esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Leg Extensor Power Rig (LEPR)  
Patients undergoing MoM THA/HRA had significantly more power in both the implant side leg (\(p<0.000\)) and the non-implant side leg (\(p<0.002\)) compared to patients undergoing MoP THA, but the inter-limb difference in muscle power between groups was not significant (\(p=0.87\)) (Table 3). Mean muscle power in the two groups correlated in the following way with the mean hip and thigh muscle mass. The implant-side leg in the MoM THA/HRA group (\(r=0.54\), \(p=0.000\)). The implant-side leg in the MoM THA/HRA group (\(r=0.06\), \(p=0.39\)). The non-implant-side leg in the MoP THA group (\(r=0.54\), \(p=0.000\)). The non-implant-side leg in the MoP THA group (\(r=0.30\), \(p=0.16\)) (Figure 3a,b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region of Interest (ROI)** & **MoM THA/HRA** & **MoP THA** & \(p\)**-Value** \\ \hline Hip area (g/cm\({}^{2}\)) & & & \\ Implant side & 9.44 (1.50) & 8.98 (1.59) & 0.24 b  \\ Non-implant side & 10.63 (1.57) & 9.78 (1.55) & 0.04 b  \\ Inter-limb difference & 1.28 (0.71) & 0.92 (0.47) & 0.02 a  \\ \hline Thigh area (g/cm\({}^{2}\)) & & & \\ Implant side & 6.96 (1.19) & 5.83 (1.28) & 0.0004 b  \\ Non-implant side & 7.13 (1.26) & 6.04 (1.22) & 0.0008 b  \\ Inter-limb difference & 0.38 (0.33) & 0.41 (0.40) & 0.71 a  \\ \hline Hip and thigh area (g/cm\({}^{2}\)) & & & \\ Implant side & 7.88 (1.15) & 6.71 (1.39) & 0.0003 b  \\ Non-implant side & 88.35 (1.18) & 7.20 (1.58) & 0.0008 b  \\ Inter-limb difference & 0.51 (0.34) & 0.54 (0.37) & 0.76 a  \\ \hline Calf area (g/cm\({}^{2}\)) & & & \\ Implant side & 3.63 (0.53) & 3.35 (1.01) & 0.0038 b  \\ Non-implant side & 3.69 (0.49) & 3.47 (1.07) & 0.0097 b  \\ Inter-limb difference & 0.29 (0.21) & 0.18 (0.12) & 0.06 a  \\ \hline \hline \end{tabular}  
* Two-sample Wilcoxon rank-sum (Mann–Whitney) test. b Paired \(t\)-test.  
\end{table}
Table 2: Comparison of mean muscle mass in both legs and inter-limb difference between mean muscle mass in MoM THA/HRA (\(n=51\)) and MoP THA (\(n=23\)). Values are mean (SD).
# Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations
## 1 Introduction  
As fidelity significantly affects the response of a tested autonomous vehicle, in a more realistic virtual world, the validity and acceptance of tests in a simulation are higher [1; 2]. Therefore, to enhance their realism, important elements such as vehicle dynamics, sensor models, and rendering technologies can be easily incorporated into the existing automated driving simulations. This is because the nature of their work remains unchanged before or after the integration and provides an adequate balance between performance and efficiency [3]. However, a primary element of this test environment is the scenario vehicle, which is expected to perform trustworthy interaction bidirectional tasks with the autonomous vehicle under test. With this emerging demand for realistic behaviors of scenario vehicles [1], the accurate simulation of the behaviors of human drivers is becoming increasingly challenging [4].  
One of the major issues in the existing models is their conservative behavior, as they can only respond negatively to the environment or other agents instead of taking assertive actions. Another problem is the underlying methodology, wherein a large proportion of the models use the finite-state machine (FSM), Markov decision process, or neural networks, which cannot be easily modified. However, rapid adjustments to the behaviors of the scenario vehicles are often necessary during simulation tests.  
