esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Three Loops
### CNOT Gate Fidelity  
The controlled-NOT (CNOT) gate with the single qubit gate provides a universal set for quantum computing. For the CNOT gate operation, we consider a kind of cross-resonance scheme that two qubits in Figures 1 and 2 are driven by an oscillating magnetic field resonant with the target qubit frequency. When two qubits (left and right qubits) are coupled, the Hamiltonian for coupled qubits can be written as [27]  
\[H=E_{z}^{l}\sigma_{z}\otimes I+I\otimes E_{z}^{r}\sigma_{z}-t_{q}^{l}\sigma_{ x}\otimes I-t_{q}^{r}I\otimes\sigma_{x}+J\sigma_{z}\otimes\sigma_{z}, \tag{38}\]  
where \(E_{z}^{l}=h^{l}/2+g\cos\omega t\), and \(E_{z}^{r}=h^{r}/2+g\cos\omega t\) with the static qubit energy gap \(h^{l(r)}\) for left and right qubits and the coupling constant \(g\) between the qubit and the oscillating magnetic field with frequency \(\omega=2t_{q}^{r}\).  
The fidelity for CNOT gate operation is given by \(F(t)=\text{Tr}(M(t)M_{\text{CNOT}})/4\), [28] where \(M_{\text{CNOT}}\) is the matrix for the perfect CNOT operation, and \(M(t)\) is the truth table amplitude at time \(t\). At the degeneracy point where \(h^{l}=h^{r}\), we introduce qubit states in a transformed coordinate as \(|0\rangle=(|\downarrow\rangle+|\uparrow\rangle)/\sqrt{2}\) and \(|1\rangle=(|\downarrow\rangle-|\uparrow\rangle)/\sqrt{2}\). \(M_{\text{CNOT}}\) is represented as  
\[M_{\text{CNOT}}=\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&0&1\\ 0&0&1&0\end{array}\right], \tag{39}\]  
where, if we consider the first qubit as the control qubit and the second the target qubit, \(M_{\text{CNOT}}\) shows that for the control qubit state \(|1\rangle\) the target qubit state becomes flipped, while the target qubit state remains unchanged for the control qubit state \(|0\rangle\), i.e., \(|00\rangle\rightarrow|00\rangle,|01\rangle\rightarrow|01\rangle,|10\rangle \rightarrow|11\rangle\) and \(|11\rangle\rightarrow|10\rangle\). \(M(t)_{ij}=P_{|i\rangle\rightarrow|j\rangle}\) with \(|i\rangle,|j\rangle\in\{|00\rangle,|01\rangle,\)\(|10\rangle,|11\rangle\}\) is the probability that the state \(|i\rangle\) evolves to the state \(|j\rangle\) at time \(t\), which can be calculated by using the Hamiltonian in Equation (38) [27]. In Table 1, we show the fidelity for the CNOT gate operation, where \(F_{0}\) is the fidelity with the given coupling energy \(J\) consisting of both the Josephson junction energy and the inductive coupling energy. If we neglect the inductive coupling energy, \(J_{1,\text{ind}}/h=0.2\) GHz in Figure 1 and \(J_{2,\text{ind}}/h=0.06\) GHz in Figure 2, the fidelities \(F_{1}\) and \(F_{2}\) can be calculated with the coupling energies \(J_{1}=J-J_{1,\text{ind}}\) and \(J_{2}=J-J_{2,\text{ind}}\), respectively. For a weak two-qubit coupling regime, \(J/h\sim 0.5\) GHz, the fidelities \(F_{1}\) and \(F_{2}\) deviate severely from \(F_{0}\) as shown in Table 1. Even for the strong coupling regime, \(J/h\gtrsim 1\) GHz, we can observe in Table 1 that the contribution of inductive coupling should be exactly included to satisfy the criteria of the fidelity error, \(\delta F\sim 10^{-4}\), of the fault tolerant quantum computing.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 1 Introduction
Using simulations, we compare the KTFW with both the standard BEB and the queue backoff (QB) [19], a high-throughput broadcast algorithm, and measure their performances in terms of _throughput_ and _average packet latency_ under various traffic injection models, jamming rates, and network sizes. We quantify these performance metrics for two different adversarial models of packet injection: the leaky bucket injection model (LBIM) and the randomized injection model (RIM) [20; 21]. Through experiments with real network traffic and using a modified KL-divergence metric, we show that the considered models provide an accurate and inclusive approximation of real-world network traffic. Our evaluation shows that the KTFW algorithm makes MAC networks significantly more resilient to jamming attacks than the BEB and the QB algorithms. Under jamming, the KTFW achieves higher throughput compared to both the BEB and the QB. As the network size increases, the KTFW's advantage over the BEB increases even further. This superiority is because the throughput of the BEB degrades as the network size increases while the KTFW maintains a high throughput regardless. For a network with 100 nodes, the KTFW can achieve up to 50% more throughput than the BEB for certain jamming rates. The KTFW algorithm has a significant and consistent superiority over both the BEB and QB algorithms in terms of the average packet latency and achieves much lower average packet latencies for all jamming rates and network sizes. Interestingly, our results reveal that, even with no jamming, the KTFW outperforms the BEB algorithm in terms of throughput and latency for larger network sizes. Although the KTFW algorithm requires the node clocks to be synchronized, it is mitigated through a synchronization step executed once by new connected nodes. The rest of the paper is organized as follows. In Section 2, we review the related works. Then, in Section 3 we describe some of the technical preliminaries. Afterward, we present the new proposed broadcast algorithm in Section 4, and then we define the traffic injection model in Section 5. Next, the jamming model is detailed in Section 6 and the evaluation is discussed in Section 7. Section 8 concludes the paper.
