esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Experiments and Analysis
### Parameters Setting  
To highlight the effectiveness of the proposed model, four conventional data-driven techniques named the Bagtree, the support vector regression (SVR), the BP neural network [35], and the ELM are employed as a control group to simulate the complete characteristic curve of the pump turbine. The Bagtree model is constructed using MATLAB's \"Bag\" function. The parameters of the SVR model including the penalty factor \(C\) and the kernel parameter \(\sigma\) are obtained using the grid search (GS) algorithm. The search range of \(C\) is set as [2-8, 28], and the search range of \(\sigma\) is set as [2-5, 25]. The \"trainlm\" algorithm is employed in training the BP neural network. The maximum number of iterations is set as 500 and the target error is 1e-5. The number of the hidden nodes is selected using a trial-and-error method.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 2 Related Work
Adaptive fuzzy multiple attribute decision routing (AFMADR) [20] designs an adaptive weight assignment mechanism to calculate weights of four proposed attributes and selects a candidate with the highest fuzzy utility score as the next hop transmission. Fuzzy logic-based directional geographic routing (FL-DGR) [21] proposed the fuzzy logic decision system (FL-DS) which is used for estimating multiple metrics to choose the next hop for forwarding packets. Since the recovery strategy of FL-DGR adopts the carry-and-forward approach, it is a DTN routing protocol, and it will perform well in some conditions where there are few vehicles and links break frequently, such as late at night. We summarize the characteristics of the representative routing protocols including AODV, GPSR, GeOpps, iCAR, iCAR-II, TAROC, and AFMADR schemes to compare with our proposed scheme as shown in Table 1.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Local Hybrid Transformation  
Feature-based panorama generation based on global single transformation has shown good results for pure rotational moves and planar scenes, but in real practice, this condition is rarely satisfied due to movement of the UAV, as shown in Figure 3[41]. Therefore,ghosting effects frequently happen when the images are aligned. Moreover, the parallax problem remains due to the move of the optical center [74].  
Local hybrid transformation is where mesh-based alignment is reviewed, since it is complemented by the other methodologies [61]. Mesh-based alignment divides images into uniform meshes. Each mesh corresponds to an estimated transformation where there are two regions: the overlapped region, which is aligned by the projective transformation, and the nonoverlapped region, which is generally warped by using a similarity transformation by calculating the local homography model to avoid potential distortions.
## 3 Analysis of the Time Fluctuation of Underwater Acoustic Communication Signals
### Statistics of the Time Fluctuation of Low-Frequency Signal Fields  
When an acoustic signal propagates in a shallow sea channel, it also has an undulating effect that changes over time, which corresponds to a time-varying channel in underwater acoustic communication. In this section, based on the spatial fluctuations of the signal field, the temporal fluctuations of the signal field were studied. The experimental ExQD_1701 data was statistically analyzed. In order to fully consider the selective frequency fading of the signal and ignore the effect of bandwidth on the time fluctuation of the signal field, the analysis used single-frequency signals. The time fluctuations in the low-frequency signal field and the high-frequency signal field were examined.  
1. Spatial fluctuation of the high-frequency signal field  
First, we analyzed the time fluctuation of the low-frequency signal. The frequencies of the transmitted signal were 95 Hz and 400 Hz, and the sound source level of the  
Figure 11: Different frequency noise field intensities with depth (40 m).  
