esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the dynamics modeling and simulation of the Wave Glider with different connection types were studied. The effect of the different connection types on the dynamic performance of the Wave Glider were analyzed by using the multi-body dynamics software ADAMS. According to dynamic response, the following conclusions were drawn:  
1. When the Wave Glider respectively connected by rigid rod, multi-link and elastic rod, the displacement responses are similar and the difference is not obvious among different connection types. The motion of vessel with the cable is slight fast.
2. The velocity responses of the four types of connection structures are also similar; the mean velocity of the Wave Glider with cable connection is faster than rigid rod, elastic rod and multi-link. And the vessel with cable also has smaller fluctuation speed.
3. The connection types have little effect on the horizontal pull force of the hinged point between the connection structure and the surface vessel. Serious impacts can suddenly occur with connection of rigid rod and multi-link, which will lead to serious extra load on the structure of Wave Glider. Due to the elastic rod and the connection cable have a certain damping effect, the sudden impacts on the pull force of the hinged point is small. Overall, the cable connection has the smallest suddenly impacts of the pull force and the best performance.  
Conceptualization, Z.C.; Investigation, P.W.; Methodology, Z.F., Z.C. and Z.Z.; Resources, P.W.; Software, Z.F. and Z.Z.; Supervision, Z.C., Z.Z. and P.W.; Writing--original draft, Z.F.; Writing--review & editing, Z.F. and Z.C.  
This research was funded by the National Natural Science Foundation of China, grant number 51875540; The Fundamental Research Funds for the Central Universities, grant number 201513053.  
The authors are grateful for the financial support of National Natural Science Foundation of China (No. ), The Fundamental Research Funds for the Central Universities (No. ) and Key Laboratory of Mechanism Theory and Equipment, Design of Ministry of Education (No. )  
The authors declare no conflict of interest.
# Statistical Modeling of Wall Roughness and Its Influence on NLOS VLC Channels in Underground Mining
## 3 Simulation Results
### First VLC Simulation Position
#### 3.1.3 Third Case  
In this case, the POW within the NLOS component varies depending on the distribution used. Consequently, the geometry of the wall is influenced by the selected distribution, which subsequently affects the link performance. Specifically, the wall configuration can result in a greater or lesser loss of signal.  
Then, the power received for UNID (Figure 8) has a pattern that shows a more gradual and dispersed behavior. In this case, the irregularity is more diverse and can produce a greater power loss at some points. In EXPD (Figure 8), the radiation appears to be more  
Figure 6: Channel impulse responses for different reflection coefficients statistical distributions. (**a**) CIR using UNID; (**b**) CIR using EXPD; (**c**) CIR using GAMD.  
concentrated, and the power gradient decreases more rapidly. The distribution tends towards low values; then the cosine of angles may approach zero or similar.  
Finally, with GAMD (Figure 8), the dispersion is even more focused in a specific region, indicating greater variability in NLOS power as a function of the angles generated by this distribution. In general, GAMD is observed to produce the most focused radiation pattern while showing greater dispersion.  
GAMD has similar behavior to EXPD, but the first admits cases with low angle values with low probability. However, compared to Figure 8, the angles generate a higher received power. When the CIR is observed across the three cases, significant differences in the peak amplitude and temporal behavior are evident. In UNID (Figure 9), the CIR is similar to the previous case. Once again, UNID provides the highest channel gain, with relatively weak reflections.  
In EXPD (Figure 9), CIR is similar to UNID, but the main peak occurs a few nanoseconds later. In this case, the second peak is less intense. For GAMD (Figure 9), although the position of the main peak is similar, its amplitude is considerably lower. This indicates a reduction in the contribution of diffuse reflections and a greater temporal concentration of the received energy. Although the angles provide more gain to the link, they remain less powerful than the other distributions. Moreover, the geometry displayed with GAMD implies a larger roughness.  
The tunnel sidewall can, in some cases, reflect light rays but will not impact PD'S FOV or the angle of the received signal makes a bigger loss in terms of power.  
