esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 5 Results and Analysis
### Results for Different Carrier Frequencies Using GWO  
Table 8 provides a comprehensive overview of the results obtained from different 5G Multi-Tier RAN planning scenarios using the Grey Wolf Optimizer. The GWO optimization process begins with an initial configuration featuring 115 base stations (BSs) for the 28 GHz spectrum and 147 for the 3.6 GHz spectrum. Initial coverage values show promise, with 0.8993 for 28 GHz and a perfect 1 for 3.6 GHz, while initial capacity values exhibit efficiency with 0.868 for 28 GHz and 0.927 for 3.6 GHz. Following the GWO optimization, the final configuration maintains the BS count but noteworthy enhancements are observed. With 115 BSs for 28 GHz and 147 for 3.6 GHz, coverage sees a significant boost to 0.9444 for 28 GHz while remaining at 1 for 3.6 GHz. Capacities witness an improvement as well, reaching 0.9375 for 28 GHz and 0.8194 for 3.6 GHz. After the elimination of specific BSs, the final configuration showcases a reduction in the number of BSs to 65 for 28 GHz and 115 for 3.6 GHz. Although the coverage for 28 GHz slightly decreases to 0.9201, the coverage for 3.6 GHz maintains a high level at 0.993. Capacities experience a decrease, reaching 0.8576 for 28 GHz and 0.7777 for 3.6 GHz. The GWO optimization process proves effective in refining the deployment of 5G networks, demonstrating a strategic reduction in the number of base stations alongside some trade-offs in coverage and capacity. Also, the stochastic nature of the GWO and other metaheuristic algorithms can indeed lead to varied results, which can influence the observed performance in reducing the number of deployed base stations.  
Figure 9a showcases the results of the GWO on 5G network deployment at 28 GHz post-base station elimination. Figure 9a displays the reduced base station count (65) achieved through GWO optimization. Figure 9b features a Voronoi Plot illustrating the coverage areas of the remaining base stations, emphasizing the optimized spatial distribution achieved by GWO. This visual representation highlights the algorithm's effectiveness in refining the 28 GHz spectrum deployment for improved coverage and capacity.
## 5 Discussion  
### Results Analysis  
This section analyzes the experimental results to evaluate the proposed method. Figures 7 and 8 demonstrate that the proposed approach significantly outperforms the conventional bowstring-only method in accurately measuring shooting time. The bowstring-only method overestimated the shooting time by an average of approximately 1.4 s across 58 video clips. This discrepancy arises because the bowstring-only method begins measuring shooting time as soon as the bow enters the athlete's facial bounding box during the drawing phase, even before the anchoring phase begins. This issue is particularly pronounced for athletes with longer drawing phases, leading to substantial errors in the measurement process. To address these limitations, the proposed method integrates pose estimation, utilizing skeletal motion to accurately identify the start of the anchoring phase. The bowstring is then detected, and shooting time is measured precisely from this identified anchoring start point. As shown in Figure 6, the proposed method successfully detects the beginning of the anchoring phase, avoiding errors caused by prematurely measuring shooting time during the drawing phases. To quantitatively validate the results, a paired t-test was conducted to evaluate the statistical significance of the performance differences  
Figure 8: Scatter plot with a regression line showing the relationship between the measured shooting time using only the bowstring and the actual shooting time, and the \(R^{2}\) score indicating the accuracy of the model.  
Figure 7: Scatter plot with regression line showing the relationship between measured shooting times using the proposed method and actual shooting times, along with the \(R^{2}\) score indicating the model’s accuracy.  
between the proposed method and the bowstring-only method. The test produced a \(p\)-value below 0.05 and close to 0, confirming that the performance differences between the two methods are statistically significant.
# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 6 Discussion  
The results obtained from the experiments show how the DDPG and DQN algorithms exhibit adequate decision making in reaching the targets in environments with and without obstacles. This finding is reflected through an analysis of metrics and variables such as trajectory, angular velocity and time. When comparing the results with the control algorithms, it is shown that DDPG and DQN obtain shorter arrival times at the destination point, and it is possible to dominate environments with and without obstacles.  
Reinforcement learning agents can obtain a route point by point in real time and only need information about the environment, the robot position and the position of the target point. The position of the obstacle can be random since, in the training process, the position of the robot was changed at every episode. In this way, the agent did not learn the position of the obstacles but learned only to rely on the sensor's data, so when a new obstacle appears, the robot will be able to avoid it.
# PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature
## 2 Materials and Methods
### BP Estimation  
PLS regression is one method that can used to address the multicollinearity issue in multivariate estimation models. Thus, we used PLS regression to develop a BP estimation model based on the LCFs of the SDPPG waveform. In PLS regression, the LCFs are converted into independent (uncorrelated) scores, and the estimation model is developed on the basis of these scores. These scores are linearly independent of each other and calculated to reduce the regression residual. The PLS regression model for estimating \(\hat{\mathbf{Y}}\) is as follows.  
\[\hat{\mathbf{Y}}=\mathbf{X}\mathbf{W} \tag{1}\]  
where \(\hat{\mathbf{Y}}\) is the estimated objective variable vector (SBPs) of size \(N\times 1\) (\(N\) is the sample size), \(\mathbf{X}\) is the centralized explanatory variable matrix (LCFs) of size \(N\times M\) (\(M\) is the number of features), and \(\mathbf{W}\) is a coefficient matrix of size \(M\times 1\).  
In order to assess the accuracy of our method in estimating the SBP, we compared our method (LCFs + PLS) with two similar methods: (1) comparison method 1 (HRFs + MRA) and (2) comparison method 2 (LCFs + MRA). In comparison method 1, we extracted the HRFs from the SDPPG waveforms of the subjects. As mentioned previously, the HRFs cannot be extracted in the absence of peaks. In these cases, we determined the mean value of a feature on the basis of the number of pulses without missing data within a single record. In comparison method 2, we extracted the LCFs from the SDPPG waveforms. We used MRA for both comparison methods to estimate the SBP. The two comparison methods used here are used for the purposes of showing that LCFs are superior to HRFs and showing that the matching of LCFs and PLS is excellent.  
We evaluated the accuracy of these estimation models by comparing the estimated SBP values with those obtained from the BP measurements at the upper arms of the subjects. The accuracy of the models was evaluated on the basis of the correlation coefficient (R value) between the measured and estimated values and the standard error of the estimate. We used the data collected from 180 subjects as the training data for BP estimation models. The testing data used to evaluate the estimation accuracy were collected from 85 subjects, and these data were not used to develop the estimation model.
## 4 Experimental Results
### Noise Reduction Performance
As shown in Figure 8, it can be seen that narrowband noise presents a different noise reduction effect from that of multi-harmonic noise. First, for narrowband noise, the overall noise reduction effect of the system was less than that of multi-harmonic noise, and the maximum noise reduction was approximately 15 dB. This is because the frequency component of the narrowband noise is more complex. Second, the increase in the order of the ANC filter can still reduce the steady-state error of the system. However, the increase in order was different from that in the case of multi-harmonic noise and did not improve the convergence speed. With the increase in order, there were large fluctuations for different frequencies in the noise frequency band. It can be seen from the figure that the power spectral density after noise reduction was relatively uniform and the noise reduction effect on the low-frequency part was much better. Figure 8: Comparison of noise reduction performance when the reference signal is 200–600 Hz narrowband signal. (**a**) The order of the ANC filter is 10; (**b**) the order of the ANC filter is 25; (**c**) the order of the ANC filter is 40; (**d**) the power change in error microphone signal e(n) when the order of ANC filter is different.
