esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Cascadable Current-Mode Inverse Filters  
### First-Order CM Inverse Filter Realization  
By slightly modifying the configuration depicted in Figure 2a, the circuit can perform a CM inverse filter as shown in Figure 3a. It consists of a primary circuit from Figure 2a and an additional VCII- #2. Due to its low input impedance and high output impedance, the scheme supports cascading inputs and outputs for CM operation. By selecting the appropriate impedance \(Z_{A}\), the proposed circuit in Figure 3a generates the following filter-order inverse frequency response for CM at the terminal \(i_{out1}\), which possesses the critical characteristics detailed in Table 5.  
\[G_{i1}(s)\ =\ \frac{I_{out1}(s)}{I_{in}(s)}\ =\ \frac{sR_{1}C_{1}+1}{sC_{1}Z_{A}}. \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filter Type** & \(Z_{A}\) & \(Z_{B}\) & \begin{tabular}{c} **Transfer Function,** \\ \(G_{i2}(s)\)=\(\frac{I_{out1}(s)}{I_{in}(s)}\) \\ \end{tabular} & \begin{tabular}{c} **Minimum Gain** \\ **(G0)** \\ \end{tabular} & \
\begin{tabular}{c} **Cut-Off** \\ **Frequency** \\ **(\(\omega_{0}\))** \\ \end{tabular} &
\begin{tabular}{c} **Quality** \\ **Factor** \\ **(Q)** \\ \end{tabular} \\ \hline ILP & \(\frac{1}{sC_{A}}\) & \(\frac{1}{sC_{B}}\) & \(G_{0}\)N(s) & \(\frac{C_{A}C_{A}}{C_{1}C_{2}}\) & \(\frac{1}{sC_{1}C_{2}}\) & \(\frac{sR_{1}R_{2}}{K_{1}C_{1}C_{2}}\) & \(\frac{1}{sC_{1}C_{2}}\) \\ IHP & \(R_{A}\) & \(R_{B}\) & \(G_{0}\Big{[}\frac{N(s)}{s(R_{1}C_{1}+R_{2}C_{2})}\Big{]}\) & \(\Big{(}\frac{R_{1}}{C_{2}}+\frac{R_{2}}{C_{1}}\Big{)}\Big{(}\frac{C_{A}}{R_{ 2}}\Big{)}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selection of impedances \(Z_{A}\) and \(Z_{B}\) for the second-order VM inverse filter in Figure 2b.  
Figure 3: Proposed VCII-based CM inverse filters. (**a**) 1 st-order filter; (**b**) 2 nd-order filter.  
From Tables 7-10, it is obvious that the non-ideal values of \(\omega_{0}\) and \(Q\) are not influenced by the current and voltage tracking error coefficients. The VCII tracking errors may result in slight changes in the gain values of the realized inverse filters. Nevertheless, the pre-distortion values of \(R_{A}\), \(R_{B}\), \(C_{A}\), or \(C_{B}\) can compensate for this impact without affecting the \(\omega_{0}\) value.
## 4 Discussion of the Temperature Influence on Resistivity  
In this section, a comparison of the results for the temperature correction models is discussed.  
To study the temperature effect on concrete resistivity, the behavior of resistivity depending on temperature was analyzed in the context of the Arrhenius law. Figure 6 represents the variation of the average resistivity as a function of temperature at four degrees of saturation, using the same average resistivities as previously used (all specimens except S2, see Table 1). It shows that the resistivity-versus-temperature relation follows a trend of exponential decay for both configurations, transmission and Wenner, and for all degrees of saturation. This is in accordance with the Arrhenius Equation (3) as well as with published results [35; 40].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\rho\) (\(\Omega\).m) & \(\mathbf{T}\) (\({}^{\circ}\)C) & \(S_{r}\)(\({}^{\circ}\)s) & \(95.0  
Table 4 shows that the percentage change in resistivity ranges from 1.3 to 3.8% per \({}^{\circ}\)C, which is partially in agreement with the values (1.8 to 3% or 3 to 5%) reported in the literature [19; 21]. However, it also indicates that the percentage resistivity change depends on the saturation degree as well as on the temperature range. For a given temperature range, the resistivity change per \({}^{\circ}\)C is, in general, larger on concrete specimens with lower saturation degrees. For example, the percentage resistivity change per \({}^{\circ}\)C is 2.3%/\({}^{\circ}\)C for a water-saturated concrete in the temperature range of 45-60 \({}^{\circ}\)C, and 3.8% for concrete with the lowest degree of saturation (33.8%). This result agrees with [24] where it is reported that, for ordinary Portland cement (OPC) concrete, resistivity changes by 3% per \({}^{\circ}\)C at 21 \({}^{\circ}\)C for a 70% saturated concrete, whereas it changes by 5% per \({}^{\circ}\)C for a 30% saturation degree. With respect to temperature, the resistivity change per \({}^{\circ}\)C is larger at a higher temperature than at a lower temperature. For example, in nearly saturated conditions, the resistivity change per \({}^{\circ}\)C is 1.3% between 20 and 45 \({}^{\circ}\)C, and 2.3% between 45 and 60 \({}^{\circ}\)C. Note that the measurement dispersion increases with high resistivity values at low saturation degrees of concrete due to the presence of heterogeneities (e.g., coarse aggregates and air bubbles). Thus, high uncertainty can influence the percentage of change per degree, such as the value at 45 \({}^{\circ}\)C and S = 33.8%.
