esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
All the dependent variables were normally distributed. Means and standard deviations were calculated in each trial for the measured variables. Moreover, intraclass correlation coefficients ranging from 0.93 to 0.97 were found for all the analysed variables measured between values of the familiarisation and testing days (premeasurements for the EC and CC). These results show the reliability and constancy of the running pattern. The descriptive data and post-pre changes' ESs are shown in Table 1.  
In the results of the analysis of variance, a significant interaction effect (condition x time) was present for the FT (F = 8.14, \(p\) = 0.011, \(\eta^{2}\) = 0.32), SL (F = 4.76, \(p\) = 0.043, \(\eta^{2}\) = 0.22), F\({}_{\rm max}\) (F = 6.67, \(p\) = 0.019, \(\eta^{2}\) = 0.28), and \(\Delta\)y (F = 5.01, \(p\) = 0.039, \(\eta^{2}\) = 0.23) variables. [PERSON]'s post hoc interaction effect analysis revealed that only the EC significantly increased FT (mean difference [MD] 0.05 \(\pm\) 0.001 s, \(p\) = 0.005), SL (MD = 0.034 \(\pm\) 0.013 m, \(p\) = 0.019) (Figure 2), F\({}_{\rm max}\) (MD = 0.026 \(\pm\) 0.007 kN, \(p\) = 0.003), and \(\Delta\)y (MD = 0.001 \(\pm\) 0.001 m, \(p\) = 0.025) post intervention (Figure 3).  
A main effect of time for the EC revealed a significant decrease in the SF (F = 6.36, \(p\) = 0.022, \(\eta^{2}\) = 0.27). Post hoc analysis indicated a significant decrease for the SF (MD = \(-\)0.057 \(\pm\) 0.023 Hz, \(p\) = 0.026) only after the EC (Figure 2). No significant interaction or main effects of time and condition were found for CT, \(\Delta\)L, K\({}_{\rm vert}\), and K\({}_{\rm leg}\) for both the EC and CC. Pre and post significant differences in the EC in individual mechanics and kinematics variables are displayed in Figure 4.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{**CC**} & \multicolumn{3}{c}{**EC**} \\ \cline{2-7}
**Variable** & **Pre** & **Post** & **ES** & **Pre** & **Post** & **ES** \\ \hline CT (s) & 0.166 \(\pm\) 0.010 & 0.166 \(\pm\) 0.011 & 0.14 & 0.167 \(\pm\) 0.011 & 0.167 \(\pm\) 0.010 & 0.03 \\ FT (s) & 0.126 \(\pm\) 0.021 & 0.126 \(\pm\) 0.020 & 0.02 & 0.126 \(\pm\)  
Figure 3: Pre and post differences in the control and experimental conditions in running mechanical parameters ((**A**): vertical stiffness; (**B**): leg stiffness; (**C**): vertical displacement of the CM; (**D**): change in leg length; (**E**): maximal vertical GRF). CM: centre of mass; GRF: ground reaction force, * significant different from Pre (\(p\) < 0.05).  
Figure 2: Pre and post differences in the control and experimental conditions in running kinematic parameters ((**A**): contact time; (**B**): flight time; (**C**): step frequency; (**D**): step length), * significant different from Pre (\(p\) < 0.05).
