esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
As described in Figure 3, the knee adduction moment was higher in the older group by 12.5 Nm as the main effect (F\({}_{1,\,36}\) = 12.80, \(p\)\(<\) 0.01). Limb effect indicated that knee adduction moment in the dominant limb was more variable than the non-dominant side by approximately 3 Nm (F\({}_{1,\,36}\) = 6.13, \(p\)\(<\) 0.05).  
Figure 3: Knee joint kinetics: adduction moment (**up**) and vertical force (**bottom**); Dom/Non = dominant/ non-dominant limb’s values; a = significant ageing effect, l = significant limb effect.  
Vertical knee joint force was also 91.5 N greater in the older group (F\({}_{1,\,36}=6.25,\,p<0.05\)).  
In Figure 4, ageing effects were observed in 0.034 s longer time to foot flat (F\({}_{1,\,36}=7.09,\,p<0.01\)) and 3.45\({}^{\circ}\) less toe-out angle (F\({}_{1,\,36}=7.56,\,p<0.01\)). No other statistical effects were identified.  
As described in Figure 5, statistical significance was obtained only in the ageing effect on larger step width by 0.034 m (F\({}_{1,\,36}=13.32,\,p<0.01\)).  
Table 1 presents correlations between knee kinetics and other examined gait parameters. High knee adduction moment was found to accompany greater variability, while the variability of knee joint force was negatively associated with increased adduction moment. Knee force and adduction moment demonstrated inverse correlations but this effect was qualified only in the young group. For foot contact angle, positive correlations were seen with knee adduction moment in the older group. Toe-out angle showed positive correlations with knee adduction moment only in the older group.  
Correlation patterns with spatio-temporal parameters demonstrated that increased step length was interlinked with reduced knee force. Step length variability was negatively correlated with knee adduction moment. Increased step width in the older group was linked with lower knee adduction moment. Furthermore, step length was positively associated with foot contact angle (young: r = 0.454, \(p<0.01\), older: r = 0.419, \(p<0.05\)) and negatively with double support time (young: r = -0.397, \(p<0.05\), older: r = -0.535, \(p<0.01\)). Toe-out angle was negatively correlated with step width for the young (r = -0.650, \(p<0.01\)) and older group (r = -0.493, \(p<0.01\)). Increased toe-out angle was further correlated with COP movement (young r = 0.425, \(p<0.01\); older r = 0.707, \(p<0.01\)).  
Figure 4: Foot Motion: (**top left**) foot contact angle; (**bottom left**) centre of pressure displacement; (**top right**) toe-out angle; (**bottom right**) time to foot flat, mean/SD; Dom/Non = dominant/non-dominant limb’s values; a = significant ageing effect, l = significant limb effect.  
Figure 5: Spatio-temporal parameters. (**top**) step length; (**middle**) step width; (**bottom**) double support time, mean/SD; Dom/Non = dominant/non-dominant limb’s values; a = significant ageing effect, 1 = significant limb effect.
###### Abstract  
Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.  
spectrum sensing; N-out-of-K rule; heterogeneous cognitive radio networks +
Footnote †: journal: Applied Sciences  
15 February 2021  
Accepted: 24 March 2021  
Submitted: 30 March 2021
## 2 Materials and Methods  
This section describes two experiments conducted in the precision farming scenario within the Robot Fleets for Highly Effective Agriculture and Forestry Management (RHEA) project. The first experiment tested two localization techniques for locating a human operator in a dynamic and unstructured environment based on GNSS and RSSI. The second experiment evaluated an RTK-based positioning system deployed on each intelligent vehicle.
# Time-Domain Dynamic Modeling and Analysis of Complex Heavy-Duty Gearbox Considering Floating Effect
## 3 Dynamic Modeling of a WTG Test Rig
### Structure of the Full Test Rig  
The 2 MW WTG modeled is depicted in Figure 4. It primarily consists of three gear sets: a low-speed planetary gear set (LSP), a high-speed planetary gear set (HSP) and a parallel-shaft helical gear set (PSH). To reach the design objective of power dividing, the LSP had four transverse fixed planet gears and a floating sun gear, while the HSP had a fixed ring gear and a floating carrier with three planet gears affixed with bearings. The wind field excitation acted on the ring gear of the LSP, and the grid load from the electric generator acted on the output axis of the PSH. The intricate dynamics of the entire floating component, including the sun gear from the LSP, the carrier and three planet gears from the HSP, made this WTG quite distinct from common designed ones.  