which showed that the channel structure stabilization time became shorter in underwater acoustic communication. As the frequency increased, the wavelength of the acoustic wave became shorter, the signal field became more complex under the influence of scattering and interference caused by interface fluctuations, and the fluctuation law of single-frequency signals appeared to be less significant.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 5 Effect of Metalloid-Based NPs on Medicinal Plants
### Silicon and Silica NPs
#### 5.1.1 Impact on Germination and Plant Growth  
SiO\({}_{2}\) NPs applied at a dose of 400 mg/L pronouncedly improved seed germination, enhanced the length and fresh/dry weight of plant organs of _Nigella sativa_ L., and increased levels of photosynthetic pigments, total protein, total amino acid, and proline [285]. Irrigation of hawthorn (_Crntaegus aronia_ L.) seedlings with SiO\({}_{2}\) NPs at doses 10, 50, and 100 mg/L for 45 days before exposure to drought stress resulted in improved plant biomass, xylem water potential, and MDA content, especially under drought conditions, and had positive influence on photosynthetic pigments; relative water content and membrane electrolyte leakage were not affected and carbohydrate and proline content showed a decrease under all water regimes, especially under water stress [286]. Plants grown on MS medium from _Matricaria recutita_ seeds treated for 1 h with SiO\({}_{2}\) NPs (4 or 6 g/L) were characterized with improved growth characteristics, higher relative water content, TPC, and TFC, but reduced levels of H\({}_{2}\)O\({}_{2}\) and MDA [153].
## 4 The Complementary Filter Method and Performance Analysis
### The Fusion Acceleration Based on the Modified Complementary Filter Method
Figure 5 exhibits the simulation of the acceleration bode response with different \(1/T_{1}\). In Figure 5, as \(1/T_{1}\) increases, the curve fluctuates more and more violently. It is because the smaller handover frequency means less influence of the non-linear link \(e^{-\tau s}\) on the fusion acceleration. Since the curve fluctuations are detrimental to controller design, we should try to choose a small handover frequency. However, since the drift and noise of the MEMS accelerometer have a serious pollution on the useful signal below 2 Hz, to reduce the MEMS accelerometer's proportion in the fusion acceleration, \(1/T_{1}\) should be no less than 2 Hz. Therefore, we choose 2 Hz as the handover frequency. Since the non-linear link \(e^{-\tau s}\) is not convenient for spectrum analysis, we give out its approximate form. \[e^{-\tau s}=\frac{1}{e^{\tau s}}=\frac{1}{1+\tau s+1/2\tau^{2}s^{2}+1/6\tau^{3} s^{3}\ldots}\approx\frac{1}{1+\tau s}(\cdot\cdot\tau=0.02<<1\ ) \tag{20}\]  
Substituting Equation (20) into Equation (19)  
\[ \begin{array}{ll}a_{fusion}(s)&\approx\ (\frac{1}{1+T_{1}s}\frac{1}{1+T_{1}s} +\frac{T_{1}s}{1+T_{1}s}\frac{1}{1+T_{2}s})a_{real}\ =\frac{T_{1}s^{2}+(T_{1}+T_{2}s)+1}{(1+T_{1}s)(1+T_{2}s)}a_{real}\\ &=\frac{T_{1}s^{2}+(T_{1}+T_{2}s)+1}{[T_{1}s^{2}+(T_{1}+\tau)s+1](1+T_{2}s)}a_ {real}\ \approx\frac{1}{(1+T_{2}s)}a_{real}(\cdot\cdot\ T_{2}<<\tau<<T_{1})\end{array}  \tag{21}\]  
Figure 5: The simulation of the acceleration bode response with different \(1/T_{1}\). Equation (21) is the approximation of Equation (19). Intuitively, the rebuilt acceleration object \(F_{a}\) is to add an inertial element behind the original one. However, in fact, purely adding an inertial element could only filter the high-frequency noise but the complementary filter method would simultaneously eliminate the time-domain drift. Considering that the proposed way would also lead to a decrease of the acceleration's bandwidth, in order to avoid the decrease of the acceleration closed-loop bandwidth, in the acceleration feedback loop we still use the MEMS accelerometers' original signal. Since an accurate model in high frequency is commonly unreliable, with DOB method we generally choose to compensate low- and medium- frequency disturbance. What's more, as a feed forward method, DOB would not affect the closed-loop performance. Therefore, the relatively low-bandwidth fusion acceleration is very suitable to the DOB method.
