esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Finite Element Model Accuracy Verification  
The Figure 17 compares experimental data (black line with square markers) and numerical simulation results (red line with circular markers). The overall trends show good agreement, indicating the numerical model accurately captures the system's behavior. However, discrepancies are noted in regions where experimental data fluctuates more significantly. These differences may stem from experimental errors, model simplifications, or unaccounted material property variations. Overall, the numerical simulation reliably predicts experimental outcomes with minor deviations in certain areas.
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 3 Method
### Attention-Based Feature Extraction  
The attention mechanisms that are widely used in computer vision are dynamic weight adjustment processes that can find salient regions in complex scenes [43; 44; 45; 50; 51; 52; 53; 54; 55; 56]. Thus, we adopted attention mechanisms that could extract meaningful features from salient regions to use as clues to predict the depth of complex scenes. Attention mechanisms can be divided into three categories: channel-wise, spatial-wise, and mixed attention mechanisms. Channel-wise attention methods adaptively recalibrate the weight of each channel to focus on meaningful features [44; 50; 51; 52; 53; 54]. Spatial-wise attention methods focus on finding the relationships between pixels and training planar attention maps with corresponding weights for different regions within images [43; 55; 56; 57; 58; 59; 60]. Mixed attentions methods combine the advantages of the other two types of attention mechanism to adaptively fine-tune the weight of each channel and planar map [45; 61; 62; 63; 64; 65]. Therefore, we took advantage of mixed attention mechanisms to extract features. Firstly, ResNet101 [48] is applied to extract multi-scale feature maps \(f_{i}\) (where \(i=1\) to 4) from RGB images, as shown in Figure 3. Then, \(f_{i}\) is sent to the attention mechanism to extract features \(f_{a_{i}}\) (where \(i=1\) to 4). The details of our attention mechanism feature extraction strategy are shown in the Experiments  
Figure 2: The architecture of our proposed model.  
Section. Finally, the extracted features \(f_{a_{i}}\) are used to predict foreground and background depth maps.
###### Abstract  
Due to the common path structure being insensitive to the environmental disturbances, relevant Fabry-Perot interferometers have been presented for displacement measurement. However, the discontinuous signal distribution exists in the conventional Fabry-Perot interferometer. Although a polarized Fabry-Perot interferometer with low finesse was subsequently proposed, the signal processing is complicated, and the nonlinearity error of sub-micrometer order occurs in this signal. Therefore, a differential quadrature Fabry-Perot interferometer has been proposed for the first time. In this measurement system, the nonlinearity error can be improved effectively, and the DC offset during the measurement procedure can be eliminated. Furthermore, the proposed system also features rapid and convenient replacing the measurement mirrors to meet the inspection requirement in various measuring ranges. In the comparison result between the commercial and self-developed Fabry-Perot interferometer, it reveals that the maximum standard deviation is less than 0.120 \(\upmu\)m in the whole measuring range of 600 mm. According to these results, the developed differential Fabry-Perot interferometer is feasible for precise displacement measurement.  
Keywords:differential Fabry-Perot interferometer; homodyne interferometer; nonlinearity error; linear displacement +
Footnote †: journal: Applied Sciences
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 1 Introduction
In feedback control, a proportional-integral (PI) controller is utilized to track the desired velocity. To avoid the jump of desired velocity, the Tracking Differentiator (TD) is introduced to calculate the transient profile of the desired velocity [26]. In feedforward control, the component of gravitational acceleration is applied as the compensation to deceleration. Furthermore, the compensation of the desired velocity is also calculated according to the road slope, which relieves the effect of the road slope. The remainder of this paper is organized as follows: Section 2 introduces the model of the driverless bus and proposes some assumptions about the model and the details of FPPC-FAR and feedback-feedforward algorithm. Section 3 gives several simulation results and discusses the advantages of the proposed methods. Section 4 implements the path-tracking algorithm in a real bus, and describes the performances of the controller. Section 5 provides a succinct conclusion.
# Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light
## 2 Theory of Ghost Imaging in Monostatic Turbulence and Bistatic Turbulence
### Monostatic Turbulence Case  
In this case, \(F_{4}(x_{1},x_{2},x_{3},x_{4};u_{2},u_{2})\) cannot be separated as the product of the ensemble average over the second-order statistics of \(\psi_{1}\) and that of \(\psi_{2}\) due to that the turbulence in two paths being statistically correlated. There are some theoretical approximation models to describe this process [43; 44; 45; 46; 47]. Here, we adopt the theoretical model based on [PERSON]'s analysis [43], and \(F_{4}\) is expressed as:  
\[\begin{split} F_{4}(x_{1},x_{2},x_{3},x_{4},u_{1},u_{2})=\exp \!\left[-0.5D_{\psi}(x_{1}-x_{2},0)-0.5D_{\psi}(x_{1}-x_{4},u_{1}-u_{2})-0.5D_{ \psi}(x_{2}-x_{3},u_{1}-u_{2})\right]\\ \times\exp\!\left[-0.5D_{\psi}(x_{3}-x_{4},0)+0.5D_{\psi}(x_{2}-x_{4},u_{1}- u_{2})+0.5D_{\psi}(x_{1}-x_{3},u_{1}-u_{2})\right]\\ \times\exp\!\left[2B_{x}(x_{2}-x_{4},u_{1}-u_{2})+2B_{x}(x_{1}-x_{3},u_{1}- u_{2})\right]\\ \times\exp\!\left[iD_{xs}(x_{2}-x_{4},u_{1}-u_{2})-iD_{xs}(x_{1}- x_{3},u_{1}-u_{2})\right],\end{split} \tag{22}\]  
whereHere, \(\rho_{0}\ =\ \left(0.546C_{n}^{2}k^{2}z\right)^{-3/5}\) is the coherence length of a spherical wave propagating in the turbulent medium, \(u_{d}\ =\ u_{1}-u_{2}\), and \(C_{n}^{2}\) is the structure constant. \(B_{x}\ =\ \left(x\right)_{i}-x_{j},u_{d}\) is the log-amplitude correlation function, via:  
\[B_{x}\big{(}x_{i}-x_{j},u_{d}\cdot\big{)}=\sigma_{xs}^{2}-\frac{1}{2}\Bigg{(} \frac{1}{\rho_{0}^{2}}-\frac{1}{\rho_{x}^{2}}\Bigg{)}\left[\big{(}x_{i}-x_{j} \big{)}^{2}+u_{d}\big{(}x_{i}-x_{j}\big{)}+u_{d}^{2}\right] \tag{24}\]  
where \(\sigma_{xs}^{2}\ =\ 0.124k^{7/6}C_{n}^{2}z^{11/6}\) is the variance of the log amplitude for a spherical wave, and \(\rho_{x}\ =\ \left(0.425C_{n}^{2}k^{13/6}z^{5/6}\right)^{-1/2}\). \(D_{xs}\big{(}x_{i}-x_{j},u_{d}\big{)}\) is the log-amplitude phase structure function via:  
\[D_{xs}\big{(}x_{i}-x_{j},u_{d}\big{)}=\left[\big{(}x_{i}-x_{j}\big{)}^{2}+ \big{(}x_{i}-x_{j}\big{)}u_{d}+u_{d}^{2}\right]/\rho_{xs}^{2} \tag{25}\]  
where \(\rho_{xs}\ =\ \left(0.114C_{n}^{2}k^{13/6}z^{5/6}\right)^{-1/2}\) is the coherence length of the log-amplitude and phase.  
In the derivation of Equation (22), the Kolmogorov spectrum with zero inner scale and the quadratic approximation are used. The validation condition is \(\sigma_{xs}^{2}\ =\ 1\) for guaranteeing weak turbulence. On substituting Equations (12) and (22) into Equations (8) and (9), after some rearrangements, we rewrite Equations (8) and (9) in the following alternative tensor forms:  
\[\begin{array}{l}I_{4}^{(m)}(u_{1},u_{2})=\frac{\exp\!\left(4\sigma_{xs}^{2} \right)}{\lambda^{2}\!\left[\det\!\left(\widetilde{B}^{-1}\right)\right]^{1/2 }\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Tools and Technologies
#### API Gateway  
* **Zuul v1** is a completely integrated extension within the Java ecosystem, developed by Netflix, picked in the asynchronous model to perform dynamic routing. One main issue with Zuul v1 is its blocking nature, prompting the development of a new variant: **Zuul v2**, which improves scalability and performance through non-blocking request handling [52].
* **Spring Cloud Gateway** is the counterpart developed by the Spring team, offering advanced routing capabilities, improved performance, and an extended set of functionalities within the reactive ecosystem [53].