## 3 Perceptual Evaluation
### BRIR Synthesis
#### 3.2.1 Constant Reverberation
As expected, there was generally a strong correlation between the ratings for the impression of walking towards a sound source and plausibility. Furthermore, all other attributes correlated more with plausibility than with any other attribute. The simplified scenes with constant reverberation and also with the very simple modeling of the level of the direct sound were perceived as plausible as the scene based on the originally measured BRIR data set. The ratings for the plausibility of the created auditory illusion were not affected by this simplification. The same was observed for the other attributes. This first experiment was conducted in a quite dry listening laboratory with a reverberation time \(RT60=0.27\) s. To verify this observation, a similar experiment was conducted in a more reverberant room, a seminar room with a reverberation time \(RT60=1.0\) s. Again, the realization based on a constant reverberation and an adjustment of the direct sound level was rated with the same plausibility as the realization with the BRIR data set originally measured in this room [32]. The simple reproduction was realized in two different versions based on the BRIRs of two different positions, 1.25 m and 3.25 m, from the sound source. For both versions, the ratings for plausibility did not vary significantly from the original measured BRIR set. So far, the constant-reverberation approach has only been evaluated for the case of walking towards and away from the sound source in a close distance in front of a loudspeaker. The case of walking past a sound source requires the consideration of the source directivity, when modeling the direct sound. This will not only affect the progress of its total energy, but also its spectral content because directivity is typically frequency-dependent. One approach could be the one proposed by [PERSON] et al. [33], which is discussed in Section 2.2.3. Additionally, it remains open, whether the sensitivity to changes or a lack of changes in the early reflection pattern increases if the listener walks past and behind a sound source, where the direct sound energy is low.
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 3 Discrete Time Fractional-Order Sine Chaotic Map (DFSCM)
### Discussion
#### 3.1.1 Sensitivity Analysis  
An important aspect of the chaos theory is the sensitiveness to the change in initial conditions of the map. This section investigates the sensitivity of the chaotic map (8) with initial conditions \(X_{0}=(0.01,0.01)\) and \(X_{1}=(0.0101,0.0101)\) for the parameters \(b=12\), \(c=50\) and \(\omega=0.5\), respectively. Numerical simulation is presented in Figure 4.
## 1 Introduction
For the problem of power-on clock error, a compensation method using pseudo-range and instantaneous Doppler value is proposed in this paper, and the time synchronization of multiple receivers is realized at the algorithm level. In order to solve the instability of the low-cost antenna phase center and the multipath effect, this paper proposes a method to describe the sky map of the time-series average error correction value. Aiming at the problem of carrier single difference ambiguity, the carrier single difference is modeled as the addition of integers and decimals. The fractional part is obtained by calculating the decimals of the two parts of the carrier phase observation value and each error correction value. The integer part is determined by the baseline length. Additionally, the combination of the two parts obtains a complete carrier single difference value. This method simplifies the ambiguity fixation algorithm and makes full use of the length and geometric constraint information of the baseline. The expected value of the DOA can be obtained from the ephemeris information and the prior information on the station position. The calculated value is obtained in real-time by the DOA calculation method. The residual between the calculated value and the expected value fits to the Gaussian distribution with a zero mean value when there is no spoofing signal. And the residual fits a Gaussian distribution with a non-zero mean in the presence of spoofing signals. Based on this, a binary hypothesis testing model was constructed using the sum of squared errors (SSE), and the theoretical threshold was calculated. The experiment proves the effectiveness of the proposed method, and the detection rate reaches 100%. In addition, the spoofing signal is identified. The direction-finding of the spoofer is solved, and the DOA accuracy can reach within 5 degrees. The subsequent chapters of this paper are arranged as follows: Section 2 introduces the system structure of the method and derives the necessary principles. Section 3 introduces the experimental scenario and displays the experimental results obtained from each experiment. Section 4 analyzes and discusses the experimental results. Section 5 is the conclusion part, which summarizes the research results of this paper and discusses future work.