## 3 Results and Discussion
### Sensory Evaluation
** & **0** & **1** & **5** & **9** & **12** & **14** & **16** & **17** & **18** & **19** & **Mean** \\ \hline  & 4\({}^{\circ}\)C & 0.83 \(\pm\)0.141 & 0.83 \(\pm\)0.141 & 0.83 \(\pm\)0.158 & 0.86 \(\pm\)0.11 & 1.46 \(\pm\)0.0791 & 1.75 \(\pm\)0.2048 & 2.08 \(\pm\)0.0751 & 2.16 \(\pm\)0.0798 & 2.20 \(\pm\)0.12391 & 2.23 \(\pm\)0.0398 & 1.54 \(\pm\)0.1025 \\  & 4\({}^{\circ}\)C & 0.83 \(\pm\)0.141 & 0.83 \(\pm\)0.141 & 1.29 \(\pm\)0.1341 & 1.79 \(\pm\)0.0563 & 2.30 \(\pm\)0.0694 & 2.56 \(\pm\)0.0876 & 3.04 \(\pm\)0.0476 & 3.63 \(\pm\)0.1746 & 4.08 \(\pm\)0.0504 & 2.21 \(\pm\)0.018 \\  & 10\({}^{\circ}\)C & 0.83 \(\pm\)0.141 & 0.83 \(\pm\)0.130 & 0.96 \(\pm\)0.146 & 1.64 \(\pm\)0.0998 & 3.27 \(\pm\)0.026 & 3.56 \(\pm\)0.2346 & 4.77 \(\pm\)0.014 & 4.53 \(\pm\)0.0135 & 4.96 \(\pm\)0.0148 & 2.56 \(\pm\)0.1742 & 2.93 \(\pm\)0.032 \\  & **Mean** & 0.83 \(\pm\)0.075 & 0.83 \(\pm\)0.076 & 0.83 \(\pm\)0.017 & 1.30 \(\pm\)0.117 & 1.91 \(\pm\)0.1628 & 2.84 \(\pm\)0.2020 & 2.94 \(\pm\)0.335 & 3.24 \(\pm\)0.335 & 3.36 \(\pm\)0.0488 & 3.39 \(\pm\)0.0443 & \\ \hline  & 4\({}^{\circ}\)C & 3.14 \(\pm\)0.020 & 3.14 \(\pm\)0.008 & 3.27 \(\pm\)0.004 & 3.27 \(\pm\)0.006 & 3.27 \(\pm\)0.004 & 4.41 \(\pm\)0.1212 & 5.46 \(\pm\)0.2484 & 2.69 \(\pm\)0.1649 & 7.14 \(\pm\)0.108 & 7.09 \(\pm\)0.1145 & 4.03 \(\pm\)0.035 \\  & 4\({}^{\circ}\)C & 3.14 \(\pm\)0.020 & 3.14 \(\pm\)0.008 & 3.22 \(\pm\)0.006 & 3.34 \(\pm\)0.015 & 3.57 \(\pm\)0.123 & 7.24 \(\pm\)0.179 & 8.97 \(\pm\)0.117 & 1.14 \(\pm\)0.3162 & 1.23 \(\pm\)0.1784 & 6.51 \(\pm\)0.083 \\  & 30\({}^{\circ}\)C & 3.14 \(\pm\)0.020 & 3.16 \(\pm\)0.022 & 3.38 \(\pm\)0.005 & 4.55 \(\pm\)0.086 & 3.04 \(\pm\)0.0136 & 1.08 \(\pm\)0.2407 & 2.67 \(\pm\)0.0246 & 1.22 \(\pm\)0.3436 & 1.55 \(\pm\)0.048 & 1.43 \(\pm\)0.0310 & 8.85 \(\pm\)0.019 \\  & **Mean** & 3.34 \(\pm\)0.010 & 3.18 \(\pm\)0.020 & 3.25 \(\pm\)0.004 & 3.58 \(\pm\)0.0197 & 5.06 \(\pm\)0.4422 & 7.01 \(\pm\)0.0078 & 5.62 \(\pm\)1.0050 & 3.03 \(\pm\)1.079 & 1.14 \(\pm\)1.2282 & 1.18 \(\pm\)0.0584 & \\ \hline  & 4\({}^{\circ}\)C & 2.24 \(\pm\)0.0203 & 2.28 \(\pm\)0.020 & 2.61 \(\pm\)0.0404 & 3.34 \(\pm\)0.0320 & 4.74 \(\pm\)0.026 & 5.86 \(\pm\)0.0428 & 7.73 \(\pm\)0.008 & 9.13 \(\pm\)0.16 & 1.16 \(\pm\)0.303 & 5.68 \(\pm\)0.87A & \\  & 4\({}^{\circ}\)C & 2.24 \(\pm\)0.0203 & 2.28 \(\pm\)0.0036 & 2.32 \(\pm\)0.0046 & 2.68 \(\pm\)0.0596 & 3.34 \(\pm\)0.0134 & 4.42 \(\pm\)0.068 & 4.96 \(\pm\)0.0496 & 5.27 \(\pm\)0.0042 & 3.63 \(\pm\)0.296 & 3.63 \(\pm\)0.296 & 3.63 \(\pm\)0.296 \\  & **Mean** & 2.24 \(\pm\)0.0311 & 2.25 \(\pm\)0.0018 & 2.29 \(\pm\)0.0036 & 2.23 \(\pm\)0.0046 & 2.25 \(\pm\)0.0049 & 2.75 \(\pm\)0.0043 & 2.21 \(\pm\)0.0048 & 3.22 \(\pm\)0.0048 & 3.50 \(\pm\)0.011 & 3.89 \(\pm\)0.048 & 2.28 \(\pm\)0.115 \\  & **Mean** & 2.24 \(\pm\)0.0311 & 2.25 \(\pm\)0.0018 & 2.29 \(\pm\)0.0031 & 2.27 \(\pm\)0.0146 & 3.38 \(\pm\)0.0347 & 4.02 \(\pm\)0.0178 & 4.67 \(\pm\)0.050 & 5.31 \(\pm\)0.0646 & 5.96 \(\pm\)0.0289 & 7.29 \(\pm\)1.144 & \\ \hline \multicolumn{1}{c
# Novel Designated Ownership Transfer with Grouping Proof
## 3 Security Analysis
### Replay Attack  
Assume an attacker can intercept all previous generated grouping proof messages transmitted between all communicating parties and resend them later to bypass authentication or to generate a bogus proof.  
However, because each piece of proof contains a different random number \(Nt_{j,i}^{q}\) or a timestamp \(TS_{p}\) for every session, the reader can detect and ignore replayed messages by verifying the timestamps or the random numbers to check whether it has been used before.
## 7 Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control
### Digital Implementation of the PII\({}^{1/2}\)DD\({}^{1/2}\) Position Control  
In Section 7.1, the comparison of the controllers in the time domain (step response, Figure 7) has been carried out considering continuous-time systems (continuous-time simulation, CTS). In real applications, control algorithms are implemented digitally in discrete time with sampling time \(T_{s}\); in particular, FO derivatives and integrals are evaluated by digital filters with finite memory length \(n\) by Equation (6), with \(T_{s}\) and \(n\) limited by the computing performance of the controller. In this section the real performance of the controllers will be compared in two steps: by carrying out simulations with discrete-time and limited memory implementations of the controllers (discrete-time simulations, DTS, Section 8.2), and by experimental tests on the physical prototype (ET, Section 8.3).  
Moreover, finite displacements of position-controlled mechatronic devices are never performed using step inputs for the position set point, to avoid an abrupt increase in the error and the subsequent saturation of the control output. On the contrary, a trapezoidal speed law of the position set-point is usually adopted: a first phase with constant acceleration, then a second phase with constant speed, and finally a third phase with constant deceleration. Therefore, a trapezoidal position reference will be considered in the rest of the section.
## 3 Simulation Results and Sensitivity Analysis
### Influences of Diameter on the Von-Mises Stress of the Pipe  
In general, the maximum stress and deformation of PVC pipes increase as the diameter increases [60]; therefore, it can be inferred that HDPE pipes with different diameters have different responses to a traffic load under the same backfill conditions. Figures 13-15 display the Von-Mises stress curves of pipes with different diameters. In this part of the simulation, the traffic load was 0.7 MPa and 60 km/h, and the hoop stiffness of the pipe was 4 kPa.  