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 2 Overview of System-Level Tests of the PoL SiP  
The test vehicle chosen for this study was the PoL module from 3D PLUS and different entities of this SiP (both 2D prototype boards and 3D modules) were irradiated during experiments discussed in this paper. The PoL is a custom-built system based on COTS components, with 11 references of active components (6 discrete and 5 Integrated Circuits), from different technologies (bipolar/CMOS). More details on the PoL were already given in another paper in this journal issue [22], but the PoL schematic is reminded in Figure 1:  
PoL is a space qualified product, characterized with the use of component-level tests up to TID level of 50 krad and LET level of 80 MeV\(\cdot\)cm\({}^{2}\)/mg [7].  
An overview of radiation tests of the PoL performed within this study is given in Table 2. TID and SEE tests are presented shortly, as well as the mixed-field tests at the CHARM facility.  
Figure 1: PoL functional block diagram.  
The next chapters of this paper shall present aggregated, cross-sectional analysis of test results from the seven test campaigns summarized in Table 2, with a special focus on the opportunities and limitations of the system-level testing.
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
\begin{tabular}{l l} \(x_{i}\) & Input vector \\ \(R^{m}\) & Input space \\ \(y_{i}\) & Output vector \\ \(R^{n}\) & Output space \\ \(n\) & Number of training samples \\ \(\Phi\) & Kernel space mapping function \\ \(H\) & Feature space \\ \(w\) & Weight vector in space \(H\) \\ \(b\) & Offset parameter \\ \(C\) & Tunable regularization parameter \\ \(e_{k}\) & Error variables \\ \(a_{i}\) & Lagrange multiplier \\ \(S(p)\) & The \(p\) th element in S \\ \(S(p^{-})\) & Column vector of \(S\) minus the \(p\) th element \\ \(A^{-1}(p,p)\) & Element in row \(p\) and column \(p\) of \(A^{-1}\) \\ \(A^{-1}(p^{-},p)\) & Column vector of the column \(p\) of \(A^{-1}\) minus the \(p\) th element \\ \(\sigma^{2}\) & Kernel function parameter, labeled _sig2_ \\ \(K(x_{k},x_{1})\) & Dot product kernel function \\ \(K^{\prime}(p,p^{-})\) & Row vector of the row \(p\) of \(K^{\prime}\) minus the \(p\) th element \\ \(t\_step\) & Homotopy parameter step size \\ \(C\_step\) & Regularized parameter step size \\ _sig2\_step_ & Kernel function parameter step size \\ \(f\) & Mapping of input space to output space \\ \(C^{\prime}\) & Tunable regularization parameter of homotopy continuation method \\ \(sig2^{t}\) & Kernel function parameter of homotopy continuation method \\ \(X_{n}\) & Normalized predicted or monitored value \\ \(X_{r}\) & Predicted or monitored values before the normalization \\ \(X_{\text{max}}\) & Maximum predicted or monitored value before the normalization \\ \(X_{\text{min}}\) & Minimum predicted or monitored value before the normalization \\ \(L\) & Thickness of soft soil \\ \(e\) & Void ratio \\ \(n\) & Poisson's ratio \\ \(C_{v}\) & Consolidation coefficient \\ \(w_{n}\) & Natural moisture content \\ \(w_{L}\) & Liquid limit \\ \(k\) & Permeability coefficient \\ \(OCR\) & Over-consolidation ratio \\ \(C_{c}\) & Compression index \\ \(C_{r}\) & Rebound index \\ \(E\) & Young's modulus of elasticity \\ \(q_{u}\) & Unconfined compressive strength \\ \(h\) & Groundwater level \\ \(C_{st}\) & Cumulative settlement time \\ \(C_{sa}\) & Cumulative settlement amount \\ \(A_{v}\) & Actual value of cumulative settlement amount \\ \(P_{v}\) & Predicted value of cumulative settlement amount \\ \(e_{a}\) & average error \\ \(\varepsilon\) & Error \\ \(N_{pp}\) & Normalized predicted value \\ \(N_{qp}\) & Normalized actual value \\ \end{tabular}
## Materials and Methods
### Soil Gas Flux and Environmental Measurements  
Soil chamber measurements of CO\({}_{2}\) and N\({}_{2}\)O fluxes were made at the EC Tower Site. Measurements were made using six soil chambers placed temporarily on fixed frames, inserted 5 cm into the soil. Data were taken from April 2013 to Aug 2014 at intervals of approximately 2 weeks, except in December and January when measurements were made monthly. Prior to this, fluxes were measured monthly from Sept 2007 to Aug 2012 using four chambers at the same site [50]. CO\({}_{2}\) and N\({}_{2}\)O fluxes were measured with a non-steady state, non-flow-through chamber method, and analysed as described by [PERSON] et al. [51]. The opaque PVC chambers (40 \(\times\) 40 \(\times\) 25 cm) were closed, and three replicated air samples were taken from the chamber headspace at 0, 20, 40 and 60 min for subsequent analysis by gas chromatography (GC). The rate of concentration change after chamber closure was calculated using linear regression to determine the flux. Fluxes for all gases were rejected for individual samples in which the rate of increase in CO\({}_{2}\) concentration was judged to be anomalous (resulting in \(R^{2}\) \(<\) 0.8), as this was judged to indicate gas leakage in the chamber headspace.  