Figure 7: Probability density functions for different reflection coefficients. (**a**) PDF using UNID. (**b**) PDF using GAMD. (**c**) PDF using EXPD.
## 4 Discussion
### Limitations  
In the present study, it is noteworthy that marker trajectories and surface electromyographic data were not collected to simplify the design by focusing on the foot loading and mimicking basketball maneuvers by limiting the experimental devices that were attached to the participants. However, future studies including electromyography results with different sports shoes in basketball players should be carried out in order to determine muscle activity [19; 20; 34]. Besides, it is considered that different trajectories performed by players could influence the trial times, as well as the impact on shoes [35].
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 3 Operator's Dilemma in the Unlicensed Spectrum
### Infinite Repeated Game in the Unlicensed Spectrum
#### 3.4.3 Tit for Tat between Two LTE Operators  
When LTE operators in the unlicensed spectrum regard \(U_{w}\) not as zero, but as a negative value because of delay-sensitive applications and users, \(U_{sh}~{}>~{}(U_{s}+~{}U_{w})/2\) induces them to use many different strategies in an infinite or unknown length game. Perhaps LTE operators want to know what kind of strategies will produce the highest possible utility.  
According to Proposition 1 in [27], if \(\delta\) is sufficiently large, no best decision rule exists independently of the strategy being used by the other player in the iterated prisoners' dilemma. However, when we apply this statement to the operator' dilemma, it does not necessarily mean that this dilemma cannot be resolved or that operators are always tempted to cheat in order to maximize their utility. Although there is no one best strategy, _tit for tat_ (TFT) may be an effective strategy for two LTE operators in the co-channel of the unlicensed spectrum. TFT is a decision rule of cooperating at the first stage, and then always repeating what the other operator has done at the previous stage. TFT was first introduced as a strategy by [PERSON] in [PERSON]'s two tournaments [27]. Interestingly, in the first and second rounds, TFT was the most successful strategy by getting a higher cumulative payoff in the entire round robin tournament. Although professional game theorists and researchers in many different fields were invited to submit their favorite strategies for computer tournaments, the winner was the simplest strategy: the TFT strategy. The reasons for TFT's victory in these tournaments were analyzed as follows: (1) cooperation as long as the other player cooperates by avoiding unnecessary conflict; (2) retaliation for unexpected defection by the other; (3) forgiveness by cooperating with the other in spite of historical background; (4) clarity for the other to learn the TFT's behavior pattern quickly. More information on tournament results can be found in [27].  
When \(U_{s}>U_{sh}>U_{f}>U_{w}\) and \(U_{sh}~{}>~{}(U_{s}+~{}U_{w})/2\) are assumed, TFT is stable and credible for two LTE operators if and only if \(\delta\) is greater than \((U_{s}-U_{sh})/(U_{sh}-U_{w})\) and \((U_{s}-U_{sh})/(U_{s}-U_{f})\), e.g., TFT is called a Nash equilibrium (NE). However, TFT is generally not a subgame perfect Nash equilibrium (SPNE) that represents a NE of every subgame of the original game. One can refer to [25] for a detailed discussions on _contrite tit for tat_ known as a SPNE variant of TFT as well as for the basic concept of game theory. Finally, we summarize the results in Figure 5.  
Figure 4: A scenario of collusion between two LTE operators under high density heterogeneous networks in the co-channel of the unlicensed spectrum.