The variations in the crown Von-Mises stress versus the diameter of pipes were similar (Figure 13). When the compaction degree of L1 was lower than the standard, the crown stresses for different pipe diameters descended in the order 400 mm pipe, 800 mm pipe, and 600 mm pipe (Figure 13a). The crown stress was inversely proportional to the diameter when the degree of compaction of L2 or L3 was reduced (Figure 13b,c). The results indicated that the crown stress of the 400 mm pipe was more sensitive to the compaction degree of L3, while those of the 600 mm and 800 mm pipes were greatly influenced by the compaction degree of L1 and L2. The response of the springline stress to the different diameters of the pipe was similar to that of the crown stress, and the stress curves of the 600 mm and 800 mm pipes were more coincident at the springline than at the crown (Figure 14). This means that when the diameter of the pipe was large enough, the deformation of the springline was almost independent of the diameter. The variations in the invert stress of the 600 mm and 800 mm pipes with backfill compactness were similar (Figure 15), but the 400 mm pipe differed at the invert, where the Von-Mises stress increased with looser L1 (Figure 15a). When the compaction degree of L1 or L2 decreased, the invert stress of the 400 mm pipe was the largest, followed by 800 and 600 mm (Figure 15a,b).  
When the compaction degree of L3 decreased, the invert stresses of pipes with different diameters descended in the order 400, 600, and 800 mm (Figure 15c), which indicated that the invert stress of pipes with large diameters was more susceptible to the compaction degree of L3. In conclusion, the Von-Mises stress of the 400 mm pipe was always greater than that of 600 mm and 800 mm pipes under the same cover depth, backfilling condition, and traffic load. Therefore, it is speculated that when the diameter of the pipe is small enough, the loose backfill around the pipe and traffic load are more likely to cause pipe deformation.  
Figure 12: The Von-Mises stress under different load speeds: (a) change in L1 compaction degree; (b) change in L2 compaction degree; (c) change in L3 compaction degree.  
Figure 14: The springline Von-Mises stress of different diameter pipes under traffic load: (**a**) change in L1 compaction degree; (**b**) change in L2 compaction degree; (**c**) change in L3 compaction degree.  
Figure 13: The crown Von-Mises stress of different diameter pipes under traffic load: (**a**) change in L1 compaction degree; (**b**) change in L2 compaction degree; (**c**) change in L3 compaction degree.
# 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
#### 3.4.1 Harmonious Coexistence by Always Cooperation  
Suppose LTE operator 1 and LTE operator 2 are following the policy of always LTE with LBT, e.g., always cooperation (AC) is one of the strategies. In game theory, a strategy is generally a specification of what to do in any situation that might arise. Herein, in the infinite repeated game, we use the term strategy to describe a certain policy or behavior pattern expected from an operator in response to another's action at the _latest_ stage.  
A well-known example of many strategies is grim trigger, i.e., an operator cooperates as long as the other has been cooperating, and defects as soon as the other deviates. Although the grim trigger strategy is extremely harsh toward its counterpart, we can show the condition of \(\delta\) that no operators ever want to deviate from AC as long as everyone has been cooperating. To show this, the utility from mutual cooperation must be greater than or equal to that from temptation to defect in an infinitely repeated game. If two operators stick to AC, the utility is \(U_{sh}+\delta U_{sh}+\delta^{2}U_{sh}+\delta^{3}U_{sh}+\cdots=U_{sh}/(1-\delta)\). However, once one operator defects at the first stage, then the other operator defects according to the grim trigger strategy. The utility of the betrayer is \(U_{s}+\delta U_{f}+\delta^{2}U_{f}+\delta^{3}U_{f}+\cdots=U_{s}+\delta U_{f}/(1-\delta)\). The difference between the utilities in two cases is \(U_{sh}-U_{s}+\delta(U_{sh}-U_{f})+\delta^{2}(U_{sh}-U_{f})+\delta^{3}(U_{sh}- U_{f})+\cdots=U_{sh}-U_{s}+\delta(U_{sh}-U_{f})/(1-\delta)\). Thus, difference is non-negative if \(U_{sh}-U_{s}+\delta(U_{sh}-U_{f})/(1-\delta)\geq 0\), e.g., \(\delta\geq(U_{s}-U_{sh})/(U_{s}-U_{f})\).  
