esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 5 Conclusions  
In this work, we introduce a novel DBSNet, a dual-branch self-boosting network designed for precise, robust, and flexible image denoising without reliance on annotated images. Through meticulous analysis, we unveil a symbiotic relationship between content prediction (nonresidual mapping) and noise prediction (residual mapping) training strategies. To effectively harness the predictive power of both tasks, we propose a dual-branch network that concurrently trains on these two objectives and seamlessly integrates them within a unified architecture. To enhance network stability and denoising performance, we introduce a pioneering feedback mechanism. By incorporating this mechanism, we can introduce additional loss terms to refine the network's ability to predict both content and noise accurately. Extensive experiments validate the efficacy of our proposed dual-branch approach, which continually reinforces each branch during unsupervised training on unlabeled images. These findings demonstrate that DBSNet possesses the flexibility of unsupervised denoising networks and the efficacy of supervised denoising models. Our approach offers a solution or method to effectively tackle challenges in real-world image denoising, including limited training data, complex noise modeling, and the need for robust generalization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DnCNN** & **CBDNet** & **Restormer** & **FEUNet** & **AP-BSN** & **MM-BSN** & **N2N** & **Ours** \\ \hline Time & 0.014 & 0.092 & 1.684 & 0.032 & 5.024 & 0.224 & 12.107 & 0.236 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Computation time (ms) of various competing methods on Set12 dataset images.  
Figure 5: Visual denoising results of various methods on staircase image.  
**Author Contributions:** [PERSON] and [PERSON] contributed to the conception of the study, [PERSON] wrote the main manuscript text, [PERSON] and [PERSON] contributed significantly to the analysis and manuscript preparation, and [PERSON] conducted the experiments. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of China, grant number 62162043.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors would like to extend their gratitude to the authors who generously provided the source code for their contribution to this research. Their code has been instrumental in enhancing the quality and reproducibility of our work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 6 Conclusions  
In this paper, we propose a residual image guided cGAN process for single image dehazing which does not rely on estimations of transmission maps or atmospheric light. We regard the problem of image dehazing as a problem of image generation, and directly use convolutional neural network to learn mapping between hazy and clear images. We use the pix2 pix network architecture as the infrastructure and decompose the high-frequency components in the residual image of the hazy image through the decomposition module, then combine the results with the output of the encoder to generate a clear image using the decoder. Experimental results show that our method performs well for both synthetic and real-world datasets. However, only natural images are tested; in the future, we will consider improving the method in this article for satellite images.  
Conceptualization, Q.B.; methodology, Q.B., K.M. and J.L.; formal analysis, H.F.; investigation, J.L. and K.M.; resources, J.F.; writing--original draft preparation, Q.B., K.M. and J.L.; writing--review and editing, Q.B., K.M., J.L., H.F. and J.F.; supervision, Q.B. and J.F.; project administration, H.F. and J.F.; All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by Shaanxi International Science and Technology Cooperation and Exchange Program of China (2017 KW-010), Scientific Research Project of Shaanxi Education Department of China (15 JK1689). The authors declare no conflict of interest.  
Figure 8: The results of our method on a hazy night image and on a particularly hazy image.
