esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This research centers on the application of DBN to predict sensing outcomes in a flexible sensing design that combines FBG sensors with a simulated fiber-optic interferometer sensor. It is known that the coexistence of FBG sensors and fiber-optic interferometer sensors in the sensing architecture can perform diverse sensing tasks due to the high sensitivity of fiber-optic interferometers and the advantage that multiple FBGs can be arranged in an array. However, in practice, to maximize the number of FBG sensing points, the problem of FBG spectral overlap will inevitably occur. The overlap of the overlapping FBG spectrum and the spectrum sensed by the fiber interferometer will inevitably cause the entire spectrum power to be modulated, making it challenging to accurately extract sensing information. In this case, the use of machine learning, specifically the DBN model, offers an optimal solution for accurately interpreting the sensed information. The DBN model can effectively predict all sensed information for various levels of overlapping FBG spectra under different spectrums sensed by the interferometer, ultimately enhancing the cost-effectiveness, flexibility, and accuracy of the sensing system.  
Figure 8: (**a**) Peak wavelength prediction of strained FBG after applying the DBN model. (**b**) Peak wavelength prediction of fixed FBG and three interferences after applying the DBN model.  
**Author Contributions:** Conceptualization, S.D.B., C.-K.Y., Y.C.M. and P.-C.P.; methodology, S.D.B., C.-K.Y. and Y.C.M.; software, S.D.B. and Y.C.M.; data curation, C.-K.Y. and Z.-G.Z.; validation, S.D.B., C.-K.Y. and Y.C.M.; formal analysis: S.D.B., C.-K.Y., Y.C.M. and P.-C.P.; investigation: S.D.B., C.-K.Y., Y.C.M., Z.-G.Z. and P.-C.P.; visualization: C.-K.Y.; writing--original draft preparation: C.-K.Y. and S.D.B.; writing--review and editing: S.D.B., C.-K.Y., Y.C.M. and P.-C.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Vehicular Localization Enhancement via Consensus
## 5 Evaluation
For thorough observation, multiple probabilities of the communication failure were sampled from range of 0 to 0.8. Each pair of \(\sigma_{A}^{2}\) and \(\sigma_{\text{CM}}^{2}\) of equal values are categorized by the same color of the graph. The figure shows that, for each pair of the measurement noise levels, the CDFs of the distributed protocol  
Figure 6: Comparison with centralized approach. increase more rapidly than the those of the centralized protocol at lower estimation errors below 40 m. This indicates that the distributed mode exhibits more localization results with lower estimation errors. However, for estimation errors above 40 m, the CDFs of the distributed mode converge more slowly to 1 compared to those of the centralized mode. This indicates that low quality measurements of the anchor can be propagated throughout the network in the distributed mode, degrading the overall quality of the localization. While the distributive localization can provide high-accuracy estimation in most cases, the message exchanges are therefore inhibited when measurements of such low qualities are made in order to contain erroneous measurement. To observe whether the proposed strategy achieves the optimal performance, Figure 8 compares the CRLB obtained in (13) and the mean squared error (MSE) of the resulting location estimates under different circumstances. The horizontal axis of the figure indicates the difference between the noise levels of the self-localization of the anchor and the agents, respectively denoted by \(\sigma_{\text{self,anchor}}\) and \(\sigma_{\text{self,agent}}\). With \(\sigma_{\text{self,agent}}\) fixed to 30 m, \(\sigma_{\text{self,anchor}}\) decreases from 30 m to 0 m along the axis. The figure shows that the MSE of the proposed strategy under no hindrance, labeled as 'full capability,' coincides with the CRLB. This indicates that the proposed strategy successfully achieves the optimal performance of the estimator when no particular impediment exists. However, as examined from the previous observations, unfavorable settings may result with updates that are less accurate than the initial estimates. Specifically, when the difference \(|\sigma_{\text{self,anchor}}-\sigma_{\text{self,agent}}|\) is less than about 3 m, the minimum achievable MSE of the estimator given by the CRLB is higher than the MSE of the initial estimates. This again validates in a new perspective that in order to benefit from the proposed strategy, anchor's superiority of preciseness over the agents is required. The MSE of the proposed strategy under communication setbacks are also compared with the CRLB. In particular, results under 25% and 50% node failure probabilities are shown, respectively. As can be intuitively deduced, the proposed strategy is strays farther from the optimal performance as communication failures occur more often. The initial MSE can also be compared with the MSE of these particular cases to determine whether the utilization of the proposed strategy is desirable. Figure 7: Cumulative distribution function of the localization error of the proposed strategy.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
###### Abstract  
In the field of high-precision physical field detection, measurements based on magnetic signals are crucial due to their exceptional accuracy, sensitivity, and stability. Superconducting Quantum Interference Devices (SQUIDs), with their ultra-high sensitivity, have become key in detecting minute changes in magnetic flux. However, with increasing demands for higher precision, further enhancing device performance under quantum constraints remains a significant challenge. This study improved the precision of SQUID sensors by optimizing the geometry of microscale washers using the physical response of superconducting films under micromachining technologies and quantum constraints. Our research extensively examined the effects of square, octagonal, and circular washers on the magnetic field response, shielding currents, and inductance. Experimental results showed that, compared to traditional square washers, octagonal washers significantly enhanced sensor sensitivity and efficiency by minimizing edge magnetic flux and optimizing current distribution. Notably, all observed inductance values exceeded predictions based on traditional empirical formulas, with square washers showing the highest average relative error of 80.8%, while circular and octagonal washers had errors of 65.31% and 66.43%, respectively. This breakthrough not only a new theoretical foundation for the design of SQUID sensors but also provides robust evidence for enhancing magnetic field detection through microscale technological innovation.  