## 4 Experimental Evaluation
### Clothing Component Experimental Evaluation
Furthermore, in [43], commercial metallic snap-on buttons have been used as detachable shorting vias for wearable textile antennas. The target is to achieve antenna modularity in terms of radiation characteristics. In the proposed design concept, four male snap-on buttons integrated into the substrate are soldered back to back to serve as antenna patch holders and as shorting via when they are engaged to female buttons onto the ground plane. By selecting the engagement of different male buttons with the female ones, the textile antenna can be shorted at different corners and, thus, operate in different radiation modes. In [18], realistic structural modifications of a button have been examined in terms of simulations and measurements. The implemented modifications can introduce a novel type of dual-band button antenna that enables wireless local area network applications and can be efficiently integrated into clothing. The proposed button design is suitable for on-body and off-body wireless communication. Similar experimental evaluations of a button antenna, in terms of realistic modifications and antenna locations with respect to the cloth, textile cover, and support substrate (integrated onto cloth) are presented in [28]. In [23], a dual-band button antenna has been designed following the structure of a typical jeans button for use in personal area networks (PANs). A point-to-point transmission measurement study has been carried out for various button locations. Possible positions are on the shoulders, in breast pockets, and in jacket buttons to represent realistic wearing conditions.
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 5 Failure Analysis
### Single Event Effects  
By definition, the group of single event effects concerns situations where, at a given time, one functional node (wire, logic gate, memory cell, DFF, etc.) manifests malfunction. The lockstepping technique mitigates such events by design using redundancy. During SEE, only one node in one processor core fails. As a result, the checker module can easily detect mismatches between processor cores and take appropriate actions to prevent further misoperation.
## 6 Performance Evaluation
### Evaluation for the Designed Protocol
#### 6.1.5 Number of Hops for One Constructed Routing Path  
In Figure 5c, we compare the number of hops needed for constructing a routing transmission path between the transceiver. Reasonably, LSGR produces the largest number of hops because the best connectivity property can always be found in the nearby vehicles, which might reduce the one-hop transmission distance. Similarly, RTF shows a closing tendency on the number of hops with LSGR, due to it choosing the next hop relay randomly. As expected, DBF illustrates the least number of hops since it chooses the furthest vehicle in the radio range as the relay. For our proposed LBRP, with the consideration of both speed and position information, the increase of the number of hops compared to DBF is not much. As can be seen from the simulation results, the hop count for LBRP is within one hop greater than that of DBF.  
Figure 5: The influence of vehicular traffic density on four different routing metrics. (**a**) Path construction successful probability; (**b**) One path lifetime; (**c**) Number of hops; (**d**) Transmission delay. DBF, Distance-Based Forwarding; RTF, Random Timer Forwarding; LSGR, Link State-aware Geographic Routing.
## 3 Results  
### Evaluation of Adsorption Efficiency of Tested Adsorbents  
Results shown in Table 4 show that RPNS followed by PNS250 had the best performance, with RPNS being more efficient in terms of %R and \(q_{e}\). The explanation of these findings will be revealed in the following subsections.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Adsorbent Sample** & **\% Removal, \% R \(\pm\) SD * & **Adsorption Capacity, \(q_{e}\) (mg/g) \(\pm\) SD * \\ \hline RPNS & 96.45 \(\pm\) 0.60 & 2.89 \(\pm\) 0.08 \\ PNS250 & 52.13 \(\pm\) 0.79 & 1.56 \(\pm\) 0.09 \\ PNS300 & 30.63 \(\pm\) 0.53 & 0.92 \(\pm\) 0.01 \\ PNS350 & 25.18 \(\pm\) 0.42 & 0.75 \(\pm\) 0.03 \\ PNS400 & 22.46 \(\pm\) 0.51 & 0.67 \(\pm\) 0.04 \\ \hline \hline \end{tabular}  
* SD values are the average of three determinations.  
