esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Results and Analysis of SSC Prediction Test
#### 3.2.1 Eliminating Outlier Samples  
In this test, the Mahalanobis distance method was used to eliminate outlier samples, and the Mahalanobis distance and critical threshold of each sample were calculated. Most of the samples were within the critical threshold, and samples exceeding the critical threshold were considered to be outlier samples. Through observation, we could determine that the samples with the serial numbers 54, 64, and 78 were obviously outlier samples; hence, they were eliminated (as shown in Figure 11).  
\begin{table}
\begin{tabular}{c c c c} \hline _TP_ & _FP_ & _FN_ & _F1/\%_ \\ \hline
348 & 5 & 29 & 95.34 \\ \hline \end{tabular}
\end{table}
Table 1: Shine-Muscat grape segmentation model evaluation.  
Figure 10: Reflectance spectra of modeling set.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings
### Peak Signal-to-Noise-Ratio (PSNR)  
PSNR aims to show the quality of the resultant image after the application of the technique [9]. A greater PSNR value tends toward a high quality of the reconstructed image. Initially, a mean square error (MSE) is calculated to get a PSNR value, as given in Equation (8):  
\[MSE\ =\ \frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\left[X(i,j)-Y(i,j)\right]^{2}}{M*N}. \tag{8}\]  
After \(MSE\) calculation, the root \(MSE\) is applied in order to get a \(PSNR\) as follows:  
\[PSNR=10\cdot log10(\left(\left.\mathit{Peakvalue}\right)^{2}\right|MSE\,). \tag{9}\]  
In Equations (8) and (9), \(X(i,j)\) represents an input image with an index \(i\) and \(j\), having \(M\) by \(N\) pixels, while \(Y(i,j)\) denotes the resultant enhanced image at location (\(i,j\)). _Peakvalue_ indicates the maximum difference between an original image value and \(MSE\). The achieved \(PSNR\) value by the proposed art using four tested samples, as shown in Table 3, was found to be more significant than histogram-based approaches. This indicates that the proposed art had a greater contrast ration in comparison to the other approaches. HE, AHE, and CLAHE were observed to be less effective in enhancing the contrast, while ESIHE produced better a PSNR than its other extensions. The PSNR analysis of the proposed art reveals that it could be a useful preprocessing solution for computational algorithms in real-time settings of DR.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 2 System Model and Problem Formulation
### Optimization Problem Formulation  
In order to balance the effectiveness and reliability of the system, we evaluate the performance of the system using effective throughput as a metric. In this paper, the effective throughput of the secondary user \(u_{i}\) is defined as  
\[\Phi_{i}=P(H_{0})\Big{(}1-P_{f}(N_{s})\Big{)}(1-\frac{N_{s}}{N})R_{i}(1-\bar{ \varepsilon}_{i}), \tag{10}\]  
where \(P(H_{0})\) is the prior probability that the state of the GBS is idle.  
In short-packet communication, we can use the bits-per-channel use (BPCU) instead of the bits per second as the transmission rate. Suppose that the UAV sends \(l\) bits of information to the secondary user \(u_{i}\) in each frame, then, the transmission rate can be redefined as \(R_{i}=\frac{l}{N-N_{s}}\)[35]. Therefore, the effective throughput of the secondary user \(u_{i}\) can be simplified as  
\[\Phi_{i}=P(H_{0})\Big{(}1-P_{f}(N_{s})\Big{)}(1-\bar{\varepsilon}_{i})\frac{l }{N}. \tag{11}\]  
In this paper, we aim to maximize the effective throughput of the far secondary users by optimizing the sensing time, power allocation, and information bits with constraints on the transmission power and effective decoding error probability. Thus, the problem we are interested in is formulated as \[\max_{\{N_{s},p_{1},p_{2},I\}} \Phi_{2} \tag{12a}\] \[s.t. p_{1}+p_{2}\leq P,\] (12b) \[0<p_{1}\leq p_{2},\] (12c) \[0<N_{s}\leq N,\] (12d) \[0<I\leq I_{\max},\] (12e) \[\varepsilon_{1}\leq\varepsilon_{\max}, \tag{12f}\]  
where \(P\) is the maximum transmit power of the UAV, \(I_{\max}\) is the maximum number of information bits transmitted by the UAV in each frame, \(\varepsilon_{\max}\) is the maximum decoding error probability at the secondary user \(u_{1}\), Constraint (12b) specifies the range of the UAV transmission power, Constraint (12c) guarantees that the UAV allocates more transmission power to the secondary user \(u_{2}\), Constraints (12d) and (12e) are the feasible regions of the sensing time and information bits, respectively, and Constraint (12f) is the acceptable range of the decoding error probability for the secondary user \(u_{1}\).