To address the aforementioned problems, this study proposes a dynamic scenario vehicle model considering the perspective of a human driver. Based on the theories of [PERSON] and [PERSON] [5; 6], every action is considered to be determined by an underlying motive. The basic framework of the model is organized using a behavior tree, which describes switching between the finite sets of tasks in a modular manner. This implies thatthe actions performed, decisions made, and transfers between states are visible and easy to compose.  
Simulations were conducted using a self-developed two-dimensional (2D) simulator, which currently supports an earlier version of the OpenDRIVE format map description file. The results indicate that scenario vehicles equipped with the proposed model successfully perform long-distance driving tasks to reach their targets and demonstrate interactions with other vehicles.
## 4 Results
### Discussion  
The model results suggest that the charging demand varies with the geographic locations of the chargers and charging time. These findings are similar to those reported in [30]. Specifically, the model results imply that the charging demand tends to be concentrated at a specific time and place. The findings suggest that policymakers should understand the dynamic charging patterns in regional contexts and appropriately manage the charging demand. For example, regions with tourist destinations such as Gangneung-si may consider installing chargers at tourist facilities and accommodations to accommodate the intensive charging demands in these facilities. In a region where the EV program is in a maturity phase, such as Jeju-do, it is necessary to consider expanding the charging network throughout the region to accommodate the dynamic charging demand generated by those who use EVs for various activities.  
This study provides crucial findings and suggestions for the success of EV programs. First, we identified several factors influencing the charging demand; for example, the charging demand was associated with various charger characteristics. It is also suggested that policymakers consider implementing proper policy measures (e.g., providing free parking, determining the appropriate charger type between slow and fast chargers considering the temporal and geographic charging demand, and determining proper facilities where chargers need to be installed). Second, this study found that the temporal and geographic charging demands varied with the phases of the EV program; for example, it was found that charging patterns were different between different regions in different phases of the EV program (e.g., Gangneung-si, where the EV program is in the early phase, vs. Jeju-do, where the EV program is in the maturity phase). In the early phase of the EV program, the charging demand tends to be produced by specific activities (e.g., tourism), leading to a concentration of charging demand at a specific time and place. However, in the maturity phase of the EV program, the charging demand tends to be distributed over time and place, as EVs are used for a variety of activities. These findings provide an important implication that policymakers should consider a stepwise strategy to construct charging infrastructure at the appropriate scale and configuration, depending on the phase of the EV program.
## 2 Materials and Methods
### Batch Adsorption Experiments  
In order to evaluate the effect of the amidoximation time on adsorption capacity of a sorbent, a single-point adsorption experiment was conducted on Pd(II). Here, a Pd(II) solution of 1000 mg/L was prepared by adding 0.1 M HCl solution; then, 0.03 g of the fibrous adsorbents was added to 30 mL of Pd(II) solution in a 50 mL polypropylene conical tube. The tubes were continuously shaken by an incubator at 160 rpm and 25 \(\pm\) 2 \({}^{\circ}\)C and their samples were carefully taken.  
Isotherm experiments for testing sorption capacity and metal selectivity of the prepared and commercial sorbents were conducted. Here, also 0.03 g of PAN-oxime fiber or Amberjet(tm) 4200 was added to 30 mL of metal solutions, which included Pd(II) and Pt(IV) single-metal systems or Pd(II)-Pt(IV) binary metal systems for 24 h at 25 \(\pm\) 2 \({}^{\circ}\)C. The stock solutions of metals were prepared in 0.1 M HCl solution and diluted if necessary. In these experiments, the initial concentrations were changed from 0 to approximately 1200 mg/L.  
The sorption kinetics of the adsorbents was performed to check equilibrium time. In this case, 0.1 g of the adsorbent was floated in 100 mL of 500 mg/L metal solution at 160 rpm and 25 \(\pm\) 2 \({}^{\circ}\)C. The kinetic samples were collected at fixed time intervals. All taken samples from supernatant of batches were analyzed using an inductively coupled plasma spectrometer (ICP; ICP-7510, Shimadzu, Kyoto, Japan).  
For isotherm and kinetic experiments, five replicates of independent separate sorption experiments were conducted and the standard errors were within 5%.
# Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
## 2 Dynamic Model and New Cross-Coupled Control Law
### Cross-Coupled Contouring Controller  
As mentioned before, PD control is very popular for robots in industrial applications. In this paper, a cross-coupled contouring controller called CC-PD is proposed as follows:  
\[\tau=K_{p}e_{q}+K_{d}\dot{e}_{q}+K_{cp}e_{c}+K_{cd}\dot{e}_{c} \tag{9}\]where \(K_{p}\) and \(K_{d}\) are the proportional and derivative control gain matrices in the joint space; all these matrices are assumed to be positive definite. \(K_{cp}\) and \(K_{cd}\) are the proportional and derivative control vectors with positive values in the task space, respectively.  
From Equation (9), one can see that the proposed CC-PD is a combination of PD control in the joint space based on the tracking errors and PD control in the task space based on the contour errors. The PD controller in the joint space is used to maintain the stability of the robotic system, while the PD controller in the task space is used to reduce the contour errors. The goal for developing such a combined controller is to improve tracking performances at the joint level and the contour performances at the task level as well.  
Based on the Jacobian regulation, applying Equation (8) into Equation (9), the proposed CC-PD control law can be simplified as:  
\[\tau=\left(K_{p}+\overline{K}_{cp}\right)e_{q}+\left(K_{d}+\overline{K}_{cd} \right)\dot{e}_{q} \tag{10}\]  
\[\text{with }\left\{\begin{array}{l}\overline{K}_{cp}=K_{cp}\left[\begin{array}[ ]{cc}-C_{x}&C_{y}&0\\ \overline{K}_{cd}=K_{cd}\left[\begin{array}{cc}-C_{x}&C_{y}&0\\ \end{array}\end{array}\right]J\left(q\right)\right.\\ \end{array}\right. \tag{11}\]  
According to Equations (10) and (11), it can be seen that the proposed CC-PD control law is an equivalent nonlinear PD control in the joint level, as the Jacobian matrix is a nonlinear function of the joint positions.
## Literature Review
### OTT Services
A study based on shadow pricing was also conducted to determine an effective method to price broadband services [20], concluding that setting the pricing plan according to the usage patterns of consumers was the best strategy. Because OTT services utilize a considerable amount of network bandwidth, some studies have proposed methods of predicting network consumption and pricing via a content-delivery or a software-defined network [21; 22]. With the spread of OTT services, the pricing-related issues of OTT and network providers persist, and most existing studies have suggested plans based on the amount of network usage. Thus, in order for networks or OTT providers to establish an optimal strategy related to OTT, the OTT-service usage patterns of consumers must be identified very quickly. Both network and OTT providers can establish effective pricing strategies only when they can correctly and immediately identify which users are using what OTT services and how much data they consume, classifying all items and load-balancing accordingly. Thus, OTT user classification is the first step in establishing an effective strategy.
## 2 Materials and Methods
### Models' Evaluation  
To assess the accuracy of the predictive models, the rice grain and root soil samples were divided into a training set and a test set at an 8:2 ratio. The model was trained using the training set and evaluated using the test set to verify its accuracy. The accuracy of the predictive models was validated using the regression coefficient of determination (R2), normalized mean error (NME), mean relative error (MRE), and root mean square error (RMSE) [41]. The formulas are as follows:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}(o_{i}-e_{i})^{2}}{\sum_{i=1}^{N}(o_{i}- \overline{o})^{2}} \tag{2}\]  
\[\text{NME}=\frac{\overline{e}-\overline{o}}{\overline{o}} \tag{3}\]  
\[\text{MRE}=\frac{\sum_{i=1}^{N}\frac{|e_{i}-o_{i}|}{o_{i}}}{N}\times 100\% \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(e_{i}-o_{i})^{2}}{N}} \tag{5}\]  
where \(e_{i}\) and \(o_{i}\) represent the predicted and observed values for each sample, respectively; \(\overline{o}\) and \(\overline{o}\) are the average predicted and observed values; and \(N\) is the total number of samples.  