## 4 Research Results
### Analysis of the Research Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Relations** & **Correlation** & \(\mathbf{r2}\) & \(\mathbf{t}\) & \(\mathbf{p}\) \\ \hline prototyping in the metal industry/reduction of production costs & 0.0605 & 0.0037 & 0.6718 & 0.5029 \\ prototyping in the automotive industry/reduction of production costs & 0.1690 & 0.0285 & 1.9012 & 0.0596 \\ prototyping in the metal industry/efficient use of material & 0.1718 & 0.0295 & 1.9342 & 0.0554 \\ prototyping in the automotive industry/efficient use of material & 0.0539 & 0.0029 & 0.5987 & 0.5505 \\ prototyping in the metal industry/freedom in product design & 0.1948 & 0.0380 & 2.2032 & 0.0294 \\ prototyping in the automotive industry/freedom in product design & 0.0812 & 0.0066 & 0.9404 & 0.3678 \\ prototyping in the metal industry/no assembly stage & 0.1948 & 0.0380 & 2.2032 & 0.0294 \\ prototyping in the automotive industry/no assembly stage & 0.0812 & 0.0066 & 0.9040 & 0.3678 \\ prototyping in the metal industry/product personalisation & 0.0950 & 0.0090 & 1.0589 & 0.2917 \\ prototyping in the automotive industry/product personalisation & 0.1426 & 0.0203 & 1.5974 & 0.1127 \\ prototyping in the metal industry/quick response to market needs & 0.1139 & 0.0130 & 1.2719 & 0.2058 \\ prototyping in the automotive industry/quick response to market needs & 0.0475 & 0.0023 & 0.5274 & 0.5988 \\ prototyping in the metal industry/optimisation of product functions & 0.0812 & 0.0066 & 0.9040 & 0.3678 \\ prototyping in the automotive industry/optimisation of product functions & 0.1948 & 0.0380 & 2.2032 & 0.0294 \\ prototyping in the metal industry/development & 0.2741 & 0.0751 & 3.1604 & 0.0020 \\ prototyping in the automotive industry/development & 0.2437 & 0.0594 & 2.7863 & 0.0062 \\ prototyping in the metal industry/elimination of human labour & 0.2252 & 0.0507 & 2.5635 & 0.0116 \\ prototyping in the automotive industry/elimination of human labour & 0.2231 & 0.0498 & 2.5385 & 0.0124 \\ prototyping in the metal industry/waste reduction/lower energy consumption & \(-0.0232\) & 0.0005 & \(-0.2572\) & 0.7975 \\ prototyping in the automotive industry/waste reduction/lower energy consumption & 0.0810 & 0.0066 & 0.9009 & 0.3694 \\ \hline \end{tabular}
\end{table} 
Table 4: Correlation analysis: prototyping process.
## 3 Test and Analytical Methods
### Hydrochemical Environmental Testing  
To evaluate the environmental compatibility of RCPB for practical engineering applications, fluoride ion (F\({}^{-}\)) leachability was assessed in accordance with Chinese national standards. The leaching test adhered to [17], which specifies procedures for leachate analysis.  
RCPB specimens were mechanically pulverized to a particle size of \(\leq\)9.5 mm and homogenized with deionized water in a 2 L polyethylene container at a solid-to-liquid ratio of 1:10 (70 g solid:700 mL liquid). The mixture was agitated continuously for 24 h at 30 \(\pm\) 2 rpm and 25 \({}^{\circ}\)C using a YKC-12 rotary shaker [23]. Subsequently, leachates were filtered through 0.45 \(\upmu\)m membranes under vacuum. Fluoride concentrations were quantified via ion-selective electrode (ISE) analysis per [24] pH measurements of the leachates were obtained using a calibrated pH meter following identical preparation protocols.  
To determine the pH of RCPB specimens after UCS testing, a central core sample was extracted from each test block. Specimens were crushed, ground, and sieved through a 0.08 mm mesh to ensure homogeneity [25]. The powdered material was mixed with deionized water at a solid-to-liquid ratio of 1:10 (_w/v_) and agitated for 18 h using a mechanical shaker to achieve thorough solid-liquid interaction. The suspension was transferred to centrifuge tubes and centrifuged at 3000-4000 rpm for 10-15 min. Following centrifugation, the supernatant was allowed to settle for 15 min to ensure complete particle sedimentation. The pH of the supernatant was then measured at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C) using a calibrated pH meter [19].