## Appendix A Mesh Type and Mesh Independency Study in the Numerical Models  
The unstructured mesh is used to discretize the computational domain, with a refinement near the borehole due to its geometry and the prescribed heat flux boundary condition. The mesh independency study for the 2-dimensional numerical model is also conducted. To ensure the appropriate number of mesh elements, five different mesh counts are selected from coarse to fine as listed in Table 11 and demonstrated in Figure 12.  
Figure 14 shows the temperature distribution over the \(x\)-coordinate (i.e., the line \(y=0\)) for single, double, and 3-by-3 boreholes in spring (\(t=3\) (mon)) with the five numbers of mesh elements. As can be seen, inconsistent results are found in the three coarser mesh counts (that is, extra coarse, coarse, and fine), particularly in the single borehole scenario. However, the temperature distribution of all the three borehole arrangements remains the same while discretizing the domain with the two finer meshes (that is, extra fine and extremely fine). As a consequence, the extra fine option is selected to be the optimal mesh count for this study.  
Figure A2: Unstructured mesh scenarios used: extra coarse, coarse, fine, extra fine, extremely fine (from up to bottom); single borehole, double boreholes, 3-by-3 boreholes (from left to right).
## 3 Advanced Techniques in Fault Injection and Error Rate Estimation  
This section aims to provide an understanding of the methodology and the developed tools used for fault injection and error rate estimation for soft errors, which should be considered to improve the reliability and robustness of avionics systems. To achieve this goal, we explain the fault injection method using SEM IP and the content related to statistical fault injection testing (Section 3.1). We introduce the developed Automatic Configuration Memory Fault Injection Tool, describe its architecture and functions in detail, and provide a comprehensive flowchart for the tool's operation (Section 3.2). The SER estimation method using the ACMFI tool is discussed, and major factors such as SEU and AVF for estimating the SER are covered (Section 3.3).
## 3 The Laboratory Setup  
The test bench used in this work (Figure 3) consisted of an induction motor with the following specifications: rated power of 1.1 kW; star connection; 400 V rated voltage; 1410 rpm rated speed, and 2.6 A rated current. The motor load was a Lucas Nulle powder magnetic brake, which also incorporates a torque and speed meter. Data was captured using the cDAQ-9174 by National Instruments with an NI-9215 module. Two Hall effect sensors by LEM were used as current (LA 25-NP) and voltage (LV 25-P) transducers. The sampling frequency used for voltage, stator current and speed acquisition was 50 kHz.  
The motor was tested under three different conditions: healthy, with a rotor bar broken at 70%, and with full broken rotor bar. Bar breakage was achieved by drilling a hole at the junction of a bar and the cage end ring. Fault detection also depends on the motor load, being more challenging at low loads. For this reason, the motor was tested also at two motor load levels: low load (35% of rated torque); high load (60% of rated torque).  
An ABB inverter, model ACS355, with open loop scalar control was used as motor supply. This inverter allows the user full control of the startup transient following a linear profile. The supply voltage amplitude and its frequency increase from zero to the final cruise values configured by the user, in this case to 50 Hz. In this work, the inverter was configured so the startup transient was 10 s long with a final frequency of 50 Hz. The voltage was captured to calculate the fundamental frequency and the synchronous speed over time, as explained in [16]. The speed was also measured for the calculation of the motor slip. This information permits the computation of the fundamental component and fault-related harmonics trajectories in the time-frequency plane, which are necessary to apply the Dragon Transform to the stator current and compute the spectrogram. Although the startup lasts 10 s, only the central six seconds are analyzed to avoidable edge effects at the beginning and end of the transient that affect the fault severity quantification. A total of 150 tests were carried out: 45 at healthy condition and low load; 45 at healthy condition and high load; 15 at medium fault condition and low load; 15 at medium fault condition and high load; 15 at full broken bar and low load; 15 at full broken bar and high load.