###### Abstract
Therefore, it is important to consider not only the behavior of individual structures and the morphology of inclusions but also their matrix, which is the steel microstructure [34, 35, 36, 37, 38]. There are many papers presenting the results of fatigue strength tests of hard materials (mainly bearing steels) and those produced on a laboratory scale [25, 39, 40, 41, 42, 43, 44]. It is much more difficult to find test results made on steel with high plasticity, which also justifies taking up the topic of this paper. Steel fatigue tests are costly and time-consuming. For economic and time reasons, the industry conducts simulations more and more often to quickly estimate material properties, including fatigue strength [23, 45, 46]. There are known coefficients that allow for the estimation of fatigue strength as a function of static strength for specific conditions, such as Equation (1) [18, 47]:  
\[z_{g}=c\cdot R_{m}, \tag{1}\]  
where:  
\(z_{g}\)--fatigue strength, MPa;  
\(c\)--coefficients of the equation;  
\(R_{m}\)--tensile strength, MPa;  
Therefore, Equation (2) can be written as:  
\[c=z_{g}\cdot R_{m}{}^{-1}, \tag{2}\]  
The industry strives to determine as many material properties as possible through non-destructive testing. Equation (3) was developed to estimate the tensile strength based on the results of non-destructive tests. The \(c_{1}\) coefficient was introduced to compare the tensile strength to hardness:  
\[R_{m}=c_{1}\cdot HV, \tag{3}\]  
where:  
\(c_{1}\)--coefficients of the equation;  
\(HV\)--Vickers hardness, MPa;  
Then:  
\[c=z_{g}\cdot(c_{1}\cdot HV)^{-1}, \tag{4}\]  
Expressing the coefficients \(c\) and \(c_{1}\) with the coefficient \(k\), we obtained Equation (5):  
\[k=z_{g}\cdot HV^{-1}, \tag{5}\]  
Many papers describe the effect of non-metallic inclusions on the fatigue strength of steel [11, 12, 13, 14, 48, 15, 16, 17, 18, 19, 20, 49, 30]. Unfortunately, the analysis of the impact of pollutants on the \(k\) coefficient determined by Equation (5) is not a popular area of study. However, it is a very useful method because it allows you to quickly estimate the fatigue strength, and the method is completely non-destructive. The main purpose of this study was to determine the fatigue strength coefficients of various microstructures as a function of the relative contents of non-metallic inclusions and to determine the influence of the relative amounts of impurities on the fatigue strength coefficients of carbon steels with different tempering temperatures.
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 3 Results
### Using the Filtered SCADA Signals to Estimate Power Curves: Training Data  
For each WT, we estimate the wind-power curve using the ANN architecture described in (3) by using 3/4 of the total available data for the training by selecting the filtered wind speeds (wnac_avg_WSpd1) as the predictor and the filtered active power (wgdc_avg_TriGri_PwrAt) as the target. For all WTs, we have used the same ANN size consisting of \(N=10\) hidden nodes (neurons). Figure 12 shows all the estimated curves of each WT together with the power curve calculated from the data provided by the manufacturer in black indicating, in small squares, the specific points provided.  
At this point, we notice how the estimated power-wind curves obtained from SCADA data differ from the one provided by the manufacturer, especially in the rang that goes from 8 to 12 m/s.  
Comparing the estimated curves of each WT, we also notice that they are slightly different.  
Figure 11: On the left, in black, the anomaly points of WT84 and the power curve provided by the manufacturer. On the right, the anomalies of WT84 are isolated.  
Figure 10: On the left, in black, the anomaly points of WT83 and the power curve provided by the manufacturer. On the right, the anomalies of WT83 are isolated.
## 4 Effect of the Injection Rate Shape
The prediction results for the spray shape are compatible with the photos from the experimental data; this shape reflects the accuracy of the distribution angle, penetration, and evaporation of the spray. The injection rate and injection pressure have a great influence on these physical characteristics. Therefore, the model created can predict the spray behavior very well, especially under high rail pressure conditions. Based on the study of the effects of different rail pressures, the spray shape data from the experiment demonstrate that the size of the spray cone angle is different under the same ambient conditions, as shown in Figure 13. Figure 13 shows the spray cone angle size obtained from the spray picture of the experiment data. The result indicates that the spray cone angle used as the input data in this study depends on the size of the maximum rail pressure. The model calibration demonstrated that using the spray cone angles obtained from the experimental spray images as inputs can provide good predictive results (consistent with the experimental data). Therefore, this study used the prediction equation (Equation (8)) to predict the size of the spray cone angle at different rail pressures:  
\[\textit{Sray cone angle}=0.05P_{\textit{rail}}+13.333. \tag{8}\]  
Figure 12: The varying shape of the fuel injection rate with a constant injection duration and fuel quantity. The rectangular injection rate (RECT), the Quick Increase Gradual Decrease injection rate (QIGD), the Gradual Increase Gradual Decrease injection rate (GIGD), and the Gradual Increase Quick Decrease injection rate (GIQD).
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Intelligent Planning
#### 5.1.1 Graph Planning  
Graph planning [55] is an efficient automatic planning technique in intelligent planning. It uses the structure of a planning graph to represent and solve planning problems. A planning graph consists of alternating layers of propositions and actions, with each layer representing a time step. The proposition layer lists the propositions that are true at the current time step, while the action layer includes the available actions that can be taken, including no operation actions that maintain the current state of the propositions.  