## 5 Results and Discussion
### Meshed Configuration Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Nodes**} & \multicolumn{2}{c}{**Sizes (Mvar)**} & **Annual Costs (USD/year)** & **Red. (\%)** \\ \hline Ben. Case & — & — & — & 86,914.74 & 0.00 \\ COUENNE & \([5\] & 6 & 11 ] & [0.0000 & 0.32577 & 0.23872] & 83,254.95 & 4.21 \\ BONMIN & \([15\] & 16 & 17 ] & [0.11818 & 0.00391 & 0.34700] & 81,171.76 & 6.61 \\ SSA & \([14\] & 30 & 32 ] & [0.14620 & 0.39440 & 0.20230] & 77,870.17 & 10.41 \\ \hline GNDO & \([14\] & 30 & 32 ] & [0.11554 & 0.46482 & 0.15147] & 77,834.42 & 10.45 \\ \hline \hline \end{tabular}  
* The numerical results shown in Table 7 imply that:
* Once again, the COUENNE and BONMIN solvers got stuck in locally optimal solutions, which is attributed to the nonlinear non-convex nature of the exact MINLP model. The COUENNE solver only reached a reduction of 4.21% with respect to the benchmark case, while the BONMIN solver yielded a better local solution, with a reduction of 6.61% with respect to the benchmark.
* The proposed GNDO approach found a solution with an expected reduction in the annual operative costs of about 10.45%, i.e., 0.04% better than the solution reported by the SSA approach. However, the main characteristic of both solutions is that these located the D-STATCOMs in the same set of nodes (14, 30, and 32). However, their sizes differ, which explains the difference in the final objective function value.
* The total installed size of the D-STATCOMs with the GNDO is 731.83 kvar, whereas the SSA installed 742.90 kvar, which implies that with a better definition of the D-STATCOM sizes it is also possible to reach better final objective function values. Nevertheless, the main characteristic of this behavior is that based on its probability functions, the GNDO explored and exploited the solution space with better sensitivity than the SSA approach.  
\end{table} 
Table 8: Additional improvements reached when variable reactive power is used for daily compensation with D-STATCOMs for the meshed grid configuration.
# Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration
## 4 Discussion
### Targeted Recommendations
#### 4.2.2 Presentation of Recommendations  
In view of the critical risks of the above six nodes and the existing deficiencies of the driverless, the following recommendations are made to the research institutes, software and hardware suppliers, and vehicle companies in the driverless field:  
1. While improving the sensor accuracy, a multi-sensor fusion sensing system is adopted to achieve sensing redundancy to cover the whole scene sensing. A road sensing network based on multi-task learning is adopted to ensure the accuracy and robustness of the environment sensing system in complex traffic environments.
2. With the help of China's high-precision Beidou satellite navigation system and the development of Telematics based on the network's high-speed transmission of 5G technology, there is a need for real-time updating of high-precision maps and improvement of the software functions of the AI Learning Decision System, to ensure the timeliness, flexibility, and accuracy of the decision system.