During flux measurements, soil temperature was measured with a Hanna model Checktemp 1 probe (Hanna Instruments, Bedfordshire, UK) at 0.5, 10 and 15 cm depths around all four sides of each chamber and averaged for each depth. Volumetric soil moisture was measured at a depth of 6 cm around each chamber, as above, using a Theta probe ML3 attached to a Delta-T Devices Ltd. (Cambridge, UK) HH2 moisture meter with default mineral soil settings. Replicated soil samples were collected from 0-10 cm depth near the 6 chamber frames and aggregated; 5 sub-samples were analysed for soil water content by weight to produce a daily average. The soil moisture calculated by weight was converted to % by volume using the soil bulk density at the appropriate depth. In addition, wet and dry bulb air temperature (model DTS-5, ELE International, Loveland, CO, USA) and soil temperature at 10 cm depth (2K Thermistor, Delta-T Devices) were recorded at 10 s intervals and averaged half-hourly using data loggers (DT 500, DataTaker, Thermo Fisher Scientific, Rowville, VIC, Australia).  
For comparison with measured fluxes, monthly simulated fluxes were calculated by summing the simulated daily values within each calendar month.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
###### Abstract  
The usage of imbalanced databases is a recurrent problem in real-world data such as medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class imbalance problems, the classifiers are commonly biased by the class with more objects (majority class) and ignore the class with fewer objects (minority class). There are different ways to solve the class imbalance problem, and there has been a trend towards the usage of patterns and fuzzy approaches due to the favorable results. In this paper, we provide an in-depth review of popular methods for imbalanced databases related to patterns and fuzzy approaches. The reviewed papers include classifiers, data preprocessing, and evaluation metrics. We identify different application domains and describe how the methods are used. Finally, we suggest further research directions according to the analysis of the reviewed papers and the trend of the state of the art.  
contrast patterns; data mining; fuzzy set; imbalanced databases +
Footnote †: journal: Medical  
This problem can be solved with fuzzy patterns that have the property of flexibility, and the results are expressed in a language closer to that used by an expert [28, 29].  
To the best of our knowledge, there is no review of the most recent papers for the class imbalance problem addressed by fuzzy and pattern-based approaches. Hence, in this paper, we provide a systematic review of the state of the art for imbalanced databases that include fuzzy and pattern-based approaches, including theoretical and practical approaches. For the theory, we present common approaches to deal with imbalanced data; these approaches will include data mining, preprocessing, evaluation metrics, and classifiers. For the applications, we gather the most common real-world scenarios where imbalanced data is a significant concern. We cover different research areas so that the reader can have a better understanding of the problem.  
The rest of this paper is structured as follows: Section 2 presents the general background. Section 3 describes the research methodology and presents different approaches to deal with class imbalance problems with patterns and fuzzy approaches. Section 4 presents different research areas where class imbalance problems are presented in real-world data. Section 5 presents a taxonomy to group the different approaches. Section 6 explains our thoughts of the current state of the art and presents future directions. Section 7 presents the conclusions of this paper.
# Effects of Temperature on Growth and Isoprene Metabolism Pathway in _Eucomnia ulmoides_ [PERSON]
## 2 Materials and Methods
### Methods
#### 2.2.5 Total RNA Extraction, cDNA Library Construction, and Illumina Sequencing  
The leaves from various plants of _E. ulmoides_, specifically the second true leaf from top to bottom, which were subjected to a 40-day temperature treatment, were promptly frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C for future use. To extract total RNA from _E. ulmoides_, the Trizol method was employed. The purity of the RNA was assessed using the NanoDrop method, calculating the OD260/OD280 ratio, and its quality was verified using 1% agarose gel electrophoresis. Subsequently, Oligo (dT) beads were utilized to isolate the mRNA, followed by fragmentation of the mRNA into short fragments using an interrupting reagent. These short fragments were employed as templates for the synthesis of the first cDNA strand via first-strand synthesis. Following this, the Second Strand Synthesis Mix was utilized to synthesize the second strand. The resulting product underwent end leveling and had \"A\" added to it. RNA-Seq Adapter was connected to the product after this process. After ligating the RNA-Seq Adapter, the ligated product was purified, followed by PCR amplification. The target product of 300-600 bp was purified and recovered using magnetic beads. Finally, the cDNA library was sequenced on the Illumina NovaSeq 6000 platform once the library construction was deemed qualified.