Figure 5 shows the 3D geometry model of the entire test rig. Part 1 is a loading motor, the speed of which was controlled, and part 2 is an auxiliary reduction gearbox. A combination of the two was utilized to imitate the wind input. Part 5 is a loading generator, the torque of which was controlled. Part 4 is the tested 2 MW WTG, which is connected with Part 2 through a universal coupling and with Part 5 through a flexible flange.
## 5 Model Architectures and Training
### Model Training and Optimization
#### 5.2.1 Model Training  
As elaborated earlier, the investigated models typically require a major amount of computational resources. Considering a future in-field application on a low-power edge device, the authors place focus on the conflicting goals of achieving maximum predictive performance, while reducing model size and the number of MAC operations to a minimum.  
For training in general, the well-known ADAptive Moment estimation (ADAM) optimizer [55] is used, since it is proven to be effective in training models that utilize convolutional and attention-based mechanisms [49; 55; 56]. In terms of the learning rate, an initial value of \(\eta=3e-4\) is set. Further, cosine annealing learning rate scheduling, as proposed by [PERSON] and [PERSON] [57], is implemented, since it showed satisfying performance within previous studies in the field [54]. All models are trained for a maximum of 200 epochs; however, in order to prevent overfitting, early stopping is applied as soon as the validation error is converging, even though the training error is further decreasing.  
Opposed to the generally improved convergence behavior of large batch sizes during stochastic gradient descent (SGD) optimization [58], initial experiments indicated that an increase in the batch size did not lead to significant improvements using the ADAM optimizer, except in the case of the ResNet3D model. Consequentially, the batch size is only fine-tuned for this model, whereas it is kept to a constant value of \(\mathrm{N_{batch}}=1\) for all other architectures. This increases training time; however, considering limited computational resources (see Appendix C), it allows for exploration of larger model architectures during structural optimization, while complying with GPU memory restrictions.  
Additionally, it was found that the performance of the models improves with longer image time series M, which aligns with the intuition that longer observation periods of the measurement noise lead to improved model performance. Since the model performance showed no significant improvement for longer sequences, a constant input number of M = 200 images is utilized, as described earlier.  
As loss metric for the respective regression heads, the common mean squared error (MSE) between predicted precipitation rate \(R_{\mathrm{pred},i}\) and the respective measured value \(R_{\mathrm{meas},i}\) is used:  
\[\mathcal{L}_{\mathrm{MSE}}=\frac{1}{\mathrm{N_{batch}}}\sum_{i=1}^{\mathrm{ N_{batch}}}\Big{(}R_{\mathrm{pred},i}-R_{\mathrm{meas},i}\Big{)}^{2}. \tag{16}\]  
whereas for the classification heads, the widely used cross-entropy loss function [59] is implemented:  
\[\mathcal{L}_{\mathrm{CE}}=-\sum_{c=1}^{\mathrm{N_{SYNQP}}=15}y_{o,c}\log(p_{o,c}) \tag{17}\]  
with \(y\) as binary indicator (0 or 1) if class label \(c\) is the correct classification for observation \(o\), and \(p\) is the predicted probability that observation \(o\) is of class \(c\).
## 5 Discussion  
### Comparison with Other Fault Location Methods  
As more and more feeder terminal devices are applied in power distribution networks, scholars from different countries have proposed some fault location methods based on the fault information collected by FTUs. In addition, complex fault conditions in power distribution networks have put forward higher requirements for the research of fault location. Whether the faulted section can be effectively located in various complex situations has become an important criterion for testing a fault location method.  