washed; SQUID; python; inductance +
Footnote †: journal: _aciences_
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 4 In-Field Interference Detection Modules
### Chi-Square Goodness of Fit (GOF) Test  
The second interference detection technique implemented in NGeneApp is the Chi-square GoF test, which acts a post-correlation monitoring point along the receiver chain [43]. It is based on a test statistic for monitoring the distortion of the signal correlation function in the tracking stage of each received satellite signal.  
The algorithm is based on the fact that in the nominal case, i.e., when no interference is present, the code correlation for each satellite signal is an even function; each pair of Early (E) and Late (L) correlators equally spaced from the Prompt (P) (the early-prompt spacing and the late-prompt spacing are equal, i.e., \(d_{EP}=d_{LP}\)) can be modeled as a pair of normally distributed random variables with the same mean, i.e., \(\mu_{E}=\mu_{L}=\mu\), and variance that depends on the (C/N\({}_{0}\)). On the other hand, in the presence of an interfering signal, the E and L point correlation distributions significantly differ, because of the induced code correlation distortion. If the early-late spacing \(d_{EP}\) exceeds 1 chip, then it is possible to show that E and L are independent and \(D=E-L\) results to be a normally distributed random variable with zero mean, \(\mu_{D}=0\). Then, the test statistic is built on the vector of differences \(D\). Based on these assumptions, the GoF algorithm consists in evaluating the distribution of \(D\), against the expected distribution, i.e., the one calibrated in nominal conditions. The GoF is able to estimate how much the two distributions differ, by means of a statistical metric, the so-called \(p\)-value, which is the probability that the two distributions have the same statistical characteristics. When no disturbances affect the signals and the correlation shape is not distorted, the distribution of \(D\) is similar to the one calibrated in nominal conditions, and the \(p\)-value is close to one. On the contrary, in a critical scenario where interference distorts the correlation, the \(p\)-value assumes smaller values. A threshold mechanism is used to decide on the binary hypothesis, set on the basis of the'significance level' of the test [44]. For a thorough theoretical description of the GoF statistical test, the reader can refer to [43,44]. Hereafter, the in-laboratory calibration phase as a function of the C/N\({}_{0}\) signal ratio in nominal conditions and the on-field test results are presented in Sections 5.2 and 6.3 respectively.
## 7 Discussion
### Cache Size Validity  
The simulation results show that the cache performance, including cache hit ratio, request latency, etc., improves slowly when the total cache size is large. We follow the principle that important nodes in the network are allocated large cache space. However, even if the importance of nodes can be expressed very accurately, the optimal cache allocation may not be fully proportional to the importance. For example, nodes A and B have weights of 2 and 8, respectively. With a total cache of 10, allocating 4 and 6 cache space to two nodes may work better than allocating 2 and 8. To improve cache performance, some adjustments need to be made based on the validity of the size of the cache, which is based on the allocation of cache space by node importance.
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 1 Introduction
We derive the accurate partial derivatives of reflection coefficients with respect to dry rock parameters (bulk and shear moduli) through combining the exact Zoeppritz equations and the Biot-Gassmann equations. During the numerical tests, the characteristics of these partial derivatives are observed. Additionally, we compare the partial derivatives and their errors using this proposed method with those from the classical Aki-Richards and Shuey approximations.