# Optimization Design of Centrifugal Fan Blades Based on Bezier Curve Method
## 3 Multi-Objective Optimization Task
### Design Parameter Selection and Surrogate Model Construction  
In a two-dimensional coordinate system, \(O\) represents the origin of the coordinate axis, and determining the positions of five control points requires ten parameters. Firstly, it was determined that the coordinates of the blade inlet control point \(P_{1}\) and the radius of blade outlet control point \(P_{5}\) remained unchanged on the basis of the original blade profile. Then, the following seven parameters were selected as the blade profile control parameters: the inlet and outlet angles (\(\beta_{1}\) and \(\beta_{3}\)) of blade; the radii (\(r_{1}\), \(r_{2}\), and \(r_{3}\)) of control points \(P_{2}\), \(P_{3}\), and \(P_{4}\); the chord height (\(h\)) at control point \(P_{3}\); and the relative installation angle (\(\beta_{2}\)) of the blade, as shown in Figure 6. After multiple reconstructions of the 3D model to eliminate any cases where a 3D model of the blade could not be generated, the variation ranges of these seven parameters were finally determined, as presented in Table 2. Figure 7 provides a more intuitive demonstration of the blade profile variation range.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Expression** & **Original Value** & **Variety Range** \\ \hline \(\beta_{1}\) & \(\arctan\)\(\left(\left|(y_{2}-y_{1})/(1+\frac{x_{1}(y_{2}-y_{1})}{x_{2}-x_{1}})\right|\right)\) & 20\({}^{\circ}\) & 15\({}^{\circ}\)\(\rightarrow\)25\({}^{\circ}\) \\ \(\beta_{2}
## 4 Results and Discussion
### Measurements for Virtualizing an Existing Event Source  
As explained in the proposal, the first idea of virtualization came from giving a virtual representation of an existing event source. There were two main measurements of interest: amount of time required tohave a virtual representation of the event source, and time needed to notify the occurrence of an event using the virtual representation.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 7 Experimentation Results
### Comparison Study
However, the median and the mean of the accuracy values obtained by k-NN after applying GAF, WOA, PSO, and GA were calculated and displayed in Table 10. In addition, the Boxplot of these four methods was presented in Figure 10. As displayed, the mean and the median of the accuracy values obtained by GAF are higher than those obtained by PSO and GA, but somewhat similar to WOA. Therefore, it is worth mentioning that GAF yielded results better than or comparable to the aforementioned FS. Therefore, PPFS methods are competitive methods. Figure 8: Boxplot of the accuracy obtained by SVM after applying GAK and the 5-filter FS for all the datasets. The y-axis represents the minimum, first quartile, median, third quartile, and maximum value of the accuracy values. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **PPFS-GAK** & **CFS** & **PCA** & **GainRatio** & **MutualInfo** & **GiniIndex** \\ \hline \(p\_Value\) & - & 0.043 & 0.325 & 0.007 & 0.008 & 0.009 \\ Median & 84.5 & 76.8 & 82.5 & 71.9 & 70.0 & 70.0 \\ Mean & 84.3 & 72.6 & 80.3 & 70.4 & 72.0 & 72.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The statistical test results of GAK and the 5 filter FS using 18 datasets. Figure 10: Boxplot of the accuracy obtained by k-NN after applying PPFS-GAF, WOA, PSO, and GA for 8 datasets. The y-axis represents the minimum, first quartile, median, third quartile, and maximum value of the accuracy values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PPFS-GAF** & **WOA** & **PSO** & **GA** \\ \hline Median & 86.5 & 88 & 84 & 83 \\ Mean & 86.5 & 86.8 & 84.8 & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The statistical results of GAF, WOA, PSO, and GA using 4 datasets. Figure 9: The classification accuracy (presented in the y axis) provided by k-NN classifier after applying the four PPFS, WOA, PSO, and GA feature selection. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**kNN/ PPFS** & **PPFS-GAF (\%)** & **PPFS-GAK (\%)** & **PPFS-TF (\%)** & **PPFS-TK (\%)** & **WOA (\%)** & **PSO (\%)** & **GA (\%)** \\ \hline Breast Cancer & 97.61 & 96.85 & 96.85 & 96.85 & 96.83 & 95.4 & 95.5 \\ Sonar & 75.81 & 66.13 & 75.81 & 67.74 & 91.9 & 74.0 & 72.6 \\ Wave F. & 82.45 & 82.12 & 82.45 & 81.99 & 75.33 & 76.1 & 76.7 \\ Wine & 98.08 & 96.15 & 96.15 & 96.15 & 95.90 & 95.0 & 93.30 \\ WDBC & 95.29 & 98.24 & 98.24 & 98.24 & 97.10 & 94.10 & 93.80 \\ Heart & 81.48 & 81.48 & 81.48 & 81.48 & 80.70 & 78.40 & 82.20 \\ Ionosphere & 87.50 & 81.73 & 84.62 & 84.62 & 92.56 & 84.30 & 83.40 \\ Colon & 72.22 & 77.78 & 77.78 & 77.78 & 90.90 & 84.30 & 83.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The classification accuracy provided by the k-NN classifier after applying the four PPFS, WOA, PSO, and GA feature selection.
## 2 Principles of Underwater Communication
### Communication Mediums in Water
#### 2.1.1 Underwater Acoustic Communication (UAC)
The effects of multipath propagation and Doppler shifts can be eliminated by channel equalization and adaptive modulation techniques. Also, error correction coding can be utilized to enhance data reliability [16].
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach
### Population Size  
The population size refers to the number of individuals in each iteration and must be determined based on the extent of the search area. A larger population size results in a more precise search of the solution space, but a too large population can lead to divergence.