As shown in Table 10, the fault location method proposed in this paper is compared with other methods in various complex conditions, and all of the methods taken into comparison use fault information collected by FTUs. It is necessary to be mentioned in advance that if the judgment conditions of the methods do not need to be changed artificially when the topology of distribution network changes, the method can be considered that it is suitable for different topologies automatically. Then, we set a \"\(\surd\)\" for this method in Table 10. If the faulted section can be effectively located when part of the information is lost, we set a \"\(\surd\)\" for this method. If the method can detect and report the equipment with information loss, we set a \"\(\surd\)\" for this method. The comparison result indicates that the proposed method has the following advantages at the same time:  
1. The proposed fault location method is not only applicable for single fault, but also for multiple faults at the same time.
2. The simulation results show that the proposed method can also effectively locate the fault point when there are distributed generations in the distribution network.
3. The line list proposed in the method can be updated automatically when the topology of distribution network changes, so it is suitable for the distribution networks with the topology structure changes frequently.
4. The information loss detection algorithm can report the fault-indicating equipment with information loss and correct the wrong status of equipment in the line list, which greatly improves the accuracy of the fault location results. Moreover, with the help of this feature, if a piece of equipment often loses information, this indicates that the equipment may need to be repaired.  
In summary, the fault location method proposed in this paper is applicable for solving complex fault conditions in power distribution networks.
## 3 Results
### Subclinical Levels of Dysphonia  
AVQI and CPPS data for the different subclinical levels of dysphonia are provided in Table 5. As more judges rated G as 1 (i.e., slight dysphonia), the higher mean AVQI values were observed. A significant difference was found between the true normophonic cohort and the second subclinical dysphonia cohort (Figure 3a, U = 392.00, p = 0.028). For CPPS\({}_{\text{vowel}}\), a declining trend was observed with a significant difference between the true normophonic and the second subclinical dysphonia cohort (Figure 3b, U = 422.00, p = 0.048). For CPPS\({}_{\text{sentences}}\) and CPPS\({}_{\text{combination}}\) no consistent trend was observed (Figure 3c,d, respectively). Comparisons with the third subclinical cohort were not conducted since this cohort included only three subjects and the statistical power consequently would be too low.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normophonic** & **Subclinical** & **Statistic** & **Sign.** \\ \hline \multirow{3}{*}{**AVQI**} & N = 83 & N = 40 & \multirow{3}{*}{Mann-Whitney U = 1303.5} & \multirow{3}{*}{p = 0.054} \\  & Mean = 2.48 & Mean = 2.81 & SD = 0.86 & \\ \hline \multirow{3}{*}{**CPPS vowel**} & N = 83 & N = 40 & \multirow{3}{*}{Mann-Whitney U = 1385} & \multirow{3}{*}{p = 0.138} \\  & Mean = 15.86 & & & \\ \cline{1-1}  & SD = 2.51 & SD = 2.79 & & \\ \hline \multirow{3}{*}{**CPPS sentences**} & N = 83 & N = 40 & \multirow{3}{*}{Mann-Whitney U = 1573} & \multirow{3}{*}{p = 0.64} \\  & Mean = 7.45 & Mean = 7.48 & & \\ \cline{1-1}  & SD = 0.77 & SD = 1.10 & & \\ \hline \multirow{3}{*}{**CPPS combination**} & N = 83 & N = 40 & \multirow{3}{*}{Mann-Whitney U = 1421.5} & \multirow{3}{*}{p = 0.198} \\ \cline{1-1}  & Mean = 14.27 & Mean = 13.80 & & \\ \cline{1-1}  & SD = 1.82 & SD = 2.18 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of mean AVQI and CPPS values of subjects with normal versus subclinical dysphonic voice quality with corresponding significance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**N Judges G = 1**} & **AVQI** & **CPPS Vowel** & **CPPS Sentences** & **CPPS Combination** \\ \hline \multirow{3}{*}{**0**} & N = 83 & N = 83 & N = 83 & N = 83 \\  & Mean = 2.48 & Mean = 15.86 & Mean = 7.45 & Mean = 14.27 \\  & SD = 0.78 & SD = 2.51 & SD = 0.77 & SD = 1.82 \\ \hline \multirow{3}{*}{**1**} & N = 22 & N = 22 & N = 22 & N = 22 \\  & Mean = 2.57 & Mean = 15.75 & Mean = 7.60 & Mean = 14.29 \\  & SD = 0.75 & SD = 2.78 & SD = 1.16 & SD = 2.82 \\ \hline \multirow{3}{*}{**2**} & N = 15 & N = 15 & N = 15 & N = 15 \\  & Mean = 3.03 & Mean = 14.52 & Mean = 7.32 & Mean = 13.38 \\ \cline{1-1}  & SD = 0.93 & SD = 2.57 & SD = 0.99 & SD = 1.87 \\ \hline \multirow{3}{*}{**3**} & N = 3 & N = 3 & N = 3 & N = 3 \\ \cline{1-1}  & Mean = 3.56 & Mean = 13.40 & Mean = 7.34 & Mean = 12.33 \\ \cline{1-1}  & SD = 0.80 & SD = 3.63 & SD = 1.50 & SD = 3.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Differences in AVQI and CPPS between subclinical levels of dysphonia.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