Hence, as long as operators care about the next period at least \((U_{s}-U_{sh})/(U_{s}-U_{f})\) as much as the current stage, AC can be mutually induced. Note that AC with the grim trigger strategy is the most strict and unforgiving strategy for operators in the unlicensed spectrum game. Although operators seem to adhere to AC based on the grim trigger strategy, in reality everlasting punishment is too harsh to efficiently and harmoniously utilize the unlicensed spectrum. There is a high possibility of multi-RAT networks unintentionally interfering with incumbent systems by miss detection and false alarm. We will discuss monitoring and punishment issues for a selfish network in the next subsection.
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 3 A Novel Adaptive Sparrow Search Algorithm
### Sine Chaotic Mapping Strategy for Initialization Population  
The SSA algorithm uses a random initialization method to determine the initial position of the sparrow. Although the randomness of the initial position is guaranteed, the optimal value of the initial position of some individuals is too far from the optimal value, which reduces the convergence speed and solution accuracy. However, the pseudo random number generator (PRNG) has good statistical and random characteristics, which are regarded as ideal information sources. Adaptive chaotic mapping is a good candidate for pseudo-random number generation with high randomness and easy implementation. Some pseudo-random number algorithms for chaotic systems have been proposed in recent years [41]. Therefore, the sine chaotic map is employed to initialize the population of the SSA algorithm, and the advantages of the randomness and initial sensitivity of the chaotic sequence are used to effectively make up for the shortcomings of the random initialization method. The expression of the sine chaotic map is described as follows.  
\[x_{n+1}=\frac{a}{4}\sin\left(\pi x_{n}\right)\ a\in(0,4] \tag{8}\]  
where, \(a\) represents the control parameter, and \(x_{n}\) represents the value of the mapping function. Pine mapping is a unimodal mapping with a value range of \([-1,\,1]\).
# Stalling in Queuing Systems with Heterogeneous Channels
## 1 Introduction
The Quality of Service (QoS) in different service classes with real-time and non-real-time traffic integration is an important issue in WiMAX systems; therefore, the authors of [32,33] proposed a cross-layer QoS support scheduling framework and a corresponding opportunistic scheduling algorithm to provide QoS support to the heterogeneous traffic in a single-carrier WiMAX point-to-multipoint (PMP) system. The presented model is applied to the uplink transmission in the single-carrier WiMAX system as a multi-class priority TDMA queueing system in order to analyse the average packet delays of different service classes. The authors of [34] demonstrated that a processor's fast cores may not be ideal for system workloads and that less can be more in some situations. Furthermore, in [35], a novel fuzzy testing technique, HFuzz, was proposed as a solution enabling efficient testing on real heterogeneous architectures. Research results showed that such a system can perform well, using much more constrained resources than are usually available. This paper aims to show that stalling is an efficient approach for exploiting and managing heterogeneous systems. The model of stalling in QSs with two heterogeneous servers has been considered in [9] by the first two authors of this paper, where the explicit probabilities of steady states were derived. The results showed that the appropriate choice of stalling buffer size is helpful in solving the slow server problem. The asymptotic approximations of optimal stalling buffer size, when the ratio of the capacities of fast and slow servers is increasing, have also been established. The application of the model developed in computer networks has been discussed as well. Investigation of the developed model enables us to conclude that stalling is a universal solution which can be implemented into any heterogeneous system. The findings presented in this paper for several-server QSs might be useful for the investigation of systems with any number of heterogeneous channels or servers with different capacities. Since the analytical study of systems with heterogeneous channels is rather complicated, a system with two types of servers, equipped with a stalling buffer and a finite waiting line, is presented and analysed.