esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Performance of the ANN Model in Predicting Delamination  
To investigate the neural network performance, the mean squared error (MSE) was involved. The performance plot graph (Figure 4) represents the MSE of the neural network during the training, validation, and testing phases.  
The point at which the validation MSE is at its lowest is considered the best validation performance, marked by the green circle on the plot. In this case, the best validation performance is 6.2196 at epoch 4.  
The decreasing trend of the training and validation MSE indicates effective learning. The training MSE continues to decrease significantly, suggesting that the model fits the training data well.  
The validation MSE stabilizes after epoch 4, indicating that further training does not significantly improve the model's performance on validation data and helps in identifying the optimal stopping point to avoid overfitting.  
The test MSE aligns closely with the validation MSE, indicating that the model generalizes well to new, unseen data and that the training process is effective.  
The set of plots shown in Figure 5 illustrates the regression analysis of the neural network's performance across different phases: training, validation, testing, and overall. Each plot compares the network's predicted outputs to the actual target values and provides the correlation coefficient (R), which indicates how well the predicted outputs match the actual targets.  
Figure 4: The neural network performance graph.  
Figure 5: The regression analysis of the neural network’s performance.  
All R-values are close to 1, which translates into a strong linear relationship between the predicted outputs and the actual targets. The neural network model captures the underlying patterns in the data. The high R-values in the training (0.99774), validation (0.9322), and testing (0.97432) phases also suggest that the model performs consistently well. This minimizes overfitting and maintains generalization to new data. The fit lines also indicate that the model has a good performance.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 3 Study 1: Visual Guidance Design and Evaluation
### Results
#### 3.2.1 Selection Time and User Behavior Data in Primary Task of Study 1
For the X dimension, main effects were found for Row \((F_{2,27,24.97}=8.53,p<0.005)\) and Column \((F_{4,44}=6.11,p<0.005)\). Post hoc Bonferroni pairwise comparisons showed that distance of row 1 (9.63 cm) was significantly longer than those of row 2 (7.23 cm) \((p<0.001)\), row 3 (8.01 cm) \((p<0.05)\), and row 4 (8.11 cm) \((p<0.05)\); row 5 (9.57 cm) was significantly longer than row 4 (8.11 cm) \((p<0.05)\). The distance of column 5 (9.72 cm) was significantly longer than those of column 2 (7.82 cm) \((p<0.01)\) and column 3 (7.87 cm) \((p<0.01)\). For the Y dimension, main effects were found for Row \((F_{2,33,25,67}=11.33,p<0.001)\) and Column \((F_{2,26,24,82}=9.16,p<0.005)\). Post hoc Bonferroni pairwise comparisons showed that the distance of row 1 (6.32 cm) was significantly longer than those of row 2 (4.93 cm) \((p<0.01)\), row 3 (5.22 cm) \((p<0.05)\), and row 4 (5.19 cm) \((p<0.05)\). The distance for row 5 was the longest (6.49 cm) and was significantly longer than those of row 2 (4.93 cm) (\(p<0.01\)), row 3 (5.22 cm) (\(p<0.01\)), and row 4 (5.19 cm) (\(p<0.001\)). And the distance of column 5 (6.63 cm) was significantly longer than those of column 2 (5.16 cm) (\(p<0.01\)), column 3 (5.05 cm) (\(p<0.01\)), and column 4 (5.37 cm) (\(p<0.05\)). Figure 2: Mean selection time, head rotation degree, and head movement distance results of Primary Task in Study 1. For the \(Z\) dimension, main effects were found for Row \((F_{4,44}=6.42,p<0.001)\). Post hoc Bonferroni pairwise comparisons showed that the distance of row 1 (9.21 cm) was significantly longer than those of row 2 (7.40 cm) \((p<0.05)\), row 3 (7.43 cm) \((p<0.001)\), and row 4 (7.23 cm) \((p<0.05)\). The mean head movement distances across the Feedback Modality, Row, and Column are illustrated in Figure 2.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 4 Handover Management for Drone Networks
### Handover Procedure in 5G  
The handover procedure is a significant process that consists of different steps, algorithms, and techniques to enable UEs to switch connections from one cell to another. The procedural steps differ from one technology to another. The same procedure used for the terrestrial UE can work with drones; however, it does not guarantee efficient handover performance since the characterization of drones is different. This subsection provides a brief description of the handover procedure for one handover system scenario that may occur, as illustrated in Figure 5 (as an example).  