3. For the occurrence of emergencies, the driverless vehicle should quickly make security decisions, and through adaptive control strategies for emergency risk avoidance operations, automatically stop the vehicle operation to ensure the timeliness and stability of the control system, and effectively ensure the absolute personal safety of the vehicle personnel.
## 4 Photon Diagnostics Systems  
X-ray photon diagnostics is required for monitoring the photon pulse parameters generated by the European XFEL [41; 42; 43; 44]. The diagnostics systems provide essential information to the machine for setup, operation and optimization of the accelerator, undulator and X-ray optics, especially during commissioning. Diagnostics is also mandatory for normalization and interpretation of the experimental data. Several beam properties will be measured by so-called online methods, that is, for each photon pulse and with minimal distortion of the pulse. Examples are the pulse energy and beam position, but also spectral content and information about temporal properties can be collected through these systems. Pulse-to-pulse capability is challenging because of the 4.5 MHz repetition rate, but it is particularly important to be able to normalize data for fluctuations of photon pulse parameters due to the SASE process or due to electron or X-ray beam instabilities. In addition, for setup and specific measurements several invasive photon diagnostic systems are installed which stop the X-ray pulses, or at least severely modify the pulse properties.  
In this section, we only describe photon diagnostic devices that will be employed in the photon transport sections inside the tunnels. Further systems are integrated in the scientific instruments in the experiment hall. These include, in particular, the temporal diagnostic systems [45; 46] employed to monitor the X-ray pulse arrival, pulse duration, and, ideally, the temporal shape as shown previously [47; 48; 49; 50].
## 4 Nutritional and Bioactive Potential of Fresh Fruit and Vegetable Waste
### Dietary Uses of Fruit and Vegetable Waste
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Fruit or Vegetable Waste** & **Suggested Cooking Procedure** & **Ref.** \\ \hline Apple peel & Cookies: peels were blanched in hot water for 30 s, washed and dried (48 h, 60 \({}^{\circ}\)C), ground, and used to make cookies. & [190] \\ Citrus peel & Jam: washed and crushed peels were mixed with water to form a puree, and then sugar, pectin, and citric acid were added to obtain a jam-like texture. & [191] \\ Passion fruit peel & Cookies: peels were immersed in water (100 \({}^{\circ}\)C, 15 min) and cooled on ice, epicarp was separated with a spoon, dried (60 \({}^{\circ}\)C), ground, and used to make cookies. & [192] \\ Pineapple core & Pineapple dried core: core was cut into small pieces and dried in a microwave oven. Pineapple core fiber was extracted and incorporated into dough and steamed bean. & [193] \\ Pineapple peel & Beef marination: the peel was washed, cut, mashed with a blender, and filtered; the juice was then used to marinate beef. & [194] \\  & Gluten-free muffins: peels were sanitized with 100 ppm chlorinated solution for 15 min, dried for 8 h at 60 \({}^{\circ}\)C, and milled to obtain flour. & [195] \\ Plum peel & Marmalade: peels were mixed with water and filtered to make a puree, and then sugar syrup, pectin, and citric acid were added to obtain marmalade. & [196] \\  & Jam: washed and crushed peels were mixed with water to form a puree, and then sugar, pectin, and citric acid were added to obtain a jam-like texture. & [197] \\ Potato peel & Potato peel chips: peels were dried, milled, and sieved to obtain flour, then mixed with wheat flour to make deep-fried chips. & [198] \\  & Potato peel dough: peels were dried at 50 \({}^{\circ}\)C in an oven for 48 h, ground in a mixer grinder to make flour, mixed with wheat flour, and incorporated into dough. & [200] \\  & Gluten-free bread: peels were sanitized with sodium hypochlorite solution, dried at 70 \({}^{\circ}\)C for 24 h in a forced-air oven, and ground using a blender; the flour obtained was used to produce gluten-free bread. & [201] \\ Pumpkin rind and seeds & Braed: finds and seeds were dried in an oven at 60 \({}^{\circ}\)C for 24 h and ground using a grinder to obtain a fine particle size, then the flour obtained was incorporated into bread. & [202] \\ Watermelon rind & Marmalade: rind was mixed with water and filtered to make a puree, and then sugar syrup, pectin, and citric acid were added to obtain marmalade. & [203] \\  & Watermelon rind flour: rind was cut into small chips, dried at 60 \({}^{\circ}\)C for 48 h, and milled into powdered flour to use in cakes. & [204] \\ Watermelon seeds & Watermelon snacks: seeds were ground in a mixer grinder into a fine powder and sieved to obtain uniform size distribution of the grains; the flour obtained was mixed with rice and corn flour and used to make snacks. & [205] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of possible uses and cooking procedures for several fruit and vegetable wastes.