The graph planning solution process comprises two principal stages: the extension of the planning graph and the extraction of the solution. Firstly, assuming that the solution time step is limited to K, graph planning is performed in a stage of forward expansion of the planning graph to obtain all possible actions and propositions that can occur during the K time steps. Subsequently, the backtracking method is employed to calculate the planning solution in reverse from the final time step. If no planning solution is obtained, the solution time step is increased to K + 1, the planning graph is extended to time step K + 1, and the planning solution is searched for again in reverse. This process is repeated until a valid planning solution is successfully found.  
[PERSON] [22] is one of the earliest planners to implement graph-based planning methods. It constructs a planning graph to represent the problem and then uses this graph to perform a backward search to find a solution. [PERSON] [56] combines the planning graph with SAT problem-solving techniques. [PERSON] et al. [57] proposed an algorithm for compact graph planning that effectively reduces the size of the search space by constructing a compact planning graph and pruning irrelevant states, thus improving the solution  
Figure 4: Development timeline of penetration path planning.  
efficiency. The method was subsequently applied to penetration path planning, resulting in favorable outcomes.
## 4 Conclusions  
### Concluding Remarks  
The analysis relies on questionnaire-based survey data to identify the main maintenance factors affecting the performance of industrial assets, which rely on model-agnostic interpretation. Given that three target (dependent) variables are collected via survey, we used PCA (Principal Component Analysis) to reduce the dimensionality of response variables. The first component is used for ML regression. After obtaining features by the mean dropout loss, the same is used for hypothesis testing to estimate individual target variables' actual contribution.  
The findings suggest that outsourced failure analysis offers the highest impact on improving availability, specifically increasing Mean Time Between Failures (MTBF). Next, the data analysis tool utilised alongside advanced condition monitoring sensors suggests a positive impact on improving the MTBF of fluid power systems. The analysis of maintenance analysis program predictors also supports previous claims, suggesting that using advanced maintenance analysis programs, such as contamination control and prognostics and health management, improves operational effectiveness expressed via MTBF. Finally, the fluid power system's age (i.e., HMA) significantly impacts the negative correlation effect on MTBF, which is commonly reported.  
Considering MTTR performance, the HMA estimate positively correlates with MTTR, suggesting that age significantly impacts the time needed to return the system to its operational state. This has been supported by the capacity of fluid power systems expressed in [kW] unit size, suggesting that larger machines are associated with longer repair times--MTTR. Using lubricant condition monitoring instruments reports a potential impact on MTTR; however, the results may be uncertain due to the limited size and violation of the test's assumption.  
Lastly, the impact on sustainability performance measured by the wasted hydraulic oil per month suggests that outsourced failure analysis improves sustainability performance expressed in monthly waste oil. The findings also highlight the cases where maintenance departments consisting of managers and hydraulic specialists will positively impact sustainability, i.e., reducing monthly oil waste. However, it should be noted that with the increase in company size, maintenance personnel and machine size (expressed in kW unit power), there is a higher risk of increasing sustainability impact in terms of waste oils. Although using lubricant condition monitoring instruments and performing oil laboratory analysis suggests the existence of a statistically significant impact, the impact of these variables remains inconclusive due to the violation of several assumptions and limited sample size.