\end{table}
Table 4: A comparison of the adsorption capabilities of the five as-prepared adsorbents in terms of %R and adsorption capacity (\(q_{e}\)) using adsorbent dose (AD) = 200 mg/50 mL, pH = 9, stirring time (ST) = 30 min, and dye concentration (DC) = 30 ppm. Shown responses were calculated using Equations (1) and (2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{4}{c}{**Coded Levels of Variables**} & \multicolumn{4}{c}{**Response (Y, Adsorption Capacity, \(q_{e}\))**} \\ \cline{2-7}  & **A a** & **B b** & **C c** & **D d** & *** Obs.** & ****Pred.** & *** \%Er \\ \hline
01 & \(-\)1 & +1 & +1 & \(-\)1 & 23.97 & 23.89 & 0.33 \\
02 & 0 & 0 & 0 & 0 & 2
## 3 Results and Analysis
### Data Processing and Analysis
#### 3.2.1 The Static RT PPP with Different Data Processing Methods
The reason why positioning accuracy is high after filter convergence is that the weight factors calculated by the new method is larger than that of the priori variance method, and the contribution of low-accuracy (compared to carrier-phase measurements) RT ionospheric products is small. Table 3 summarizes the RMS of positioning error and convergence time for static single- and dual-frequency GPS/GALILEO RT PPP with three processing methods. The RMS computations are based on the position errors without considering the process of the filter convergence. From the Table 3, we can see that three methods have similar positioning accuracies after filter convergence in the dual-frequency case, but the positioning accuracy of the priori variance method becomes slightly worse than new methods in the single-frequency case. Compared to the other two methods, the proposed approach significantly improves convergence performance in static dual- and single-frequency cases. The convergence time improvement rate of the new method refers to the that of other two methods which are listed in the right column of \"convergence time\", in which the largest improvement rate is 77%. Figure 4: _Cont._  
In order to ensure a reliable value of the window size in the proposed weighting approach, Figure 5 provides the convergence time of dual- and single-frequencies using different window sizes of 1, 5, 10, 15, and 20. As a result, the varying trend of convergence times is quite consistent in dual- and single-frequency cases, the improvement of the convergence time is less significant when the window size is increased from 10 to 20. This suggests that the window size of 10 is suitable, which will be applied for the rest of our data analysis. Figure 4: Static positioning errors (a,b), number of satellites, PDOP (c,d), weight factors (e,f) of dual-frequency RT PPP by station ANRK (a,c,e) and single-frequency RT PPP by station METG (b,d,f). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Methods** & **Convergence Time (min)** & **E (m)** & **N (m)** & **U (m)** \\ \hline \multirow{3}{*}{Dual-frequency} & Without constraint & 116 (77\%) & 0.0033 & 0.0038 & 0.0346 \\  & A priori variance & 50 (48\%) & 0.0030 & 0.0039 & 0.0349 \\  & New method & 26 & 0.0019 & 0.0038 & 0.0336 \\ \hline \multirow{3}{*}{Single-frequency} & Without constraint & 106 (65\%) & 0.0102 & 0.0748 & 0.0832 \\  & A priori variance & 52 (28\%) & 0.0129 & 0.0848 & 0.0732 \\ \cline{1-1}  & New method & 37 & 0.0092 & 0.0687 & 0.0514 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Static RT PPP for three processing methods.
## 3 Results  
### Validation of the PT-JPL Model  
#### 3.1.1 Validation of Water Balance Method  
The main runoff characteristic data in the HLS come from the two hydrological stations of Toudaoguai and Longmen. The two stations are the upstream gate station and the downstream gate station, which can represent the inflow and outflow of runoff in the study area. The calculation for interval runoff is the runoff of the downstream Longmen station minus the runoff of the upstream Toudaoguai station. The runoff for Toudaoguai and Longmen stations in 2000-2018 is shown in Figure 2a. The research shows that the average annual runoff of Toudaoguai hydrological station over 19 years is 16.845 billion m\({}^{3}\), and the average annual runoff of Longmen hydrological station over the past 40 years is 18.901 billion m\({}^{3}\). The average annual runoff of the study area over 19 years is 2.056 billion m3. The runoff depth of the HLS from 2000 to 2018 is calculated as shown in Figure 2b. The ET of the HLS from 2000 to 2018 was obtained by subtracting the average precipitation of the HLS from 2000 to 2018 (Figure 2c). Figure 3 shows the validation comparison between the ET simulated by the model and the ET calculated by the water balance method.