## 2 Data Preprocessing
### Removing Noise  
In data preprocessing, it is important to remove noise. For earthquake signals, the main problem is that the measured earthquake signals do not accurately represent the actual earthquake signals we are trying to discover. These pulse signals interact with the real earthquake signals (i.e., the signals we need) and generate the final mixed signal that is received by our seismometer (this process also occurs in earthquake simulation laboratories). Therefore, the main goal of seismology is to accurately identify and separate the real earthquake signals from the mixed signals. Wavelet denoising is a relatively general signal denoising method that can be applied to various types of signals, such as audio signals, image signals, biological signals, etc. [29; 30; 31; 32]. Its advantage is that it can effectively remove noise in the signal while retaining the important features of the signal. It should be noted that the original signal we are dealing with is the convolution of artificial pulse signals and real earthquake signals, which is why we need to perform wavelet decomposition. The process is similar to deconvolution, which can reverse the convolution process and extract the real earthquake pulse signals from the mixed seismic records and artificial pulse signals [33; 34; 35; 36]. To understand the randomness in the signal, we use the median absolute deviation (MAD) value to determine the minimum threshold of the wavelet coefficients in the time series [37; 38]. Then, we filter out the low coefficients from the wavelet coefficients and reconstruct the real earthquake signals from the remaining coefficients. This successfully removes the pulse signals from the seismic records and obtains the real earthquake signals, as shown in Figure 2.
## 4 Results and Discussion
### Quantitative Evaluation of Overpressure in Different Genesis Types
#### 4.3.2 Uplift Erosion Unloading Effect  
The uplift erosion unloading effect is a key factor contributing to the abnormal low pressure observed in petroleum basins. As a result of the uplift erosion unloading effect, the pressure in the overlying rock layers decreases, leading to a rebound phenomenon in both the rock skeleton and the volume of reservoir fluid contained within it. During this rebound process, if the volume of fluid rebound in the reservoir is less than the volume of rock skeleton rebound, a relative increase in void space occurs, resulting in a decrease in pressure on the formation fluid and, consequently, a reduction in fluid pressure [50; 51]. The pressure drop caused by the uplift erosion unloading effect can be quantitatively assessed using the following formula:  
\[\Delta P_{\rm E}=\frac{1}{3}\frac{1+v}{1-v}\frac{C_{\rm r}}{C_{\rm r}+C_{\rm w }}\rho_{\rm r}g\Delta h \tag{5}\]  
where \(\Delta P_{\rm E}\) is the pressure drop caused by erosion unloading, MPa; \(v\) is the Poisson's ratio of the rock; \(C_{\rm r}\) is the compressibility coefficient of rock; \(C_{\rm w}\) is the compressibility coefficient of water; \(\rho_{\rm r}\) is the average density of eroded rock formations, g/cm\({}^{3}\); \(g\) is the acceleration due to gravity; and \(\Delta h\) is the thickness of eroded rock formations, m. In this study, the value of \(v\) is 0.24, the value of \(C_{\rm r}\) is 1 \(\times\) 10\({}^{-3}\) MPa\({}^{-1}\), and the value of \(C_{\rm w}\) is 3 \(\times\) 10\({}^{-4}\) MPa\({}^{-1}\). The mudstone acoustic time difference method was employed to quantify the extent of erosion during the last uplift. The results indicated that the study area experienced an amplitude ranging from 1.3 to 3.5 km during this uplift, with W207 exhibiting the greatest erosion thickness of approximately 3524 m. This finding aligns closely with the calculations reported by [PERSON] et al. [52] and [PERSON] et al. [53].  
Using Formula (5), we quantitatively assessed the pressure reduction caused by the unloading effect of uplift erosion. The results showed that the pressure reduction caused by uplift erosion unloading varied from 14.07 to 38.55 MPa, averaging 26.68 MPa. Notably, the largest pressure reduction, recorded at 38.55 MPa, occurred in Well W207 (Figure 7c, Table 2).