The 5G handover process is closely similar to LTE-Advanced system with some further enhancements. The Access and Mobility Management Function (AMF) conducts the responsibility of the Mobility Management Entity (MME) [73]. The User Plane Function (UPF) is the same as the Serving Gateway (SGW). The handover procedures are listed as follows:  
1. The UE periodically sends the measurement report to the S-BS.
2. The S-BS configures the measurement procedure of the UE.
3. Based on the measurement report, the S-BS makes the switch decision, and the handover request is then received by the Target Base Station (T-BS).
4. The T-BS replies with an acknowledgment to the S-BS based on its resources.  
Figure 4: Handover scenarios with connected drones in future mobile networks.  
Figure 5: S1 key renewal process in drone networks.  
The handover is initiated, and the T-BS supplies the UE with the necessary information, connecting it to the target cell.
* The UE receives uplink allocation and timing info sent from the T-BS.
* The T-BS updates the AMF for UE cell alteration, the UPF is updated by the AMF for the UE, the path of the UE is updated by the UPF, then the AMF notifies the T-BS for path update.
* The S-BS is updated by the T-BS for the completion of the handover.  
Another way of categorizing handover is based on whether the UE controls or assists in the process. A handover in which both the network and the UE are involved is known as a hybrid handover. These categories have been investigated for mobile Internet Protocol (IP) networks and VANETs, but only a few studies are currently available for drone networks.
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
## 2 System Description
### Transmission Channel  
The transmission channel of MAI system is a single channel subsystem, as shown in Figure 2, which can be roughly divided into two parts: the excitation source and the power amplifier. Firstly, the baseband signal of pseudo-random coded modulation is generated by the waveform generation module according to the transmitting parameters. Then, after up-conversion by the direct digital synthesizer (DDS) device, the signals of about 1 mW are fed into three 2 kW broadband power amplifier (AMP) units. Finally, through a three-in-one combiner, the total power of 6 kW is fed into the transmitting antenna.  
The 2 kW broadband power amplifier unit adopts the all-solid-state structure, which is shown in Figure 3. It mainly consists of pre-driver, power divider, 4 x 600 W linear amplifier, and power combiner, directional coupler, monitoring unit and power supply apparatus. The monitoring unit monitors the output power and voltage standing wave ratio (VSWR) at all times. Once the singular changes occur, such as the over-voltage, over-current or the over-power events, the transmitter will be issued a turn-off command. When the fault is eliminated, the transmission channel resumes to work.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Indicators** & **Values** \\ \hline System function & Mesospheric/Ionospheric sounding \\ Working frequency & Mesospheric: 1.98 MHz/ Ionospheric: 2\(-\)20 MHz \\ Sounding range & Mesospheric: 70\(-\)100 km/ Ionospheric: 100\(-\)800 km \\ Peak power & Mesospheric: 6 Kw/ Ionospheric: \(\leq\)1 Kw \\ Waveform & Interpulse coding waveform \\ Duty cycle & 5\% \\ Height resolution & 1.98 km \\ Time resolution & Mesospheric: \(\leq\)2 min/ Ionospheric: \(\leq\)4 min \\ Sounding mode & Mesospheric: Fixed Frequency/Ionospheric: Frequency sweeping \\ Sounding Direction & Vertical upward \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main technical indicators of MAI system.  
Figure 1: The overall structure of the MAI system, which is mainly divided into three parts: the transmitting channel, multi-channel receiver and receiving antenna array.
# Design and Modelling of an Amphibious Spherical Robot Attached with Assistant Fins
## Simulation and Experiments
### Movement Experiments
#### Movement on the Water  
Linear motion experiments of the amphibious spherical robot on the water were carried out in order to validate the effectiveness of the assistant fins, as shown in Figure 11. Sixteen assistant fins, eight on each hemisphere shell, were attached to the outside surface of the spherical shell. The amphibious spherical robot with fins was then placed onto the surface of static water out of doors. The rolling motion motor was started at \(t=0\) and the robot's subsequent track of motion was recorded by a video camera. After that, all the assistant fins were removed from the spherical shell, transforming the amphibious spherical robot to a traditional spherical robot. The linear motion experiment was repeated and the track of motion of the robot without fins was also recorded. Comparison of locomotion performances of those two cases are made based on those video records.  