## 2 Mathematical Models of the WT and of the Doubly Fed Induction Generator
### WT Mathematical Model and Parameters
This operation is not safe enough, as it lets the WT accelerate to high MAS values, which may damage the structure. Another possibility is to increase wind speed until reaching the stationary condition \(P_{TV}=P_{GE}\) for a constant and known value \(P_{GE}\), again for a prior set value \(\beta\
eq 0\), from which, based on the above relationship (4), we find \(d\). This approach is mostly impractical and should be performed in a wind tunnel with controllable wind speed. Finally, if \(dn/dt\) is measured at any point in time, then from (4) and with the WT operating at \(\beta\
eq 0\), we could obtain all parameters from \(\theta\) simultaneously, via nonlinear regression. A closeup of the \(P_{TV}\) curve for \(V=11\) [m/s] is revealed in Figure 1 where the MPP corresponds to an optimal MAS value. Although the power curve \(P_{TV}\) in Figure 1 is a classical one from the literature, its shape is instrumental in analyzing the dynamic optimal operation behavior. Unstable dynamics observed by linearization at the MPP will challenge how the MPP tracking performance will manifest in response to variable wind speed. The above MM is critical for deducing optimal operation at various wind speeds [3; 4; 5; 6]. For the WT to operate at MPP, the optimal MAS control is ensured by changing the load at the electric generator, based on wind speed variation [9; 10; 11; 12]. We note another issue with adapting to quick wind speed variation due to the high mechanical inertia of the WT [18; 19; 20; 21; 22; 42].
## 2 Maritime Cognitive Radio Communication Model  
### Centralized Cognitive Radio Communication System  
With the development of communication technology, the demand for digital communication among ships is increasing. Cognitive radios have been proposed as a solution to overcome spectrum congestion enabling opportunistic use of underutilized bands occupied by licensed users, which is typically the case of the VHF maritime band [22; 28]. Therefore, constructing a maritime cognitive radio communication system is of great significance for improving the utilization of spectrum resources.  
In cognitive radio, in order to solve the problem of the lack of spectrum resources and low efficiency of fixed spectrum allocation, it is necessary to improve the spectrum utilization and improve the efficiency of system communication, both of which are closely linked with spectrum allocation. Cognitive radio spectrum allocation can be divided into two categories according to the network structure: centralized spectrum allocation and distributed spectrum allocation [17]. Centralized spectrum allocation refers to the existence of a CBS in the network cell, and the CBS completes the spectrum allocation of each user in the cell; furthermore, the distributed spectrum allocation refers to non-CBS: users coordinate with each other to complete the spectrum allocation. Compared with distributed spectrum allocation, centralized spectrum allocation has certain advantages, such as convenient network management, less delay, and the easy control of interference to the PU's network. In view of the maritime wireless communication scenario, the purpose is to realize the data interaction between ship and ship, or between ship and shore station. It is a typical point-to-point direct communication.  
As shown in Figure 1, the CBS is a buoy when communications between ships happen at sea, while the shore station is the CBS when the location is close to shore. PU represents licensed users that are authorized to use the VHF maritime band, for example, VHF walkie-talkies and AIS. SU represents the cognitive radio communication equipment installed on the ships which can opportunistically use spectrum. SUs can communicate with each other and the CBS sends control information and receives status information from SUs [1; 32].  
The CBS manages the spectrum resources and allocates channels to SU. At the same time, it is responsible for perceiving the arrival of PUs and notifying SUs to avoid the PUs.  
Figure 1: Centralized maritime cognitive radio communication system.  
Independently of the ship's location, the cognitive radio networks evidence the clear characteristics of a centralized cognitive network. Thus, it requires a centralized cognitive radio communications system.
# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 2 Radar Frontal Image  
When a 2D array is available, the direction of arrival (DOA) of the scatterer can be found in terms of the azimuth and elevation. The analytical form of the received signal of a phase array is an exponential function, of which the frequency represents the DOA. From the matched filter concept, the pseudo spectrum used to find DOA is, in general, produced by the Fourier transformation of the received signal from the array. When the pseudo spectrum is generated in 2D, it becomes the frontal image. When the received signals from an array are available, the weighted sum of the received signals reaches its maximum, which is also when the weight has the best correlation with the measure, according to the matched filter theory [16]. The weighted sum of the received signals, \(y(k)\), becomes,  
\[y(k)=w^{H}\cdot x(k) \tag{1}\]  
where \(k\) is the sample number. Then the power of \(y(k)\) can be calculated as  
\[Pbf=E[|y(k)|^{2}]=E[\left|w^{H}\cdot x(k)\right|^{2}]=w^{H}\cdot E[x(k)^{H}x( k)]\cdot w=w^{H}\cdot R_{xx}\cdot w \tag{2}\]  
where \(R_{xx}\) is the autocorrelation matrix. When the incident wave is from \(\theta,\phi\) direction, the power becomes  
\[Pbf=E[\left|w^{H}\cdot x(k)\right|^{2}]=E[\left|w^{H}\cdot a(\theta,\phi)s(k) +n(k)\right|^{2}]=\left|w^{H}\cdot a(\theta,\phi)\right|^{2}\cdot({\sigma_{s} }^{2}+{\sigma_{n}}^{2}) \tag{3}\]  
where \({\sigma_{s}}^{2}\) is received power and \({\sigma_{n}}^{2}\) is noise power. The above equation reaches its maximum when the weight factors match the steering vector. Under that condition, the received power is  
\[Pbf=E[\left|y(k)\right|^{2}]= a(\theta,\phi)^{H}\cdot R_{xx}\cdot a(\theta,\phi) \tag{4}\]  
A pseudo spectrum can be constructed by changing theta and phi. In other words, the 2D Fourier transform constructs the frontal imagery that reflects the received powers from each part of a target depending on the azimuth and elevation angle. Frontal imagery pertains to the information about the physical characteristics of a target, which is critical in target classification.  
To construct the data set, we use a millimeter-wave FMCW radar system developed by Smart Radar Systems. The radar system comprises four AWR1243 chips (from TI Co. Ltd., Gumi-si, Korea) operating at 77 GHz with 12 dBm of transmitting power. Each  
Figure 1: General diagram of processing.  
AWR1243 has three TXs and four RXs, thus allowing the construction of a 16 virtual Rx array for each chip. Cascading four chips makes 192 channels available through 12 TXs and 16 RXs. The radar employs a sparse antenna array for DOA estimation with improved angular resolution [14]. Using a non-linear MIMO antenna configuration, the radar antenna produced a 2D virtual array of 31 \(\times\) 33, resulting in an angular resolution of approximately 3.5 degrees for the azimuth and elevation.
## 4 Results and Discussion
### Discussion  
The specific indicators [33] of the three controllers are listed in Table 3.  
It can be learned that there were some gaps between the experiment and simulation results. First, the steady state error in the experiment could not be ignored like that in the simulation because of the environmental noise, the sensor error and other nonlinear factors (such as the motion of the robot, air source instability and cylinder friction). Second, some indicators in the experiment, such as the settling time and the errors of the sinusoid track, were even better than those in the simulation. This was mainly because the simulation started from the linear approximation model, while the experimental model was estimated through experimental data. Despite this, the simulation and experiment results both showed the same result: the BPNN-SP-PID had the best performance among the three controllers.  
BPNN-SP-PID and BPNN-PID were obviously better than traditional PID, as the BPNN could adaptively adjust the PID parameters when dealing with the influence of nonlinear factors. The indicator of the maximum overshoot proved that BPNN-SP-PID had the best robustness. On the other hand, it is interesting to find that the performance gap between BPNN-SP-PID and BPNN-PID was different in the step response and sinusoid track. In the case of the step response, the BPNN-SP-PID showed a huge improvement compared with BPNN-PID. The maximum overshoot and settling time of BPNN-SP-PID were reduced by 68% and 65% in the experiment, respectively. In the case of the sinusoid track, however, the BPNN-SP-PID was just a litter better than the BPNN-PID controller. The maximum error and average error of the BPNN-SP-PID were only reduced by 9% and 8% in the experiment, respectively. The reason for this was probably that the setting force in the sinusoid track process was time-varying, which would partially affect the fitting precision of the Smith predictor and weaken the effect of it. A potential solution is adding a neural network identifier to update the ANN2 to further enhance the matching degree of the theoretical model and the actual one.  