## 4 Optimal Frame Generation Time Threshold Calculation
### Algorithm Design  
The optimal frame generation time threshold is derived based on the policy. The policy is typically represented by \(\tau_{\theta}\). When the state is \(s_{t}\), the probability of the agent executing action \(a_{t}\) based on the parameters \(\theta\) is given by Equation (16):  
\[\tau_{\theta}(a\mid s)=p[a_{t}=a\mid s_{t}=s,\theta] \tag{16}\]  
where \(\theta\) is the weight parameter vector of the policy function; \(\tau_{\theta}(a\mid s)\) is the policy function obtained by function fitting of the parameter vector \(\theta\). When the policy gradient is \(\
abla\hat{E}(\theta_{t})\) and the learning rate is \(\alpha\), the formula for updating the policy parameter \(\theta\) of the objective function by using the gradient rise method is as shown in Equation (17) :  
\[\theta_{t+1}=\theta_{t}+\alpha\
abla\hat{E}(\theta_{t}) \tag{17}\]  
In this paper, the transition function \(P(s_{t+1}|s_{t},a_{t})\) is used to represent the probability that the environment state is transferred from \(s_{t}\) to \(s_{t+1}\). The actions made by the agent will affect the state of the environment and the related reward values. For example, the frame time threshold calculated by the time slot \(t-1\) will affect the average queue length, average frame time and average frame length in the time slot \(t\). If the threshold value is large, the number of packets combined in the data frame will increase, and the average queue length will decrease. At the same time, the average frame time and average frame length will also increase.  
The policy optimization and loss function structure presented in this section is formulated by the authors to support stable training in the context of real-time adaptive framing. The specific expressions for the surrogate objective, critic loss, and entropy term reflect our design choices.
## 3 Major Agriculture Applications and Services  
All the traditional approaches have been changed and overlapped by advanced technologies, such as the IoT and UAVs. Currently, the implementation of various wireless sensors and IoT sensors leads to the gateway of many innovations for crop improvement. Several traditional crop issues, including disease management, effective irrigation, cultural practices, and drought responses, are currently addressed by these new emerging technologies. Figure 2 depicts the primary applications, wireless sensors, and services hierarchy for advanced agricultural applications. The advanced technologies used to monitor major applications to improve crop productions are discussed below.
## 4 Simulation Analysis
### Algorithm Verification and Compensation Result Analysis  
This paper proposes a C-LM algorithm to solve the problem of easily falling into local optima when identifying geometric parameters simultaneously. To verify the sensitivity of the C-LM algorithm to the initial parameter values, convergence analysis was performed using different calibration algorithms on the TM-MDH model with different preset parameters (\(\theta_{1}\) and \(\alpha_{2}\)). A deviation vector \([\Delta\theta_{1},~{}\Delta\alpha_{2}]\) was added to the nominal parameters, where \(\Delta\theta_{1}\) and \(\Delta\alpha_{2}\) varied from \(-3.1\) rad to \(3.1\) rad with equal intervals. Under \(63^{\circ}63\) sets of initial parameter conditions, different algorithms were used to validate the convergence of these cases.  
Table 8 presents a comparison of the convergence indicators of the LM algorithm and the C-LM algorithm under different initial conditions. The criteria include run time and convergence ratio. From Table 8, it can be observed that both the LM algorithm and the C-LM algorithm had cases where the results did not converge, even after reaching the maximum number of iterations. However, the C-LM algorithm exhibited a higher global convergence ratio of \(53.16\%\). The specific convergence conditions of the LM algorithm and C-LM algorithm are shown in Figure 6, which displays three-dimensional color maps of the final orientation error and position error of the cases with different initial values \([\Delta\theta_{1},~{}\Delta\alpha_{2}]\). It can be concluded that when the norm of the bias vector is relatively small, the algorithms tend to converge to the global optimum, as reflected by the large flat blue region in the middle of the three-dimensional color map. Clearly, the proposed C-LM algorithm has a broader global convergence range. That is to say, The C-LM algorithm is insensitive to the initial values of the parameters to be identified.  
Table 9 gives the identification results of the industrial robot's structural parameters based on the C-LM algorithm, with and without introducing transformation parameters \(u\) and \(v\). In this experiment, the true pose data of the TCP included Gaussian measurement noise with standard deviations of \(0.02\) mm for position noise and \(0.002\) rad for orientation noise. The position errors of the TCP relative to the BCF and the deviations along each axis are shown in Figure 7, while the orientation errors and the deviations around each axis are shown in Figure 8. The statistical results (mean, minimum, and maximum) are presented in Tables 10 and 11.  
Figure 8: The orientation errors and the deviations around each axis.  
Figure 7: The position errors and the deviations along each axis.