## 4 Materials and Methods
### Dual-Luciferase (Dual-LUC) Assay  
Dual-luciferase assays were performed to assess whether _DZHDZ32_ could activate the promoters of genes involved in diosgenin biosynthesis. The _DZHDZ32_ coding sequence was cloned into the pGreenII62-SK vector as the effector, while the promoters of target genes were inserted upstream of the luciferase reporter gene in the pGreenII0800-LUC vector, serving as reporters. An empty pGreenII62-SK vector was used as a negative control. Effector and reporter constructs were individually transformed into _A. tunefaciens_ strain EHA105. The transformed bacteria were cultured in LB medium containing 50 mg/L kanamycin and 20 \(\upmu\)M AS at 28 \({}^{\circ}\)C with shaking at 200 rpm for 8 h. Cultures were then resuspended in infiltration buffer (10 mM MES, 10 mM MgCl\({}_{2}\), and 200 \(\upmu\)M AS) and adjusted to an OD600 of 0.8. The suspension was incubated in the dark at 22 \({}^{\circ}\)C for 2 h before being infiltrated into the abaxial side of young leaves. After infiltration, plants were kept in the dark for 12-16 h, then transferred to light for 17-21 h. LUC (luciferase) and REN (Renilla luciferase) activities were measured using a luciferase assay kit (Promega, Madison, WI, USA). All experiments were conducted with three biological replicates.
## 3 Proposed Cumulative QoE Model
### Cumulative QoE Model  
Through the investigation of the above human-related influence factors, the proposed cumulative QoE model is presented in Equation (7). In this model, to quantify how each of the user's past experiences influences the cumulative perception, the instantaneous QoE needs to be weighted by the memory effect from the beginning of playback to the investigated time point \(t\) within a streaming session. According to our proposed model, the procedure of estimating cumulative QoE is described as follows: Firstly, the instantaneous QoE is predicted by LSTM-QoE model [15], and stored into  
Figure 6: Scatter plot between the mean of subjective DoI scores and the subjective overall QoE obtained in the database.  
vector \(Q_{t}=(q_{0},q_{1},\ldots,q_{t})\). Secondly, the memory weight is calculated by Equation (6) to form vector \(W_{t}=(w_{0},w_{1},\ldots,w_{t})\).  
\[CQ_{t}=\lambda_{1}\left(Q_{t}\times W_{t}^{T}\right)+\lambda_{2}DoI \tag{7}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are correlation coefficients, which, respectively, determine the contribution of the user's past experience and user's interest in video content to the predicted cumulative QoE \(CQ_{t}\) at time instant \(t\).
## 2 Harvesting System
### Electro-Mechanical Equivalent Model of the Mini Wind Harvester or Reactor
#### 3.3.2 Parameters Measurement and Obtainment  
The DC \(R_{in}\) was measured with a polymer and is 11 \(\Omega\) for all the phases.  
The electro-mechanical coefficient, \(G\) (V/rad/s), is obtained as a linear relationship between the generated voltage, \(V\) (V), and the angular velocity, \(\omega\) (rad/s) as shown in Equation (16):  
\[G=\frac{V}{\omega} \tag{16}\]  
Electro-mechanical coefficients for different air velocities ranging from 3 to 10 m/s are presented in Table 14. The most suitable coefficient is 0.002 V/rad/s:  
The damping effect constitutes a mechanical element in the electro-mathematical model. This behavior is represented in the model as a resistance with a value given by Equation (17).  
\[R_{damping}=\frac{1}{c_{m}} \tag{17}\]  
where \(c_{m}\) is the damping coefficient. The aerodynamic damping can be determined using a non-linear time domain equation. However, the damping \(\delta\) (aerodynamic plus structural damping) can also be calculated from the logarithmic decrement, as shown in Equation (18):  
\[R_{damping}=\delta=\frac{1}{n}\log\Bigl{(}\frac{x_{n-p}}{x_{n}}\Bigr{)} \tag{18}\]  
This relationship only has meaning for underdamped systems because the logarithmic decrement is defined as the natural log of the ratio of any two successive amplitudes, and only underdamped  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Velocity (m/s)** & **Voltage (V)** & **\(\omega\) (rad/s)** & **\(G\) (V/rad/s)** \\ \hline
3 & 2.05 & 997 & 0.00205 \\
4 & 3.02 & 1498.51 & 0.00202 \\
5 & 4.17 & 2031.14 & 0.00205 \\
6 & 5.32 & 2526 & 0.00211 \\
7 & 5.98 & 2749 & 0.00218 \\
8 & 6.66 & 3219.99 & 0.00207 \\
9 & 7.57 & 3779.50 & 0.00200 \\
10 & 8.38 & 4159.75 & 0.00202 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Electro-mechanical coefficient for different air velocities.  
systems exhibit oscillation. As this system belongs to the latter group, consequently, Equation (18) could be used for damping coefficient calculation.  