The velocities of the spherical robot with fins and without fins during accelerating process are shown in Figure 12. The results show that the spherical robot with fins speeds up more rapidly than that without fins when accelerating. It takes about 5 s for the spherical robot with fins to reach the velocity of 0.4 m/s, while it takes about 12 s for that without fins to reach the same velocity. The final steady velocity of the spherical robot with fins under constant rolling motion motor outputs, however, is only slightly higher than that without fins. The maximum on-water velocity that this amphibious spherical robot can reach is about 0.4 m/s, which is about 70% of the on-land locomotion velocity.  
Figure 11: Movement on the water. (**a**) Fins attached; (**b**) Fins removed.  
Figure 10: Curvilinear motion on land.
## 3 Results and Discussion
### Two Classical Methods Introduced to Establish PLS Models  
To validate the prediction accuracy and stability of the prediction model, two classical algorithms for selecting variables based on the near-infrared spectrum were introduced for comparison with the performance of the proposed DWT-BOSS selection algorithm. Two classical variable selection methods are the GA and interval iPLS.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 5 Analysis  
Although the unprocessed (raw) LiDAR measurements are distinct enough to the human eye for tie and ballast classification, an automatic differentiation method is needed to process large datasets resulting from track measurements over extended distances. The purpose of this section is to explore various data analytic methods that can process the LiDAR sensor measurements, enabling accurate and efficient differentiation of ties from ballasts in an automated manner.  
First, a statistical data analysis method (i.e., moving standard deviation) is evaluated because of its effectiveness in extracting surface variation characteristics. Next, two machine learning (ML) methods, namely Decision Tree and Logistic Regression, are considered to better deal with data uncertainty and ambiguity. Additionally, several preprocessing techniques are investigated to transform the LiDAR measurements into more suitable parameters for training the ML models. The downsides and upsides of each model are elaborated, and the model shown finally to be the best in terms of accuracy and robustness will be selected for analyzing the LiDAR data for tie and ballast differentiation.
# Lower Limb Muscle Activation in Young Adults Walking in Water and on Land
###### Abstract  
Previous research has shown that exercise interventions requiring increased activation of the tibialis anterior (TA), the primary ankle dorsiflexor, can improve walking performance in individuals with foot drop. Correspondingly, heightened drag forces experienced during walking performed in water may augment TA activation during the swing phase of gait, potentially leading to improved walking gait on land. Therefore, this study aimed to compare surface electromyographic (sEMG) activation in the TA and medial gastrocnemius (GM) during gait performed in water versus on land. Thirty-eight healthy, recreationally active young adults, comprising 18 females and 20 males, participated in the study. Each participant completed 2 min walking trials under five conditions: land 2.5 mph, land 3.5 mph, water 2.5 mph, water 3.5 mph, and water 3.5 mph with added jet resistance. Stride kinematics were collected using 2-dimensional underwater motion capture. TA and GM, muscle activation magnitudes, were quantified using sEMG root-mean-square (RMS) amplitudes for both the swing and stance phases of walking. Additionally, TA and GM co-activation (Co-A) indices were estimated. Two-way within-subjects repeated measures analyses of variance were used to evaluate the main effects of and interactions between the environment and walking speed. Additionally, paired sample t-tests were conducted as a secondary analysis to investigate differences between walking in water at 3.5 mph with and without added jet resistance. Main effects and interactions were observed across various stride kinematics and sEMG measures. Notably, TA sEMG RMS during the swing phase of walking gait performed at 2.5 mph was 15% greater in water than on land (\(p<0.001\)). This effect increased when walking gait was performed at 3.5 mph (94%; \(p<0.001\)) and when jet resistance was added to the 3.5 mph condition (52%; \(p<0.001\)). Furthermore, TA Co-A was increased during the stance phase of gait in water compared to on land (\(p<0.001\)), while GM Co-A was reduced during the swing phase (\(p<0.001\)). The findings of this study offer compelling evidence supporting the efficacy of aquatic treadmill walking as a potential treatment for individuals suffering from foot drop. However, further research is needed to evaluate whether a causal relationship exists between heightened TA activation observed during aquatic treadmill walking and improvements in voluntary dorsiflexion during gait.  