## 5 Results and Discussion
### Assessment of the Measurement Model
Table 6 shows the EFA results 6. All items loaded well to the latent constructs that they were designed to measure without any cross-loading issues; we thus confirmed the validity of measurement model. Finally, we verified discriminant validity to confirm that the items were not related to other items or latent constructs other than the latent constructs they were designed to measure. As shown in Table 7, the values in the diagonal representing the square root of AVE for each latent construct were greater than the longitudinal and transverse correlation values. We thus confirmed discriminant validity. The measurement model test results enabled us to fully verify the reliability and validity of each measure, which allowed us to formulate the structural model to test the proposed hypotheses. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Latent Construct** & **\# of Item** & **Removed Item** & **Cronbach’s Alpha** \\ \hline Irrelevant Information Overload & 3 & 0 & 0.793 \\ Open Reachability & 3 & 0 & 0.822 \\ Engagement & 4 & 1 & 0.870 \\ Maintaining Self-Reputation & 3 & 0 & 0.800 \\ SNS Fatigue & 4 & 1 & 0.826 \\ Living Disorder & 4 & 1 & 0.789 \\ Experience of Privacy Violations & 3 & 0 & 0.817 \\ Reduced Intention to Use SNSs & 4 & 0 & 0.862 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Reliability testing results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Latent Construct**} & \multicolumn{5}{c}{**Descriptive Statistics**} \\ \cline{2-6}  & **N** & **Mean** & **Minimum** & **Maximum** & **St.Dev** \\ \hline Irrelevant Information Overload & 579 & 5.071 & 2 & 7 & 0.063 \\ Open Reachability & 579 & 0.357 & 1 & 7 & 0.076 \\ Engagement & 579 & 0.416 & 2 & 7 & 0.100 \\ Maintaining Self-Reputation & 579 & 0.533 & 2 & 7 & 0.137 \\ SNS Fatigue & 579 & 0.448 & 1 & 7 & 0.099 \\ Living Disorder & 579 & 0.556 & 2 & 7 & 0.053 \\ Experience of Privacy Violations & 579 & 0.454 & 1 & 7 & 0.778 \\ Reduced Intention to Use SNSs & 579 & 0.396 & 1 & 7 & 0.105 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics results. Since we used various measures in relation to the same participants and since each construct showed a relatively high degree of correlation, common method bias (CMB) was a real possibility in this study. To test for CMB issues, we used [PERSON]'s [48] single factor method. The method performs exploratory factor analysis on all the variables and assumes that bias exists when variance for a single factor is large based on factors that had eigenvalues greater than 1 [49]. Unrotated factor analysis using principal component analysis yielded eight factors. The first factor explained about 32.9% of the variance, which certainly does not constitute the majority of the total variance, given that [PERSON] et al. [50] reported that 40% was not a majority of the variance. Thus, the result of [PERSON]'s single-factor test indicated that the CMB in this study was not likely a source of major issues.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 6 Conclusions  
This paper studied the jamming method against the FDA-MIMO beamforming level, and the main conclusions are as follows:  
1. By studying the FDA-MIMO deceptive jamming model, an effective jamming method against FDA-MIMO radar was analyzed;
2. A suppression and range deceptive compound jamming method against FDA-MIMO beamforming was proposed. The beamforming gain and range bin estimation level were simulated and analyzed. Compared with the existing deceptive jamming methods, the proposed jamming method can obtain the same beam gain as the target, and it can produce a significant compound jamming effect for the range parameter estimation performance of FDA-MIMO radar;
3. The simulation results showed that the output SINR of the proposed jamming increased more slowly, and when the input SINR was 10 dB, the output SINR was also about 10 dB, and the output SINR could always be kept at a low level, so the proposed jamming method can produce an effective compound jamming effect, and the jamming effect was good.  
The jamming method studied in this paper for the frequency diverse array system radar also had a certain reference value for the jamming of new system radars and also provided a guiding meaning for the application and improvement of frequency diverse array system radar in complex electromagnetic environments. Future work could be carried out from the adaptive cognitive jamming technology.  
Conceptualization, Y.C. and B.T.; methodology, Y.C. and Y.Z.; software, Y.C. and J.G.; validation, Y.C. and B.T.; data curation, Y.C. and C.W.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and B.T.; Supervision, C.W.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
Figure 12: The output SINR performance of NFT under anti-jamming technology.  
**Funding:** This work was supported by the Natural Science Foundation of Shaanxi Province under grant 2021 JM-222 and the China Postdoctoral Science Foundation under grant 2019M662257.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Results Analysis
#### 5.1.4 The Result Analysis of Carbon Emissions  
Carbon emissions are directly affected by TGC price and CET price, reflecting the environmental benefits of energy production methods. With the increase in the TGC quota ratio, the price of TGC has risen. Driven by economic benefits, power enterprises have increased their investment and application in renewable energy generation projects, significantly reducing the carbon emissions generated by fossil fuel power generation [14; 50]. When the TGC quota ratio increases by 30%, the reduction in carbon emissions becomes more pronounced. Significantly increasing the TGC quota ratio may face supply and demand pressures in the short term. However, in the long run, it can lay a solid foundation for achieving carbon reduction goals. CET price fluctuations have a more direct impact on carbon emissions. As a binding policy, when the carbon emission cap target is reduced by 30%, the CET price rapidly increases. Under immense economic pressure, enterprises are forced to take emission reduction measures, resulting in a significant decrease in carbon emissions in the electricity market [51]. On the contrary, when the carbon emission cap target is relaxed, the CET price remains stable, and power enterprises have no economic pressure to invest in renewable energy projects, leading to an increase in carbon emissions. Carbon emission cap targets affect the effectiveness of carbon reduction through carbon price signals [52]. Therefore, policy design should find a balance between the two, improving the economic and environmental benefits of the electricity market through economic regulation and market supervision.