###### Abstract  
Grouted connections are commonly used in marine engineering, especially on oil platforms, cross-sea bridges, and offshore wind power turbines. The prediction methods for axial carrying capacity of grouted connections with shear keys and their application ranges in current codes were analyzed in this paper. The calculated results by using different codes were compared based on a practical grouted connection between steel piles and the jacket foundation of a wind turbine. The research team conducted axial compression tests on seven specimens, collected a wide range of experimental results to establish a database, and finally compared the standard calculation results with the experimental results. The study indicates that the axial strength of grouted connections predicted by different methods is distinct. The calculation formula of the British Health and Safety Executive (HSE, 2002) has obvious limitations; specifically, with increased shear keys, strength is overestimated, resulting in insecure design outcome of structures. The results calculated by the Norwegian Det Norske Veritas (DNV, 2013) are generally consistent with the experimental results, in which the reduction effect of multiple shear keys was considered. The prediction method of the American Petroleum Institute (API, 2007), which undervalues the bearing performance of connections, is excessively conservative. The method of the combined Norwegian and German Det Norske Veritas-Germanischer Lloyd (DNV-GL, 2016) has wider applicability and is safe, reliable, and economical.  
grauted connection; shear key; axial carrying capacity; comparison between codes; experimental result; predicate method +
Footnote †: journal: Applied Sciences
# Recent Advances in Ultrafast Structural Techniques
## 2 Structural Techniques Involving Ultrashort X-ray Pulses
### Introduction to Fourth-Generation Fs-Hard X-ray Sources: X-ray Free Electron Lasers
#### 2.4.2 Time-Resolved Fs-X-ray Crystallography and Scattering
There are clear structural changes in the vicinity of the heme. (**b–d**) Difference electron density maps showing bound (-3\(\sigma\) contour level, red) and photodissociated (+3\(\sigma\), contour level green) CO, doming of the heme, out-of-plane movement of the iron, and concomitant movement of His93 away from the heme, as well as rotation of His64 and movement of Phe43, which is displaced in different directions at \(t\) = +0.5 ps and \(t\) = +10 ps. The orientation in (**b**) is rotated by 90\({}^{\circ}\) with respect to the orientation in (**c**) and (**d**). Figure adapted from Reference [107] with permission from Science AAAS. Right panel: Time-dependent changes in wide-angle X-ray scattering (WAXS) data recorded from detergent-solubilized samples of RCvir. (**e**) Experimental setup illustrating the microjet of solubilized RCvir (green), X-ray detector, XFEL beam (orange) and 800-nm pump laser (red). (**f**) Time-resolved WAXS structure factor difference, \(\Delta\)S(q, \(t\)) = Slight(q, \(t\)) - Sdark(q), recorded as a function of \(t\). Linear sums of the four basis spectra (red) shown in (h) are superimposed upon the experimental difference data (black). (**g**) Time-dependent amplitudes of the components C1–C4 used to extract the basis spectra. (**h**) Basis spectra extracted from the experimental data by spectral decomposition. Figure adapted from Reference [113] with permission from Springer Nature. the universal threshold for plasma formation (\(\sim\)1 J cm\({}^{-2}\)) [127]. Figure 9g depicts the separation of TR-WAXS data (Figure 9f) into their major components by spectral decomposition. The extracted spectral components are presented in Figure 9h. C1 is an ultrafast component, C2 is a protein component that displays the expected oscillatory features, and C3 and C4 are components that relate to heating effects. Arnlund et al. [113] concluded that XFELs facilitate the direct visualization of ultrafast protein motions. However, it should be noted that the interpretation of TR-WAXS data from solution phase requires the assistance of calculated structure factors from molecular dynamics (MD) simulations [128; 129]. Moreover, depending on the solute under investigation the input of quantum mechanically optimized geometries [129; 130] may be necessary. Time-resolved X-ray absorption spectroscopy (TR-XAS) is another approach for the study of molecular dynamics in solution with great sensitivity to structural changes.