electromyography; aquatic; treadmill; foot drop +
Footnote †: journal: Applied Sciences
## 3 Results
### Monte Carlo Approaches  
In this subsection, results of stochastic time series generation of precipitation, runoff, and SMC are presented. Fifty Monte Carlo simulations are carried out for each of these physical quantities, and for each of these simulations, the errors in the mean and standard deviation are calculated. This yields samples of the errors whose mean, median, and standard deviation are calculated and reported in the Tables below. Table 3a,b shows the error statistics on the mean and standard deviation for Paterno and Busachi, respectively. Figure 8: Trends in the number of hours (events) with intense precipitation (greater than 3.6 mm/h), on an annual basis and on a seasonal basis (spring and autumn), for the Busachi case study. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & & & & & & & \\ \hline \multicolumn{8}{c}{**e\({}_{\text{mean}}\)(\%)**} \\ \hline  & **Win** & **Spr** & **Sum** & **Aut** & **Win** & **Spr** & **Sum** & **Aut** \\ \hline Mean & 2.4 & 3.9 & 4.2 & 2.5 & 2.8 & 4.1 & 4.2 & 2.7 \\ Median & 1.6 & 3.3 & 3.3 & 2.3 & 2.1 & 3.4 & 3.3 & 2.3 \\ STD & 2.2 & 2.8 & 3.4 & 1.7 & 2.7 & 3.1 & 3.5 & 2.1 \\ \hline \multicolumn{8}{c}{(b)} \\ \hline  & & & **e\({}_{\text{mean}}\)(\%)** & & & & **e\({}_{\text{STD}}\)(\%)** & \\ \hline  & **Win** & **Spr** & **Sum** & **Aut** & **Win** & **Spr** & **Sum** & **Aut** \\ \hline Mean & 2.7 & 3.4 & 6.0 & 3.3 & 3.6 & 3.3 & 5.9 & 3.5 \\ Median & 2.3 & 2.7 & 4.9 & 2.8 & 2.8 & 2.8 & 4.4 & 3.1 \\ STD & 2.0 & 2.5 & 4.3 & 2.6 & 3.1 & 2.6 & 5.0 & 2.9 \\ \hline \end{tabular}
\end{table} 
Table 3: (a) Mean, median, and standard deviation (STD) of the errors on the mean (e\({}_{\text{mean}}\)) and standard deviation e\({}_{\text{STD}}\)) evaluated in the case of synthetic hourly precipitation profiles, for the Paterno case study. (b) Mean, median, and standard deviation (STD) of the errors on the mean (e\({}_{\text{mean}}\)) and standard deviation (e\({}_{\text{STD}}\)) evaluated in the case of synthetic hourly precipitation profiles, for the Busachi case study. The narrow values of error statistics show that the approach based on Pearson's family of distributions appropriately simulates the statistical and physical variability of historical precipitation values, for both locations. Once the hourly synthetic precipitation profiles are obtained, they can be used as inputs to the equations for direct runoff assessments. Equation (8) is a linear combination of the precipitation values, so it is expected that the error statistics on the means and standard deviations would give results very close to those of precipitation. Regarding the direct runoff evaluated by (9), this equation binds the direct runoff nonlinearly with the precipitation, therefore, it could be expected that in this case, the results of the error statistics of the synthetic runoff profiles could deviate from the error statistics results of synthetic precipitation profiles. Table 4a,b shows the results obtained for the direct runoff with both the Rational and the NRCS methods, for the [PERSON] and Busachi case studies, respectively.
## 5 Formal Specification and Analysis
### Reasoning of Using Formal Description  
There exist various traditional tools and techniques which are typically used for expressing the properties of software systems, however, these methods require a full commitment because the specification produced must be used to construct a complete and consistent model which will be assumed as a baseline for the further development. For complex and incomplete models such methods are not effective for a complete validation and verification of a large scale software specification. Consequently, it needs to apply mathematics-based techniques to overcome the weaknesses of these traditional approaches. Formal methods are advanced mathematics-based techniques, having computer tool support, used for modeling and formal specification of complex and critical systems. Formal specification of a system requires a detailed and thorough study of the design. Although it cannot be proved that formal specification of a system is correct, in general, because initially requirements are always provided in an informal way, but, it can be used to prove the properties and consistency in the design enhancing confidence to develop the systems. Experience of applying formal methods [28] has shown that it is an effective way of modeling, specifying, analyzing and verifying of properties of complex systems.