# A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm
## 3 The Proposed Algorithm
### Design of Foraging Strategy
\begin{array}{ll}1&if\;Lmark[i,j,k]=1\\ \frac{[\tau_{ijh}^{i}]^{*}\eta_{ijh}]^{\beta}}{\sum\limits_{h\in alloed_{h}}[ \tau_{ijh}]^{\alpha}[\eta_{ijh}]^{\beta}}&if\;k\in allowed\;i\\ 0&Otherwise\end{array} \right. \tag{15}\]  
where the meaning of each symbol and the calculation method of the heuristic factor are the same as those presented in Formula (14). In the artificial bee colony algorithm, scout bees are mainly responsible for exploring in the whole empty frame of the search space, finding new food sources and guiding the optimization process out of local optimization. while the global pheromone matrix will reflect the preferred search area and search intensity of the current employed bee, the local pheromone matrix is able to reflect the preferred search area and search intensity of the observation bee. When the employed bee abandons the food source and turns to the onlooker bee for the search, this means that the employed bee thinks it is difficult to find a better food source in these areas, and exploration should be directed towards other areas. Therefore, in the DPD-HABC algorithm, when the employed bee abandons the food source, a new candidate food source is generated in a region far from the employed bee's current preference by combining the global pheromone matrix with the local pheromone matrix. For the employed bee \(l\), when it becomes a scout bee, when it is at the \(j\)-th node of the \(i\)-th layer in the process of generating a candidate food source, the selection probability formula used to select the node \(k\) of the next layer is calculated as shown in Formula (16). \[P_{ij}^{k}=\left\{ \begin{array}{ll}\frac{[\tau_{max}-(\tau^{\delta}_{ijk}+ \tau_{ijk})/2]^{\alpha}*[\eta_{ijk}]^{\beta}}{\sum\limits_{h\in alloed_{h}}[ \tau_{max}-(\tau^{\delta}_{ijk}+\tau_{ijk})/2]^{\alpha}*[\eta_{ijk}]^{\beta}}& if\;k\in allowed_{h}\\ 0&Otherwise\end{array} \right. \tag{16}\]  
where \(\tau_{max}\) represents the upper bound of the value of the pheromone in the pheromone matrix. When a new candidate food source is constructed by roulette according to the probability formula, the scout bee turns into an employed bee in order to search again, takes the newly generated candidate food source as the current best food source, and reinitializes the local information matrix by flipping it, that is, making any element in the local pheromone matrix \(\tau_{ijk}^{l}=\tau_{max}-\tau_{ijk}^{l}\). Evaluating food sources is an essential step for the DPD-HABC algorithm when solving the problem of degenerate primer design. Here, the food sources are primer pairs. When comparing the food sources, this paper divides them into three cases: the two compared food sources meet the constraints; one of them satisfies the constraints, while the other does not satisfy the constraints; and neither meets the constraints.
# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
###### Abstract  
In this study, we proposed a system to reduce the speaker's suffering from the strong light of a beam projector by applying regional brightness control over the screen. Since the original image and the projected one on the screen are quite different in area, brightness, and color, the proposed system first transforms them so that they have the same area and similar color tone. Then, to accurately determine the difference between those images, we have introduced a SSIM map, which is a perception-based method of measuring image similarity. Accordingly, an image segmentation model is used to determine the speaker's silhouette from the SSIM map. We applied a couple of well-trained segmentation models, such as Selfie and DeepLab-v3, provided with MediaPipe. The experimental results showed the operability of the proposed system and that it determines most of a lecturer's body area on the screen. To closely evaluate the system's effectiveness, we have measured error rates consisting of false-positive and false-negative errors in the confusion matrix. With the measured results, the error rates appeared so insignificant and stable that the proposed system provides a practical effect for the speakers, especially in the case of applying DeepLab-v3. With the results, it is implied that an accurate segmentation model can considerably elevate the effectiveness of the system.  
multimedia system; beam projector; brightness control; SSIM; segmentation Article  
A Regional Brightness Control Method  
Avoid Human Glare  
Hyeong-Gi Jeon  
1 Smart ICT Convergence HRD Center, Pai-Chai University, Daejeon 35345, Republic of Korea;  
jeonhk@pcu.ac.kr  
2