Figure 17: Comparison of actual sinusoid tracking response curves in the experiment. (**a**) Response curves of the three controllers. (**b**) Histogram of the tracking errors.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 3 Methods
### QR Decomposition Based Recursive Least Squares  
The QR-RLS leverages the advantages of the triangulation process with good mathematical properties [37; 61]. It is based on robust QR decomposition, which contains the Givens transformation. It is a numerically stable algorithm with positive definiteness [37; 61].  
In the first step, the filter output is calculated based on Equation (5). Estimation of QR-RLS is determined by Equation (6), where \(d(n)\) is a desired signal, \(\overrightarrow{w^{\mathrm{T}}}(n)\) is a weight vector, and \(x(n)\) stands for the reference signal. The estimated error \(e(t)\) is calculated according to Equation (6) [33; 36].  
\[\overrightarrow{y}(n)=\overrightarrow{w}^{\mathrm{T}}(n)\overrightarrow{x}( n). \tag{5}\]  
\[e(n)=d(n)-\overrightarrow{w}^{\mathrm{T}}(n)\overrightarrow{x}(n), \tag{6}\]The filter periodically updates its weights based on Equation (7), where \(p(n)\) is the corresponding vector and \(R(n)\) represents the triangular matrix [33; 36].  
\[\overrightarrow{w}(n)=R^{-1}(n)\overrightarrow{p}(n), \tag{7}\]
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 4 Results and Discussion
### Far-Field Noise of the Original Model  
In order to study the far-field noise radiated from the original model, a standard observer point is selected, and its coordinates are (0, 25, 3.5) m. The time history of the sound wave is divided into three parts, namely the noise radiated from the panhead, middle, and bottom areas. Figure 9 compares the overall sound pressure level (OASPL) of the noise radiated from different parts, and the OASPL values are 105.12 dB, 103.29 dB, 94.61 dB, and 98.87 dB, respectively. It is found that the noise intensity of the panhead area is obviously higher than the other two parts, which is consistent with the qualitative analysis based on the pressure fluctuation contour shown in Figure 7. In addition, the far-field noise is also calculated using a finer grid of 43 million cells. The OASPL value of the noise radiated from all parts is 104.99 dB, indicating that the grid independence is well satisfied.  
Furthermore, the A-weighted sound pressure level spectra (A-weighted SPL) of noise radiated from different parts are presented in Figure 10. The peak frequency \(f_{p}\) of the total noise is 630 Hz, and the characteristic length scale \(L\) according to Aeolian tone characteristic is  
\[L=\frac{StUL_{\infty}}{f_{\rm p}}=\frac{0.2\times 350/3.6}{630}=0.031\ {\rm m} \tag{11}\]  
The value is close to the result in [24]. By comparing the contribution of different parts to the total noise, it is found that the noise radiated from the middle area is close to that of the bottom area, apart from the peak frequency of the former being lower than that of the  
Figure 8: Contour of the surface pressure level on the pantograph surface: (a) \(f_{c}\) = 2 kHz; (b) \(f_{c}\) = 250 Hz.  
Figure 9: Comparisons of overall sound pressure level between different noise parts.  
latter. In the high-frequency range (\(f>f_{p}\)), the spectrum value of noise radiated from the panhead area is larger than the noise radiated from the middle and bottom areas. However, the noise radiated from the panhead area is weaker than that from the other two parts in the low-frequency range (\(f<f_{p}\)).
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMCSPO and PDSMCSPO
#### 3.2.5 Output Error over Perturbation  
As it has been discussed in Chapter 2 that our proposed scheme for PDSMCSPO has more attenuation capability compared to SMCSPO. In Figure 14, it is visible that the trajectory tracking error in the case of SMCSPO is larger than our proposed scheme. The dotted blue line is the error trajectory of PDSMCSPO and the solid red line about SMCSPO.