The model is considered more accurate when R2 approaches 1. A negative NME indicates that the BAF is underestimated, while a positive value indicates overestimation. The smaller the MRE and RMSE, the more accurate the model is.
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Numerical Results
#### 5.2.1 BER Analysis  
In Figure 6, we show the mean BER (in %) as a function of the number of sample points, i.e., iterations, where each iteration corresponds to one complete communication cycle. Simulation results have been collected considering a T-Student distribution, with results providing an accuracy of 95%. Confidence intervals are shown through vertical bars in the plot.  
A reduction in the confidence interval has required a minimum number of iterations in the order of at least 1500 samples. Note, however, that the BER remains around an average value of approximately 1.1%. In Figure 7, we report the detailed distribution of BER values for both the BPSK and QPSK modulation alternatives. The \(x\)-axis highlights the BER bins and the \(y\)-axis shows the percentage of occurrences within each bin. The majority of observed losses lie within the 0-1% range, which witnesses the reliability of the system, especially when considering the possibility to also exploit Forward Error Correction to speed up performance. Typically, in the case of BPSK, we observe a lower bit error rate as compared to the QPSK due to its intrinsic robustness in noisy conditions. In particular, in case of rare fish species, lower gain values and BPSK modulation are selected.  
Figure 5: Energy consumption comparison.  
Similarly, in Figure 8, we show the histograms of the BER for different classes of fishes (1 to 9) and for both BPSK and QPSK modulations. In case of common fish species (low class values), no relevant environmental constraints emerge and, thus, higher gain and packet length are set. The difference here is due to the modulation type where classes 1 and 2 exhibit the minimum losses, as compared to class 3, which uses the BPSK and overpowered gain value. Similarly, for intermediate fish species from classes 4 to 6, the BPSK exhibits a lower BER as compared to QPSK, which requires more power and is less robust in noisy conditions. For the case of rare/vulnerable fish species, from classes 7 to 9, only BPSK is employed. Although the BER remains below 1% on average, occasional spikes occur due to the combination of low gain values and large packet sizes. These results show how modulation selection, gain optimization, and packet sizing choices are interrelated and can significantly influence system reliability under varying environmental and operational constraints.  
Figure 6: Mean BER over iterations.  
Figure 7: BER comparison for BPSK and QPSK.
# Determining the Safety Level of State Roads: An Italian Case Study
## 4 Analysis Method
### Safety Indicator Evaluation  
At this stage, the safety indicator evaluation can be performed. Various safety indicators are considered and compared. The HSM safety indicator (excess of expected crashes over predicted crashes) involves the application of the calibrated predictive model to evaluate the actual safety level of each road section. First, the empirical Bayesian method is used to obtain the best estimate of crash frequency (expected crashes) for each homogeneous segment. The road network is divided into elementary sections of a constant length of 1 km to make them comparable in terms of safety level. Then, observed, predicted, and expected crashes are transferred from the road network divided into homogeneous segments to the same road divided into elementary sections. Different safety performance indicators are evaluated for each elementary segment, and different safety rankings are performed accordingly. The analyzed sections are ordered from the least dangerous to the most dangerous based on each safety performance indicator. These safety rankings are extremely useful for identifying the most critical road sections and for planning field inspections. Finally, a comparison between the results obtained with different safety indicators is carried out, and a possible approach to assigning priority to road sections is defined. The following section describes the procedure in detail and applies it to an Italian case study.
# Investigation on Aerodynamic Noise Characteristics of Coaxial Rotor in Hover
## 3 Results and Discussion
### Noise at Different Observation Points
It is well known that the loading noise frequency of a single rotor is usually low [34], and in the high-frequency band, the magnitude is rather weak. As seen in the figures, on one hand, the aerodynamic interaction of the coaxial rotor leads to stronger loading noise. On the other hand, the features of the frequency spectrum have been changed. The noise in the high-frequency band is enhanced, which is obviously different from the single rotor.