One technique to derive the aerodynamic damping ratio is to analyze the decay of the free vibration of the tower top after pulse loading. First, the mini wind harvester is set to normal work operation. When this operation mode is reached, the mini wind harvester is braked, and the voltage waveform at the output of the generator provides the damping ratio of the wind harvester, Figure 17.  
The accuracy of the data was improved by measuring several points which avoids any incongruent/fail result. Three different points were taken from the measured damper curve of Figure 17, besides, the reference points x\({}_{0}\), x\({}_{2}\), x\({}_{6}\), and x\({}_{10}\) would corroborate the similar value of the damping coefficient. Finally, Equation (18) was applied, achieving as a result values presented in Table 15.  
The value chosen as the damping effect resistance is an intermedium value, thus: \(R_{damping}=1/c_{m}=0.0215\ \Omega\).
## 4 Multi-Objective Optimization
### Significance Analysis of Influencing Factors
The coefficients of outlet angle \(\beta_{2}\), outlet width \(b_{2}\) and impeller timing position are negative, which means that they have a negative effect on efficiency, i.e., as the outlet angle \(\beta_{2}\), outlet width \(b_{2}\), and impeller timing position increase or decrease, the change in efficiency tends to rise or fall. As the exit angle \(\beta_{2}\), exit width \(b_{2}\) and impeller timing position increase or decrease, the change in efficiency tends to decrease or increase. The exit width \(b_{2}\) has the greatest influence on efficiency, followed by the exit angle \(\beta_{2}\), and the impeller timing position is the smallest. The standard error of each influencing factor is 0.4218, and the \(p\)-value of each factor shows that the exit angle \(\beta_{2}\), exit width \(b_{2}\), and blade wrap angle \(\varphi\) are the significant influencing factors of the head. From the results of the significance analysis of the influencing factors of the pump head and efficiency, it was determined that the outlet placement angle \(\beta_{2}\), outlet width \(b_{2}\), and blade wrap angle \(\varphi\) were the optimization parameters. According to the specific situation of this research model, the optimization parameters are determined as follows: \(17\leq\beta_{2}\leq 35;7.8\leq b_{2}\leq 15;58\leq\varphi\leq 130\).
## 3 Results and Discussion
### Complex Ecospatial Networks Evaluation
#### 3.2.5. Ecospatial Network Robustness  
The ecosspatial network in the study area was simulated as an adjacency matrix using MATLAB, and the malicious attacks and random attacks on ecological nodes and corridors were simulated by matrix calculation. The calculation results are shown in Figure 12.  
It can be seen from the figure that the initial node recovery robustness of the network is 1. Under random attack, when the number of removed nodes is less than about 150, the network structure can be completely recovered; with an increase in the number of attacked nodes, the node recovery robustness decreases, and the degree of its decrease increases. That is to say, when the number of removed nodes increases, the negative impact of removing a node on the node recovery robustness will become stronger and stronger, and the degree of damage to the network structure will also increase; when the number of removed nodes exceeds 600, the rate of decrease in the node recovery robustness is accelerated. When the number of nodes exceeds 830, the value of node recovery robustness is below 0.1, and the ecological function of the network is almost completely lost. Under malicious attacks, when the number of removed nodes is less than about 300, the network structure can be completely recovered; then, with the increase in the number of removed nodes, the node recovery robustness shows a decreasing trend, and the rate of decrease increases, which is similar to the curve under random attacks. However, several important node number critical points of the malicious attack are larger than the random attack, indicating that the damage caused by random attacks is stronger than malicious attacks in terms of node recovery robustness.  
The initial corridor recovery robustness of the network is 1. The corridor recovery robustness curves under random attack and malicious attack are both convex curves. When fewer corridors are removed, the network structure can be recovered; as the number of damaged corridors increases, the corridor recovery robustness decreases significantly, and the rate of decrease increases. When the number of removed corridors is less than 830, the negative impact of malicious attacks on corridor recovery robustness and the damage to the network structure are higher than those of random attacks. The critical point of the corridor number where the corridor recovery robustness drops below 0.1 for malicious attacks is around 830, and for random attacks, it is around 800. At this time, the network structure is close to collapse. When it is more than 830, the damage of random attacks is slightly stronger than malicious attacks.