# Application of Direct Shear Test to Analysis of the Rate of Soil Improvement with Polyester Fibres
## 1 Introduction
The fibres had the same length (12 mm), but the sisal fibres had a larger diameter (140 \(\upmu\)m in comparison with 32 \(\upmu\)m of the polypropylene fibre), larger tension strength (558 MPa in comparison with 250 MPa of the polypropylene fibre), and higher surface texture roughness in comparison with the polypropylene fibres. The results of the unconfined compression strength tests carried out with and without the application of cyclic loading showed that the inclusion of fibres induced a decrease in the accumulated permanent axial strain, and this reduction was more significant for the use of sisal fibres due to the greater roughness and higher mechanical properties of this type of fibre. Analysing the obtained values of axial strain and unconfined compressive strength, it could be stated that, independent of the application of cyclic loading, reinforcement with sisal fibres was more effective than the use of polypropylene fibres. The greater roughness and better mechanical properties of sisal fibres promoted more effective anchorage and mobilisation of a higher tensile strength. Furthermore, the larger diameter of the sisal fibres provided a larger contact area between the fibres and surrounding material for the same strain level in comparison with the polypropylene fibres, so the anchorage was more effective and higher tensile strength was mobilised. From the above-mentioned literature review, it can be stated that there are many studies dealing with soil improvement using DST, but most of them applied only three specimens and the test result uncertainty was not provided, so improvement could not be confirmed. The application of the fibres TEXZEM PES 200 improves the soil shear strength parameters considerably. The surface condition of the fibres significantly affects the characteristics of the reinforced soil and it is useful to deal with this issue. In this study, DST has been carried out for three different soils improved by the mentioned fibres and four specimens have been tested in DST, applying four different normal stresses. The shear strength parameters were determined for all four specimens and various combinations of three specimens so that the influence of specimen number on test results could be analysed. The uncertainty of shear strength parameters was determined, also considering covariance between correlated input quantities (normal stresses and shear stresses) and a worst-case strategy. The obtained values of uncertainty enabled us to confirm the improvement rate and deepen the understanding of DST uncertainty. The applied SEM uncovered the possible reason for the significant improvement.
## 4 The Dynamic Effects
### Direct Dynamic Effect  
The variations of aerosol concentration result in differences in microphysical processes, leading to differences in latent heating and cooling. The vertical cross sections of zonal-mean latent heat and cross-front wind perturbation vectors \((v^{\prime},w)\) (here, \(v^{\prime}=v-V_{g}\)) are shown in Figure 11. At t = 21 h, the main heating region is located in the ascent region of frontal zones and the main cooling region is located in the descent region of frontal zones. Compared to C-case, P-case heating and cooling are both much greater. In P-case, heating reaches 18 K h\({}^{-1}\) and cooling reaches \(-6\) K h\({}^{-1}\), which is 2-3 times larger than C-case. Strong heating invigorates updrafts, while strong cooling invigorates downdrafts, causing both stronger updraft and downdraft in P-case than in C-case (Figure 11). At the same time, P-case convergence in the low-level frontal region and divergence at high altitudes are both greater than C-case (Figure 12).  
Based on the above analyses, under polluted conditions, a large number of small droplets are transported to high levels by frontal updraft, leading to the enhanced riming and WBF processes to produce more ice-phase droplets, which increases latent heat release. As a result, the extra heat reinforces the updrafts and thus enhances cold-cloud processes, leading to the formation of large amount of snow and graupel. On the other hand, with large amounts of snow and graupel falling and melting, rain water content increases below the frontal zone, leading to more evaporation. Stronger melting and evaporation cooling produce a stronger cold pool and strengthens downdrafts, resulting in stronger low-level convergence under the action of frontal wind shear.  
Here, the roles of latent heat (strengthens updrafts, downdrafts, and low-level convergence) under polluted conditions involves two mechanisms pointed out by [PERSON] et al. [78], which are the latent heat-dynamic effect and the cool pool effect. We suggest that the two direct effects driven by latent heat release may be collectively referred to as the first dynamic effect/direct dynamic effect (DDE).
## 2 Materials and Methods
### Data Analyses
#### 2.4.2 Audio Filtering and Processing  
All audio signal was processed with a passband filter (150 Hz and 10 KHz cutoff frequencies), normalized to \(-\)3 dB, and digitally limited to \(-\)1 dB. A noise gate was employed to minimize the unwanted sound artifacts.
# Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning
## 5 Conclusions  
The so-called 'vanishing gradient' is one of the typical problems of GAN. In this paper, we proposed a VLGAN that improved the learning stability, while it maintained the quality of the data that were generated using variational learning. In the proposed method, VAE was utilized to stably provide supplementary gradient to GAN. Therefore, even when the gradient of the generator vanished owing to the learning imbalance between the discriminator and the generator, learning was maintained in a stable form by receiving the gradient from the VAE decoder. The proposed method used the VAE encoder model as the secondary model, while it maintained the basic structure of the conventional GAN. Therefore, it can be easily applied to improve the learning stability of various types of GAN-based models that have already been developed. The experimental results of the MNIST and the CelebA datasets that were extensively used in prior research studies on computer vision technology with deep learning verified that the proposed method overcame the vanishing gradient problem and, at the same time, achieved excellent data generation performance of GAN. The proposed method is expected to help solve the mode collapse problem that often occurs in the learning process of GAN, given that the loss of VAE forces learning for all diverse situations. In the future, we will conduct experiments and additional research on the mode collapse problem.  