## 3 Results
### Clinical Characteristics of RRIs among the Consenting Race Entrants in the Past 12 Months
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tissue Type**} & \multirow{2}{*}{**Pathology Type**} & \multicolumn{2}{c}{**Injuries Reported in the 12**} & \multicolumn{2}{c}{**Injuries Reported in the Two Weeks**} \\ \cline{3-6}  & & **Months Leading up to the Race** & \multicolumn{2}{c}{**before the Race**} \\ \cline{3-6}  & & **Number of RRIs** & **\% of All** & **Number of RRIs** & **\% of All RRIs** \\ \cline{3-6}  & & **(n = 55)** & **RRIs** & **(n = 12)** & **\% of All RRIs** \\ \hline \multirow{3}{*}{Muscle/Tendon} & All & 33 & 60 & 7 & 58 \\ \cline{2-6}  & Muscle injury & 20 & 36 & 3 & 25 \\ \cline{2-6}  & Tendinopathy & 13 & 24 & 4 & 33 \\ \hline \multirow{3}{*}{Nervous} & All & 1 & 2 & 1 & 8 \\ \cline{2-6}  & Peripheral nerve injury & 1 & 2 & 1 & 8 \\ \hline \multirow{3}{*}{Bone} & All & 3 & 5 & 1 & 8 \\ \cline{2-6}  & Fracture & 2 & 4 & - & - \\ \cline{2-6}  & Bone confusion & 1 & 2 & - & - \\ \hline \multirow{3}{*}{Cartilage/Synovium/Bursa} & All & 2 & 4 & 1 & 8 \\ \cline{2-6}  & Cartilage injury & 1 & 2 & - & - \\ \cline{2-6}  & Bursitis & 1 & 2 & 1 & 8 \\ \hline \multirow{3}{*}{Ligament/Joint capsule} & All & 5 & 9 & 2 & 17 \\ \cline{2-6}  & Joint sprain & 5 & 9 & 2 & 17 \\ \hline Nonspecific & All & 11 & 20 & 1 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RRIs among consenting race entrants by tissue and pathology type (% RRIs) (n = 55). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **OR (95\% CI)** & **\(p\)-Value** \\ \hline Age & 1.01 (0.97–1.05) & 0.709 \\ \hline Sex & 0.59 (0.26–1.21) & 0.168 \\ \hline BMI & 0.96 (0.85–1.07) & 0.433 \\ \hline
46 km & 1.94 (0.81–4.43) & 0.123 \\ \hline \multirow{3}{*}{Average weekly vertical gain} & \multirow{3}{*}{50–1000 m} & 1.06 (0.41–2.68) & 0.900 \\ \cline{3-3}  & & 100–1500 m & 0.93 (0.37–2.36) & 0.880 \\ \cline{2-3}  & & \textgreater{}1501 m & 1.08 (0.42–2.91) & 0.871 \\ \hline \multirow{3}{*}{Total running experience \textgreater{} 5 years} & Running & 1.39 (0.71–2.64) & 0.326 \\ \cline{2-3}  & Trail running & 1.62 (0.85–3.19) & 0.149 \\ \hline \multirow{3}{*}{Average number of running sessions per week \textgreater{} 3 sessions} & Running sessions & 1.93 (0.90–3.98) & 0.082 \\ \cline{2-3}  & Running sessions on trail & 1.16 (0.50–3.03) & 0.738 \\ \hline \end{tabular}
\end{table} 
Table 5: Univariate analysis (Odds ratio, 95% CI, \(p\)-value) of variables in relation to RRIs sustained in 12 months preceding the race. None of the variables listed in Table 5 showed a statistically significant connotation with sustaining RRIs in the 12 months before the race.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 4 Verification and Utilization of the Proposed Tools
### Local Problems Solutions Versus Global Solutions  
In our tests, we applied the results from the algorithms for the detection of the bending dominance. We calculated sums of elemental strain energy components present in the criterion (29). In the case of the plate problem, the shear and bending energy components were computed, while, in the shell examples, the sum of shear and membrane components and the bending component of the energy were determined. The assessed global plate problem was characterized with \(p=1\), while in the shell examples the assessed global problems were characterized with \(p=2\). Then, the energy components were calculated for the sequence of problems (30) characterized with \(P_{1}=p,p+1,\ldots,8\). The averaged values of the interelement stresses were used in the local problems definition. The transverse order of approximation corresponded to the second-order hierarchical shell model (\(Q=q=2\)) for all examples. The used mesh was characterized with \(H=h=l/2\).  
The above results from the sequence of the local problems were compared with the results from the global problems, where \(q=2\) and \(h=l/2\). In the case of the plate, the following longitudinal orders of approximation were applied \(p=1,2,\ldots,8\) in the global problems, while in the case of two shell examples, the values of \(p=2,3,\ldots,8\) were taken into account. The results of the comparisons are presented in Figures 3-8 for the plate and bending- and membrane-dominated shells, respectively. The figures present the ratios of the shear (the plate) or the sum of shear and membrane (the shells) energies to the entire strain energy and the ratios of the bending energy to the entire strain energy obtained from the local problems (top) and the global problems (bottom). Values of these ratios are presented versus the longitudinal order of approximation.  