## 3 Proposed System
### Data Acquisition and Analysis  
We tested the proof-of-concept device with high-frequency signals. As Figure 4 shows in a schematic of the experimental condition, mechanical vibration transferred through the hand is captured by the piezo sensor attached on top of a smartphone. For the actuation signals, a set of rectangular waves was adopted, with frequencies of 18 kHz to 20 kHz at increments of 0.5 kHz. Each set of  
Figure 3: Internal view of the proposed system. A surface transducer is firmly mounted to the custom Galaxy Gear I housing.  
Figure 2: (**a**) The amplifier (left) and the surface transducer (right) used for the system; (**b**) a close view of the surface transducer.  
signals for driving the surface transducer was generated at the rate of 48 kHz using the data acquisition device. The voltage amplitude of the rectangular waveform was set to 0.5 V, and the measured average power consumption of the surface transducer was approximately 200 mW. The signals transmitted to the smartphone were collected at a rate of 96 kHz using a referenced single-ended (RSE) measurement of the data acquisition device. Analysis of the collected signals in the frequency domain was performed in LabView-based software.
## 4 An Example
the aforementioned approach, chord rotation at yielding is \(\theta_{y}=0.01112\) radians, while ultimate chord rotation is equal to \(\theta_{u}=0.03117\) radians. The corresponding downward displacements are \(\delta_{y}=44\) mm and \(\delta_{u}=125\) mm. 5. The bilinear compliance curve is derived from the data previously obtained. It results \(F_{y}=283.5\) kN and \(F_{u}=308.5\) kN. Thus, the tangent stiffnesses in the elastic and the hardening phase are computed through Equations (7), resulting in \(k_{e}=\)6,443,181 N/m and \(k_{p}=\)311,482 N/m. 6. the displacement due to a quasi-static column removal process was computed through Equation (8) as \(u_{st}=28.4\) mm. The displacement at yielding to be inserted in the dimensionless quantities computed at the following point is \(u_{y}=\delta_{y}=44\) mm. 7. The dimensionless quantities were computed according to Section 2.2 as \[\Pi_{2}=\frac{N}{F_{y}}=0.640\qquad\Pi_{3}=\frac{k_{e}}{k_{p}}=0.049\] (11) from which \(\Pi_{1}^{dyn}=1.379\) is computed. Referring to the dimensionless static displacement, it results \(\Pi_{1}^{st}=0.640\). 8. The dynamic amplification factor is \[\text{DAF}=\frac{1.379}{0.640}=2.155,\] (12) from which it results \(u_{dyn}=61.3\) mm.
# HYDRUS-1D Simulation of Nitrogen Dynamics in Rainfed Sweet Corn Production
## 3 Results and Discussion
### Nitrogen Loss Due to Surface Runoff  
The comparison between the simulated and observed N runoff losses during two seasons is shown in Figure 9. The simulated runoff values were determined considering the N concentrations at the surface observation point that represents runoff concentration. The observed amount of NO\({}_{3}\)\({}^{-}\)-N and NH\({}_{4}\)\({}^{+}\)-N lost through runoff were 29.6 kg/ha and 10.2 kg/ha, 14.8 kg/ha and 8.6 kg/ha during the first season and second seasons, respectively. The cumulative observed runoff loss was 39.8 kg/ha (first season) and 23.4 kg/ha (second season). The simulated runoff loss was 42.4 kg/ha (first season) and 26.7 kg/ha (second season). As the hydraulic conductivity of heavy textured soils is low, these high runoffs were expected under high-intensity rainfall. Few intense rainfall events during the first season caused the high N loss though the overall amount of rainfall was relatively less during the first season. The N loss due to runoff that occurred immediately after fertilizer application was significantly high as compared to subsequent runoff losses. However, the rate of loss through immediate runoff also vary among fertilizer applications and between two seasons. The variation in the rate of loss depends on the time gap between fertilizer application and rainfall occurrence and other factors that control the runoff, i.e., the antecedent moisture level, duration, and intensity of rainfall. [PERSON] and [PERSON] [37] observed a significant increase in runoff generation chances when rainfall amount exceeded 10 mm during the study period.  
Figure 8: Comparison of simulated (Sim.) and observed (Obs.) cumulative nitrogen uptake during the (**a**) first season and (**b**) second season.
# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 4 Data Description and Pre-Processing  
### Data Descriptions  
The experimental data were obtained from the PHM 2012 Challenge dataset, which was collected on the PRONOSTIA accelerated aging test bed [27]. The structure of the test rig is shown in Figure 7, with synchronized motors, double pulleys, a pneumatic jack, and two accelerometers, model 3035B DYTRAN. The accelerometers were mounted at the horizontal and vertical positions of the bearings to obtain the vibration signals. The data from bearing 1_1 was chosen as the test data set since it exhibited significant degradation behavior. It was subjected to a run-to-failure test under a 4000 N radial load and an 1800 r/min rotational speed, and a total of 2560 samples were recorded with a sampling frequency of 25.6 kHz/10 s. The signals from the horizontal accelerometers will be used  
Figure 6: Flowchart of improved BiLSTM model.  
for the prediction of the bearing's health state in the next step by truncating the publicly available data set bearing 1_1. The last 1004 sets of data from the public dataset Bearing1_1 are used as the test data, with 2560 data points in each set.  
The vibration signal of bearing 1_1 is shown in Figure 8. At the beginning, the vibration amplitude of the vibration signal is low and steady, indicating that the bearing is in normal working condition. In the middle of the cycle, the amplitude of the signal waveform gradually increases dramatically, indicating that the bearing starts to show initial wear, and finally, the amplitude of the signal waveform starts to change dramatically, indicating that the bearing has failed.
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization
#### 4.1.5 Feedback and Update  
The actual flight state of the UAV is fed back to the MPC in real time. Using this updated state information, the MPC refines the system model and predictions, subsequently adjusting the control input for the next time instant.  
The UAV's flight is divided into five distinct phases: takeoff, climb, cruise, descent, and landing. The MPC algorithm is applied individually to each phase, with tailored configurations including state matrices, input matrices, state weight matrices, terminal state weight matrices, input weight matrices, prediction horizons, and prediction intervals. Upon completing the MPC process, the output is the optimized electrical power consumption, ensuring efficient energy management across all flight phases.
## 3 Calculation of the GDOP Based on the UVM  
The values of \(f_{t}\), \(g_{t}\), and \(\rho_{t}\) are recorded when the iterative process of the UVM converges. It is assumed that \(f_{t}\), \(g_{t}\), and \(\rho_{t}\) are recorded values and are independent of \(\mathbf{X}\). At this point, \(\mathbf{X}\) is the analytic form of the measured data \(\theta\).  
It is assumed that the mean of \(\theta\) is \(\mu\), and that the variance of each measurement is \(\sigma^{2}\). \(\mathbf{X}(\theta)\) can be expanded at \(\mu\), and the linear part is maintained as:  
\[\mathbf{X}(\theta)\approx\mathbf{X}(\mu)+\frac{\partial\mathbf{X}(\mu)}{\partial\mu}( \theta-\mu) \tag{8}\]  
The covariance matrix of \(\mathbf{X}\) is:  
\[\begin{array}{l}\sigma_{X}^{2}=E\Big{[}(\mathbf{X}(\theta)-\mathbf{X}(\mu))(\mathbf{X}( \theta)-\mathbf{X}(\mu))^{\mathrm{T}}\Big{]}\\ \qquad\qquad=\sigma^{2}\Big{(}\frac{\partial\mathbf{X}(\mu)}{\partial\mu}\Big{)} \Big{(}\frac{\partial\mathbf{X}(\mu)}{\partial\mu}\Big{)}^{\mathrm{T}}\end{array} \tag{9}\]  
The GDOP can be expressed as [19]:  
\[\mathrm{GDOP}(\mu)=\sigma\sqrt{\mathrm{tr}\Bigg{(}\bigg{(}\frac{\partial\mathbf{X }(\mu)}{\partial\mu}\bigg{)}\bigg{(}\frac{\partial\mathbf{X}(\mu)}{\partial\mu} \bigg{)}^{\mathrm{T}}\Bigg{)}} \tag{10}\]  
Data obtained from each measurement included errors. It is necessary to calculate Equation (10) with the measurement data with errors as \(\mu\). The problem of discontinuity in the function of GDOP varying with the observation geometry arises when calculating Equation (10) with data containing errors.  
It is worth noting that the measurement error conforms to the Gaussian distribution. The measurement data without errors \(\mu_{0}\) can be expressed as:  
\[\mu_{0}=\mathrm{E}(\mu) \tag{11}\]  
GDOP's expectations can be expressed as:  
\[\mathrm{E}[\mathrm{GDOP}(\mu)]=\mathrm{GDOP}(\mu_{0}) \tag{12}\]  
To show the variation characteristics of GDOP with observation geometry, the accuracy of the orbital solution will be represented by the GDOP calculated from the data without errors.