## 4 Correlations between Spatial Indicators and Lighting Parameters
### Correlations in the Case of Scenario 2 (Design Status)  
In Figure 15, the correlation between the \(I\) index and \(L_{uv}\) at the design status (see Section 3.3) is shown. In this case, compared to the present status (see Figure 12), \(R^{2}\) increases from a negligible value to 0.63. The regression line slope is positive suggesting that as roads centrality increases, luminance levels increase because more trafficked roads need to be more enlightened. The \(R^{2}\) coefficients increase also for the correlations between \(I\) index and both \(E_{h,uv}\) and \(E_{h,min}\).  
Looking at the sidewalks on the same side of luminaires (see Figure 16), for \(E_{h,uv}\), \(R^{2}\) varies from 0.16 (see Figure 13) to 0.48; while for \(E_{h,min}\), \(R^{2}\) varies from 0.33 (see Figure 13) to 0.39. The slopes of the regression lines are positive so for more central roads the illuminance levels increase. Looking at the sidewalks on the opposite side of luminaires (see Figure 17), it can be observe that, for \(E_{h,uv}\), \(R^{2}\) varies from 0.08 (see Figure 14) to 0.43; while for \(E_{h,min}\), \(R^{2}\) varies from a negligible value (see Figure 14) to 0.41. The slope of the regression lines is positive so for more central roads the illuminance levels increase.  
Figure 14: Scenario 1 (present status): correlations between \(I\) and \(E_{h,uv}\) (black circles and a continuous line for the regression) and between \(I\) and \(E_{h,min}\) (white circles and a dotted line for the regression) on the sidewalks on the opposite side of the luminaires.  
Figure 15: Scenario 2 (design status): correlation between \(I\) and \(L_{uv}\).
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 2 Principle
### Imaging Model
The origin \(O_{ci}\) is located at the camera's optical center, and the \(Z_{ci}\) axis is perpendicular to the image plane and coincides with the optical axis of the camera. The \(X_{ci}\) and \(Y_{ci}\) axes are parallel to the \(u_{i}\) and \(v_{i}\) axes of the \(O_{i}\)-\(u_{i}v_{i}\), respectively. The plane where \(Z_{ci}=f\) is the image plane, where \(f\) is the principal distance between the optical center and the image plane. 4. Auxiliary Coordinate System \(O_{p}\)-\(X_{p}Y_{p}Z_{p}\) is a right-handed (\(X_{p}\)-\(Y_{p}\)-\(Z_{p}\)), orthogonal coordinate system. Its origin \(O_{p}\) is located at the optical center. The plane \(X_{p}O_{p}Z_{p}\) is a virtual plane, whose axis of \(X_{p}\) and \(Y_{p}\) are parallel to that of \(Y_{w}\) and \(X_{w}\) in the same direction, respectively, and the axis of \(Z_{p}\) is parallel to that of \(Z_{w}\) in the opposite direction. Figure 2: Schematic diagram of the coordinate systems.
## Appendix A Specific Measures of the Radar Data Quality  
### Distance from the Radar  
Each radar bin was evaluated according to its distance from the radar. The radar beam broadens and its volume grows as it propagates in the atmosphere. The resulting measurement, then, represents an average of a larger volume. In this case, a linear relationship is used:  
\[qi_{1,i}=\begin{cases}1.0;&r<r_{min}\\ 1.0-\frac{r-r_{min}}{r_{max}-r_{min}};&r_{min}\leq r\leq r_{max}\\ 0.0;&r>r_{max}\end{cases} \tag{1}\]  
where \(r\) is the distance from the radar and \(r_{min}\) and \(r_{max}\) are parameters of the computation (with default values of 0.0 and the maximum range of the radar, respectively). The index \(i\) indicates that the quality index is estimated for the \(i\)-th available radar (the same holds true for the upcoming equations).  
This quality index (QI) belongs to the most elementary ones in assessing the radar signal quality: it appeared in the same [19; 20; 21] or in a similar form with most studies (such as using the exponential relationship [16]). There may be sophisticated methods in defining the minimum or the maximum ranges of the radar [19; 20; 21]. Beyond this, [PERSON] et al. [16] incorporated a correction factor that depended on the distance of the target bin from the nearest radar.