Comparison of the top and bottom figures for the model problems leads to the conclusion that the detection tools based on solution of the element local problems give practically the same results as the solutions to the global problems, both qualitatively and quantitatively. In both bending-dominated problems, the change of the membrane dominance resulting from the locking phenomena to bending dominance due to removal of the locking is perfectly indicated. In addition, the membrane-dominated problem has been perfectly identified, as shown by the third couple of figures--the membrane energy component dominates over the bending one for all values of the longitudinal order of approximation.  
Figure 3: The sum of local energies—the bending-dominated plate.  
Figure 4: The global energy—the bending-dominated plate.  
Figure 5: The sum of local energies—the bending-dominated shell.  
Figure 6: The global energy—the bending-dominated shell.
## 4 Interference Monitoring
### Correlator Output  
When no interferences exist, the correlator output is basically constant, which is decided by pseudo-code autocorrelation, correlation length of the correlator and integral time. However, interferences can make the correlator output vary. Therefore, the correlator output can be also used as an interference monitoring indicator.
## 6 Implementation
### Composition and SPD Assessment  
Initially, we instantiate the system's components (\(n_{1}\), \(n_{2}\), \(n_{3}\), _router_, _net_, _over_) for moderate SPD with low power consumption. PBAC, PEP, and IPsec are set at the SPD's of \(PBAC_{Auth}\), \(PEPE_{Auth}\), and \(IPsec_{128}\), accordingly. The SPD of each distinct component is assessed by the CompoSecReasoner-no composition operations are performed at this time-point and no component includes sub-components.  
Node \(n_{3}\) (laptop) executes three operations with ULCL to encrypt the underlying data for PIP, PDP, and PAP, accordingly: \(Happens(PerformOp(n_{3},ULCL,Source_{i}),\)\(SourceSPD(Source_{i},\ S,P,D),t))\to HoldsAt(SourceSPD(Source_{i},\ S,P,D),t+1)\).  
The SPD value for each of these sources is the SPD level of \(ULCL\)-\((80,50,30)\). The changes trigger the SPD assessment for the node \(n_{3}\) (\((80,50,30)\)--Definition 7).  
In the same manner, the nodes \(n_{1}\) and \(n_{2}\) encrypt their local versions of PEP. The nodes' and sources' SPDs are calculated at \((80,50,30)\).  
Nodes have to execute the network and middleware attributes to enter the LAN. Operation \(op_{1}\) enforces the relevant composition requirements, designating the subsequent execution of WiFi, IPsec, and PEP. The type of such compositions is the MEAN-operation (as discussed in Section 3.3).  
Local networking is supported by the router. The LAN is a sub-component of the router. The composition requirements for this network are modelled by operation \(op_{2}\), denoting the subsequent performances WiFi and IPsec under an OR-operation composition.  
Apart from the internal networking, the router offers external connection with Internet, modelled under \(op_{3}\). An OR-operation defines the composition between the router and the Internet.  
Thereafter, \(n_{3}\) is integrated to \(net\) (\(op_{1}\)): \(Happens(Component(net,n_{3},op_{1}),ComponentSub(net),t))\to(HoldsAt( ComponentSub(net),n_{3},t+1)\ \land(EvaluateCompSPD(net),CompSD(net,S,P,D),t+2))\).  
The SPD for \(net\) is revisited due to this composition (\((70,50,70)\)--Definition 5), and then, the n\({}_{3}\)'s SPD of is also re-assessed (\((70,50,30)\)--Definition 7). Nodes \(n_{1}\) and \(n_{2}\) are also integrated to \(net\) (\(op_{1}\)). The SPD level reaches \((68,30,70)\) for \(net\).  
The network \(net\) is incorporated to the \(router\) (\(op_{2}\)). The \(router\)'s SPD restricts the SPDs of its sub-components (\(net\) and subsequently \(n_{1}\), \(n_{2}\), and \(n_{3}\)). Thereupon, \(router\) connects to Internet (\(op_{2}\)). After the system's composition, we can reason about the metrics that are eventually achieved. The total SPD of this system is estimated at \(\langle 70,50,30\rangle\) and is strongly influenced by \(n_{3}\)'s SPD.