## 3 Experiment and Discussion
### Initial Pixel Alignment of the DOPC System and Initial Conjugate Phase Acquisition  
#### 3.1.1 Initial Conjugate Phase Acquisition  
The experimental DOPC system is shown in Figure 2. The illumination beam (632.8 nm) was split into two beams by BS1: an object beam and a reference beam. A1 and A2 were used to adjust the interference field to the appropriate intensity. A half-wave plate (H) was used to adjust the polarization to a horizontal direction to ensure the maximum modulation efficiency of the SLM (Holoeye, PLUTO-VIS-016). L2, L3 and L5, L6 are aspherical lenses that were used to expand the reference beam and the object beam to the appropriate size. The diameters were 24 and 4 mm, respectively. In the object beam, a scattering medium (Thorlabs, DG-220) was placed in the back of the microscope objective (MO1, 20X, NA = 0.4), and the scattering field was collected by MO2 (20X, NA = 0.4). The MO3 (20X, NA = 0.4) and the hole were used to expand and filter the object beam. The reference beam passed through a quarter-wave plate to achieve phase shift holography. The scattering field was interfered with by the reference beam by BS3. The SLM plane was directly imaged onto the CCD1 (Teledyne DALSA, G2-GC10-T4095) target by L8. Additionally, the interference intensity on the SLM was recorded by CCD1. Phase measurement of the scattering field was realized by the two-step digital phase shift holography using a quarter-wave plate (Q), and the conjugate phase of the scattering field can be expressed by ([40])  
\[\varphi_{0}=\tan^{-1}\left[\left(\frac{I\left(x,y,\,\overline{z}\right)-I_{ref }-I_{obj}\right)}{I\left(x,y,0\right)-I_{ref}-I_{obj}}\right]\right]^{*},\]  
where \({}^{*}\) represents the complex conjugate. In the playback step, after adding the conjugate phase to the SLM, the conjugate beam propagated to BS2 and was focused. The final focus field was recorded by CCD2 (IMPERX, GEV-B2021M-TF000). Initial pixel alignment of the DOPC system is described in detail in Section 3.1.2.
## 4 A Survey on Cognitive Radio Implementation Research
### Adaptrum WSD device  
Adaptrum Inc.'s Cognitive Radio Platform is an integrated hardware and software development system that has been designed for TV white space operation on UHF television channels 21-51 (512 MHz-698 MHz). The system is capable of various forms of TV signal sensing including waveform/signature sensing, spectral identification, signal power estimation, and network-level cooperative sensing. It detects both analog and digital TV signals. The system is also capable of signal transmission in the TV bands with flexible waveform, modulation and signal bandwidth construction. It incorporates transmit power control and chain linearization to reduce adjacent channel interference. The maximum transmitter output power specification is 100 mW (+20 dBm) over the selected bandwidth [113; 114].  
Key components of the development platform include a wide-band high dynamic-range RF transceiver operating over the frequency range 400-1000 MHz and an FPGA-based hardware development board with integrated high-speed ADCs and a high-density FPGA where the baseband and protocol-layer functions can be implemented. The software design is based on Matlab-based integrated developmentenvironment (IDE) where CR hardware functions are controlled using Matlab GUI and Matlab scripts. Figure 22 shows a lab picture of the prototype system which includes the RF transceiver board and the FPGA board [112, 113, 114].  
In the 2008 FCC White Space Device testing, Adaptrum demonstrated its CR prototype system, which is capable of reliably sensing ATSC and NTSC signals at very low detection threshold [112, 114]. More details can be found in [113].