ADC optimized for IoT applications in the 2.4 GHz band. The proposed design incorporates a quadrature bandpass continuous-time delta-sigma ADC that achieves an ENOB of 10.9 bits while consuming only 0.81 mW from a 1.0 V supply. The ADC successfully provides a BW of 2 MHz while operating at a 32 MHz sampling frequency, making it ideally suited for BLE applications. The receiver architecture demonstrates several significant performance achievements through its innovative features. The carefully optimized low-IF architecture operating at 1.5 MHz IF delivers strong image rejection capability with a measured IRR of 54.2 dBc. The design also achieves an excellent SFDR of 79.8 dBc, demonstrating its robust signal-handling capabilities. Most notably, the architecture achieves superior energy efficiency with an \(\mathrm{FOM}_{\mathrm{W}}\) of 103.2 fJ/conv, representing the best figure among current state-of-the-art implementations. The complete receiver system demonstrates an impressive sensitivity of \(-95\) dBm at a \(10^{3}\)-bit error rate while maintaining low power consumption of 4.08 mW, with the ADC portion requiring only 0.81 mW. The design implementation in 28 nm CMOS technology achieves a compact form factor, occupying just 0.375 mm\({}^{2}\) for the RF front-end and 0.145 mm\({}^{2}\) for the ADC. These specifications demonstrate that the proposed architecture successfully addresses the demanding requirements of modern IoT applications, particularly for BLE standards, by delivering an optimal balance between power efficiency, performance, and integration density. The achieved specifications make this design particularly well suited for battery-powered IoT sensor nodes where both power efficiency and reliable wireless connectivity are critical requirements. The demonstrated architecture provides a robust foundation for advancing the development of next-generation low-power wireless sensor networks.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Extension of the Improved Upper-Bound Pushover Analysis Procedure (EIUB)  
This approach was developed by [64] to accurately predict the behavior of both ordinary and tall structures. The extended IUB examines the seismic response of irregular structures with setbacks. This procedure aims at expanding the improved upper-bound (IUB) analysis to account for the influence of setbacks on the seismic response of mid-rise structures. In addition to the first two modes of vibration utilized in the IUB approach [63],a third mode is used to construct the applied load vector, which may portray the irregularity characteristic. The proposed expanded IUB version was applied to fifteen 10-story structures with varying setback configurations, as well as a reference structure. This new version's performance was assessed in terms of target displacement, story displacements, story drift, and plastic hinge rotations, which were compared to those produced from prior pushover processes using nonlinear time history analysis (NTHA) as a benchmark.  
The extension of the improved upper-bound pushover analysis procedure involves the following steps, summarized:  
1. Conduct an eigenvalue analysis. Conduct an eigenvalue analysis to compute the natural frequencies of the structure, \(\omega n\), and the mode shapes, \(\Phi n\), such that the lateral component of \(\Phi n\) at the roof equals unity.
2. Determine the upper-bound of mode contributions. Using the elastic response spectrum of the specified earthquake data, determine the upper-bound of the contribution of the 2 nd and 3 rd modes \(q_{i}/q_{1}\), as described by the following relationship: \[f^{\prime}=0.48q_{1}\bigg{[}\bigg{(}\omega_{1}^{2}M\varphi_{1}\bigg{)}\pm \frac{0.26}{0.48}\bigg{(}\omega_{1}^{2}M\varphi_{2}\frac{q_{2}}{q_{1}}\bigg{)} \pm\frac{0.26}{0.48}\bigg{(}\omega_{3}^{2}M\varphi_{3}\frac{q_{3}}{q_{1}} \bigg{)}\bigg{]},\] (35)
3. Calculate the lateral load distribution vector. Calculate the lateral load distribution vector throughout the building's height using the following relationship: \[f_{i,EIUB}=\max\Big{(}f_{i,LIB}^{\prime}f_{i,Until}\Big{)},\] (36)
4. Calculate the target roof displacement. Calculate the desired roof displacement \(Ur\) by applying either one of the following relationships: \[U_{r}=U_{r\text{MI}}\bigg{(}1+\frac{q_{2}}{q_{1}}C_{r}\bigg{)},\] (37) \[U_{r}=U_{r\text{MI}}\bigg{(}1+\frac{q_{2}}{q_{1}}C_{r}+\frac{q_{3}}{q_{1}}C_{ r}\bigg{)},\] (38)
5. Perform a pushover analysis. Perform a pushover analysis using the lateral load estimated in Step 3 until the target displacement calculated in Step 4 is reached (single-run analysis).
6. Determine the seismic responses. Determine the maximum values of the seismic responses from the single-run analysis conducted in step 5.