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 7 Conclusions  
The PPF-OM scheme proposed in this paper both ensured the transmission security and eliminated the PDL. For eavesdroppers, the related parameters used to recover the signals were difficult to decipher, and thus, the BER performance was degraded. In addition, the errors in these parameters led to self-interference in [PERSON]'s received signals. Therefore, the signal-to-noise ratio decreased, and the average secrecy rate became a positive value. In this manner, the transmission security was enhanced. The PPF-OM scheme is a signal processing technology that can adapt to most current communication systems. In the next step, we further consider simplifying signal processing on the basis of guaranteeing performance.  
Methodology, Z.P. and C.Y.; Formal analysis, Z.L. ([PERSON]); Investigation, Z.L. ([PERSON]), Z.P., C.Y., Z.L. ([PERSON]) and H.C.; Resources, Z.P.; Writing--original draft, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Laboratory Basic Research Program (DXZT-JC-ZZ-2020-003) and the National Natural Science Foundation of China (Grant 62005320, 61975044).  
The authors declare no conflict of interest.
## 3 Discussion
### Effects of Temperature on Metering Error  
Since the designed lifespan of SEMs is more than 10 years, the effects of performance degradation in 6 months on the measurement error could be neglected. Therefore, the changes of the measurement error of SEMs could be considered to be due to the changes in temperature. The measurement error at temperatures of \(-\)20, \(-\)15(5), 25, 28, 32 \({}^{\circ}\)C and under current \(I_{\text{b}}\) from 1 January 2019 to 30 June 2019 were selected and are presented in Figure 9.  
Figure 7: Metering error of SEMs under conditions of (**a–c**) for current: \(I_{\text{max}}\), \(I_{b}\), and 0.5\(I_{b}\), respectively.  
Figure 8: Metering error under various power coefficients, (**a**,**d**), (**b**,**e**), (**c**,**f**) for current \(I_{b}\), \(0.5~{}I_{b}\) and \(0.1~{}I_{b}\).  
Figure 9: Measurement error under various levels of power coefficient, (**a**,**b**) for mean values and standard deviation, respectively.  
As a nonlinear trend was exhibited between metering error and temperature, a quadratic polynomial was employed to fit the changing trends:  
\[\gamma_{\Omega}(T)=p_{\Omega,\psi,2}\ T^{2}+p_{\Omega,\psi,1}\ T+p_{\Omega,\psi,0}. \tag{3}\]  
where \(\gamma_{\Omega}(T)\) is the function of the mean value and the deviation function changing with temperature \(T\); \(p_{\Omega,\psi,2}\), \(p_{\Omega,\psi,1}\), \(p_{\Omega,\psi,0}\) are parameters, which can be computed with the help of Matlab and are listed in Table 3. The fitting curves are also presented in Figure 9.  
As shown in Figure 9a, the mean value of the measurement error was inversely proportional to the temperature. For example, the mean value declined from about 0.14% at \(-15\ ^{\circ}\)C to around 0.02% at 32 \({}^{\circ}\)C when the current was \(I_{\text{b}}\) and the power coefficient was 1.0. The results were consistent with the existing research. We also paid attention to the evolution of metering error under the other power coefficients. When the power coefficient was 0.8C, the mean value reduced from around 0.12% at \(-15\ ^{\circ}\)C to 0.03% at 15 \({}^{\circ}\)C, and then to \(-0.02\%\) at 32 \({}^{\circ}\)C. As for power coefficient 0.5L, the mean value fluctuated between \(-0.05\%\) and 0.1%. However, the standard deviation exhibited a declining trend in [\(-15\ ^{\circ}\)C, 15 \({}^{\circ}\)C], whereas it presented a rising trend in [\(15\ ^{\circ}\)C, \(32\ ^{\circ}\)C] as shown in Figure 9b. For example, \(3\sigma\) decreased from about 0.40% at \(-15\ ^{\circ}\)C to around 0.1% at 15 \({}^{\circ}\)C and then increased to about 0.15% at 32 \({}^{\circ}\)C. The quadratic polynomial presented a good fitting result, which could be further employed to model the effects of both current and temperature.