## 4 Radiative Transfer in Coupled Atmosphere-Water (Including Snow/ice) Systems  
### Radiative Transfer Equation--Unpolarized Radiation  
In the AccuRT computational tool, one considers a _coupled_ system consisting of two adjacent slabs (atmosphere overlying a water body) separated by a plane, horizontal interface. The refractive index changes abruptly across this interface from a value \(\tilde{m}_{c,1}\) in the upper slab (hereafter slab\({}_{1}\), the atmosphere) to a value \(\tilde{m}_{c,2}\) in the lower slab (hereafter slab\({}_{2}\), a water body). If the IOPs in each of the two slabs vary only in the vertical direction denoted by \(z\), where \(z\) increases upward, the corresponding vertical optical depth, denoted by \(\tau(z)\), is defined by  
\[\tau(z)=\int_{z}^{\infty}[\alpha(z^{\prime})+\beta(z^{\prime})]dz^{\prime} \tag{111}\]  
where the absorption and scattering coefficients \(\alpha\) and \(\beta\) are defined in Equations (1). Please note that the vertical optical depth is defined to increase downward from \(\tau(z=\infty)=0\) at the top of the atmosphere. In either of the two slabs, assumed to be in local thermodynamic equilibrium so that they emit radiation according to the local temperature \(T(\tau(z))\), the diffuse radiance distribution \(I(\tau,u,\phi)\) can be described by the radiative transfer equation (RTE)  
\[\mu\frac{dI(\tau,u,\phi)}{d\tau}=I(\tau,u,\phi)-S(\tau,u^{\prime},\phi^{ \prime}) \tag{112}\]  
where  
\[S(\tau,u^{\prime},\phi^{\prime})=S^{*}(\tau,u^{\prime},\phi^{\prime})+[1- \varpi(\tau)]B(\tau)+\frac{\varpi(\tau)}{4\pi}\int_{0}^{2\pi}d\phi^{\prime} \int_{-1}^{1}p(\tau,u^{\prime},\phi^{\prime};u,\phi)I(\tau,u^{\prime},\phi^{ \prime})du^{\prime}. \tag{113}\]  
Here \(u\) is the cosine of the polar angle \(\theta\), \(\phi\) is the azimuth angle, \(\varpi(\tau)=\beta(\tau)/[\alpha(\tau)+\beta(\tau)]\) is the single-scattering albedo, \(p(\tau,u^{\prime},\phi^{\prime};u,\phi)\) is the scattering phase function defined by Equation (5), and \(B(\tau)\) is the thermal radiation field given by the Planck function. The differential vertical optical depth is (see Equation (111))\[d\tau(z)=-[\alpha(\tau)+\beta(\tau)]dz \tag{114}\]  
where the minus sign indicates that \(\tau\) increases in the downward direction, whereas \(z\) increases in the upward direction, as noted above. The scattering angle \(\Theta\) and the polar and azimuth angles are related by (see Equation (25))  
\[\mathbf{\hat{\Omega}}^{\prime}\cdot\mathbf{\hat{\Omega}}=\cos\Theta=\cos\theta \,\cos\theta^{\prime}+\sin\theta^{\prime}\,\sin\theta\,\cos(\phi^{\prime}-\phi).\]  
By definition, \(\theta=180^{\circ}\) is directed toward nadir (straight down) and \(\theta=0^{\circ}\) toward zenith (straight up). Thus, \(u=\cos\theta\) varies in the range \([-1,1]\) (from nadir to zenith). For cases of oblique illumination of the system, \(\phi=180^{\circ}\) is defined to be the azimuth angle of the incident light.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis for the validation study of flualprazolam and isotonitazene was conducted using LabSolution(r) software (v5.97). The data were analyzed to assess key validation parameters, including linearity, precision, recovery, and reproducibility, in compliance with international guidelines. Linearity was evaluated using nine independent blood samples spiked with standard solutions across a concentration range of 1-100 ng/mL. The correlation coefficient (r\({}^{2}\)) for the calibration curves was calculated automatically by the software, with r\({}^{2}\geq 0.99\) confirming high linearity. Precision was expressed as the relative standard deviation (%RSD) for both repeatability (RSDr) and reproducibility (RSDwR). For RSDr, two analysts conducted measurements at two fortification levels (10 ng/mL and 50 ng/mL) in five replicates on the same day. RSDwR was assessed under intra-laboratory conditions over multiple sessions. The recovery rates were calculated by comparing the measured concentrations with theoretical values at each fortification level. Recovery values between 70 and 120% were considered acceptable, and compliance with this range was confirmed. The method's limit of detection (LOD) and limit of quantification (LOQ) were determined using the standard deviation of the response and the slope of the calibration curve, following the guideline-defined formulas. Statistical comparisons of the mean recovery, SD, and %RSD between the two analysts and fortification levels demonstrated the method's robustness. All statistical calculations and results adhered to the pre-specified acceptance criteria, validating the method's reliability and applicability in forensic and clinical toxicology.