## 4 QoS and QoE
### QoS and QoE in ICN  
In addition to the traditional metrics, other QoS and QoE factors need to be considered in ICN to account for the new features it introduces. For instance, cache hit ratio, server hit rate, cache replacement rate, and cache utilization are some of the QoS and QoE metrics associated with ICN's in-network caching. Similarly, the metrics associated with ICN's name resolution, forwarding, and routing include data delivery rate, path discovery time, and routing success rate. Furthermore, ICN media-streaming performance for distributed content can be assessed by source switching delay. The commonly used QoS and QoE metrics in traditional networks and ICN are shown in Figure 2.
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 5 Results: Algorithm Verification Using On-Orbit Data
### Geolocation of the Example Reflection Tracks  
This CYGNSS data set was processed using a custom ground based software receiver, where the algorithms can be validated using a snap shot of real on-orbit signals. Two example signals were selected to demonstrate the proposed tracking algorithm. These reflection tracks are located off the north-west coast of Australia as indicated in Figure 3a. The reflection tracks for GPS transmitters were detected for pseudo random noise (PRN) codes 26 and 21. The reflection track for PRN 21 transitions from ocean to land at approximately second 43 of the raw data collection.  
Figure 3b shows the signal to noise (SNR) values for each of the two example signals over the duration of the data set (calculated as described in [29]). The ocean reflection SNRs are both similar and relatively consistent (with slight fluctuation due to surface conditions and geometry) while the land reflections for PRN 21 clearly show a jump in the SNR as the signal is reflected from relatively flat dry land. The ground processed code phase delay and Doppler of the reflected signal maximum was logged over the duration of the track and was used as a truth reference for the reflected signal time and Doppler parameters versus time. Additionally, the direct signal for each track was tracked on the navigation channel over the 60 s interval using established GPS signal acquisition and tracking techniques.
# Pothole Classification Model Using Edge Detection in Road Image
## 2 Related Work
### Pothole Prediction Technology Trends in Road Damage  
In road damage, potholes are caused by road aging, climate change, and traffic volume. This is a major cause of tire damage in cars driving on the road, vehicle damage, traffic accidents, and traffic jams. In addition, it is not easy to find a pothole at night, which slows the driver's response. Therefore, the severity of traffic accidents increases. Accordingly, a method capable of maintenance through continuous monitoring is required. The pothole detection includes vibration method [26], laser scanning method [27], video processing and image method [15; 28]. The vibration method is a method of detecting a pothole by analyzing a pattern of the degree of shaking occurring when a vehicle passes through the pothole. This allows the system to be built at a low cost. However, the shaking occurs even when passing through speed bumps, manholes, and obstacles, so the accuracy of awareness of potholes is low [26]. The laser scanning method is a method of finding a pothole by detecting the shape of a road surface using a laser scanner. This is higher in accuracy than the vibration method, but its scalability is low because it costs a lot [27]. In addition, the video recognition method can quickly detect a wide area at a low cost. Potholes have cracks, darkness, and features that stand out from the surroundings  
Figure 1: Image object detection process.  
compared to flat road surfaces. However, there is a need for an algorithm with excellent performance that can accurately recognize videos with noise such as video quality and texture [28; 29]. As a result, methods and techniques for detecting potholes using deep learning have been developed in recent years. In Korea, [PERSON] et al. [29] proposed a Saliency Map based algorithm to improve the accuracy of pothole detection. The proposed algorithm is composed of a pothole candidate extraction section and a decision section based on Saliency Map. Saliency Map is an algorithm that finds a region that is more visible than the surroundings in an image [29; 30]. The candidate extraction section separates the suspected pothole region from the road surface and creates it as a candidate region. The decision section determines whether the region extracted as a candidate is an actual pothole. Thus, the feature of the pothole on the road surface can be extracted and detected. In foreign countries, [PERSON] et al. [31] developed a road damage detection system using big data analysis of multiple sensors to monitor the road damage conditions such as potholes. This is a method of detecting road damage using time and frequency. The data set for this capture the data using accelerometers, cameras and recording devices on the mobile. The Morlet parent algorithm and STFT (Short-Time Fast Fourier Transform) algorithm commonly used in the field of audio recognition are used for the collected data set to find the frequency through accelerated signal processing to find the signal frequency of the damaged road. Thus, road damage can be detected at the frequency found.