###### Abstract
Successful applications in cursor control [26], text typing, [13; 21] or mobile device navigating [27; 28; 29] have been introduced. Besides, standalone systems employing local processors, such as programmable gate array (FPGA) [4], have been proposed with low power consumption and compact construction. However, in these research studies, the measured magnetic field distribution signals are utilized for feature signal classification and command derivation, and a limited number of commands could be provided, where the suppleness and flexibility of the tongue are not fully exploited. In TCI systems based on precise magnetic tracking, a nonlinear magnetic inverse problem needs to be solved to realize magnetic localization with high accuracy, where time-consuming iterative algorithms are often employed to search for the solution. A precise magnetic tracking system for tongue movement developed by [PERSON] et al. [30] solved this nonlinear magnetic problem by the Nelder-Mead algorithm and resulted in a root mean square localization error of 0.5 mm within 30 mm of the tracking range, where the measured position and magnetic moment parameters at the start point were provided as initialization for optimization. Research studies have focused on improving localization accuracy with reduced computational time. In the study of [PERSON] et al. [31], a linear inverse model was proposed, in which the solution could be computed using measurements from five three-axis sensors by the matrix and algebra computations. Besides, methods based on modified forward models for the magnet, such as the analytical forward model [32] and the distributed multilevel current model [33], were developed. Typically, the tongue motions are limited in the intraoral region, which is sealed and narrow. It is impractical to deploy devices inside the mouth, where the narrow space and the wet environment will be a challenge. Besides, a tongue device layout with a bulky size may cause discomfort and even lead to digestive disorders, since the tongue is particularly sensitive. In this paper, a proof-of-concept prototype TCI is presented for potential environment control. A combined \(T\)-type PM is taken as magnetic marker, to promote the anisotropy of the magnetic field distribution. Precise tongue motionswith high resolution can be provided by an adaptive Levenberg-Marquardt algorithm (aLMA), in which the error brought by sensing system deviations, involving the PM and the sensor array, is assessed by a comprehensive calibration method. This offers a promising basis for TCI-based smart environment control. The reminder of this paper offers the following:  
* Section 2 presents the inverse magnetic localization model based on the combined \(T\)-type PM and sensor array measurements, the constitution of the aLMA, and a comprehensive sensing system calibration method;
* Section 3 describes the architecture of the prototype system, including the hardware and the custom-made graphical user interface (GUI);
* In Sections 4 and 5, the performance of this system in precise magnetic localization is evaluated and discussed by bench-top experiments.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 5 Discussion  
### Summary of Key Findings  
The results of this study demonstrate that machine learning (ML) models, particularly the random forest (RF) algorithm, significantly outperform traditional linear regression (LR) and standalone decision tree (DT) methods in predicting the short-term movement of the U.S. 10-year Treasury bond yield. Among the models evaluated, RF achieved the highest accuracy (R\({}^{2}\) = 0.5760), followed by DT and MLP, while LR yielded the lowest performance. These findings are consistent with prior research that has shown ensemble methods like RF to be highly effective in capturing nonlinear and high-dimensional relationships in financial data [6, 9].  
The analysis further confirms that key macroeconomic indicators, namely the federal funds rate, core inflation (CPI), GDP growth, and the federal debt growth rate, have significant explanatory power in bond yield forecasting. The importance rankings derived from the RF model suggest that monetary policy and inflation expectations remain dominant drivers of long-term yields, aligning with the term premium and expectation theory of interest rates [1, 4].