[PERSON] and [PERSON] participated in the study design, contributed to the formal analysis, data collection, validation, drafted the manuscript and edited the draft. All authors approved the final version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea Grant through the Korean Government (MSIT) under Grant 2018R1A2B6001400, and in part by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning from the Ministry of Trade, Industry and Energy, South Korea, under Grant 20174030201740.  
The authors declare no conflict of interest.  
Figure 7: MS-SSIM and training time per iteration for the CelebA dataset.
## 1 Introduction
Therefore, this paper focuses on studying the dynamic behavior of an SMC based on a washout filter to control the voltage when the load is formed by a combination of CIL and CPL. The voltage reference signal was changed to evaluate the response of the controller to different types of load. To perform the test, an electronic circuit was modeled and simulated that considers internal resistances, system discretization, quantization effects, and delay effects in order to study a more realistic circuit as in [18; 19]. Finally, a numerical bifurcation was performed by changing the parameters \(k\) with the aim of visualizing the nonlinear effects of the circuit and the load. All these tests were validated by measuring the voltage in the output bus (\(v_{c}\)). Contributions of this paper with respect to the literature are as follows: 1) The dynamic behavior of the SMC is evaluated with different types of load; 2) quantization, system discretization, parasitic resistors, and delay effects are modeled to obtain closer results to real circuits; 3) a buck converter with a quasi-sliding control technique is used as a CPL load, which converts it to a nonlinear CPL; 4)unstable zones and steady-state errors are obtained to evaluate the system stability when the control parameters are changed; 5) bifurcation diagrams for the output voltage and voltage error are used to evaluate the response of the controller in the steady-state operation under changes in the control parameter \(k\); and 6) comparisons among the proportional-integral-derivative (PID), conventional SMC, and SMC with a washout filter are performed with variations in CIL and CPL. Therefore, the materials and methods used in this research along with the mathematical fundamentals, simulations, and simulated electronic circuit are presented in Section 2. The results, analysis, and discussion are included in Section 3, and Section 4 presents the conclusions.
## 4 Evaluation
### Success Criteria SC-6: Analysis of Collective Change Impact Internal Functional Testing  
The internal functional test of the impact analysis method had three focal points: validation of the impact model, estimation of the consecutive change effects in the requirement network and estimation of the collective impact. The estimation of local effects was derived from the generic systems engineering processes, whose validity was taken as given. For this reason, no own validation of the processes was performed. The validation of the impact assessment (cf. Figure 4) was performed by two scientific publications with blind peer-review [55]. The evaluation of the consecutive change effects is based on an adapted PageRank algorithm, similar to the likelihood analysis. Here, the identical procedure was chosen to ensure the mathematical correctness of the adjustments: The results of the analysis of a test dataset were manually checked for plausibility. The final evaluation of the collective change effects is a combined representation of the local effects and consecutive effects, so that for the validation its correct functionality in the software prototype was ensured.  
Validation Workshop with Industry Users [W8 and 10]  
The validation with industry users took place in two workshops. In the first workshop [W8], the quality of the results was checked: sufficient accuracy and comprehensibility. Sufficient accuracy of the analysis was rated as fulfilled by the industry users. A ranking of requirements according to the expected impact in the requirement network seemed appropriate for selecting requirements for manual analysis. Analyzing traceability is hardly possible when using PageRank algorithm, due to the high computational effort required. Because of that, a path-specific analysis based on [PERSON]'s laws [68] was presented as an alternative. This offers better traceability for small sets of requirements, but due to the long propagation paths this decreases sharply from the 3 rd order of dependencies and for more than ten requirements. In addition, the computational effort increases exponentially with the number of requirements, resulting in long processing times. The industry users rated the advantage in traceability as insignificant compared to the longer processing time and the associated application effort. Instead, the deficit in comprehensibility was rated as tolerable for the selected solution based on PageRank, since a minimal application effort can be achieved. The availability of the required information was addressed in the second workshop [W10] and assessed as fulfilled. While the evaluation of the consecutive effects is fully automated, expert knowledge is required for the estimation of the effects. Due to the possibility to involve subject matter experts for the questionnaire as needed, this was rated as practical. In addition, the possibility to manually adjust the pre-calculated result (cf. Section 3.4) was evaluated as advantageous to ensure sufficient accuracy.  
Validation result: SC-6 is fulfilled.