esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### UV Camera  
UV cameras can be used to detect scratches with higher contrast than with normal RGB cameras. However, an additional UV light source is required. These light sources produce a very narrow beam of light and therefore create an uneven illumination in the image, as can be seen in Figure 8. This algorithm not only facilitates measurements with heterogeneous illumination sources, which is a common challenge with hyperspectral imaging (HSI) or UV cameras, but also provides a solution that is particularly useful in industrial settings. Achieving even illumination can be difficult due to safety or financial constraints in such environments. By applying the same algorithm to reconstruct an image at a specific spatial location, it ensures even illumination, thereby enhancing the detection of defects. Selecting a single line position can address this issue. However, since the starting position is initially unknown, imaging the entire sample becomes necessary. Our innovative algorithm plays a crucial role here, enabling us to seamlessly stitch together the images. This process allows us to reconstruct the image at a specific, targeted location, effectively overcoming the challenge of the unknown initial position. The sample studied, which was used for this measurement, is an epoxy-coated carbon steel sample. Several incisions of varying depth were made in this sample using a knife blade. An RGB image of this sample can be seen in Figure 9. From the RGB image, it is difficult to see all the different incisions. When looking at the UV sequence in Figure 8, the lowest cuts are very visible in the first image and second image (Figure 8a,b) but they become less visible in the last image (Figure 8c).  
When the spatial matrix is converted to a temporal matrix, the object under study is recreated. Three separate frames from the spatial matrix can be seen in Figure 10. Each frame in the spatial matrix represents a different physical location and, in this case, a different intensity of the light source used. Figure 10a shows that on the first image, the incisions are most obvious, but other artifacts enter the image due to the reflection of the UV light. Figure 10b is better suited to analyze this type of defect. In Figure 10c, we notice a significantly darker image. This darkness is attributed to the algorithm reconstructing the  
Figure 8: Three frames from the spatial matrix: (**a**) frame 90 (2.57 s); (**b**) frame 150 (4.29 s); (**c**) frame 230 (6.57 s).  
Figure 9: RGB image of the epoxy-coated steel sample with incisions. The incisions are marked with red squares. The sample has the following dimensions: 100 \(\times\) 50 \(\times\) 13 mm.  
image at a spatial position with less intense illumination, resulting in a darker appearance in the reconstructed output.
## 5 Blasting Vibration Reduction Effect Analysis Using Electronic Detonators
### Comparison of Vibration Response between Non-Electric and Electronic Detonators  
#### 5.2.1 Comparison of Vibration Response of the Dangerous Points on the Pipeline  
After replacing the detonation method of the blast holes with electronic detonators, the vibration response of the extracted danger points based on the calculated model is shown in Figure 13.  
After observing the calculation results of changing the detonation method, the following can be noticed:  
1. The vibration velocity in the vertical direction exceeds that in the horizontal direction. The PPV in the vertical direction and horizontal direction are 28.32 cm/s and 22.04 cm/s, respectively. After using the hole-by-hole detonation technology of electronic detonators, the maximum PPV is reduced from 79.12 cm/s to 28.32 cm/s. The vibration velocity is significantly reduced by 64.21%;
2. From the results of the integration PPV (Figure 13d), the integration PPV of Partitions I and III was significantly higher than those of Partitions II and IV after detonation. It is again proven that in the near range of blasting, the pipeline is significantly affected by the blast vibrations induced by the holes close to it. However, although the blast holes of Partitions III and IV were symmetrically distributed, the vibration velocity of Partition IV was lower than that of Partition III. The mainly because, after Partition III was blasted, more free surfaces were created for the blasting of Partition IV, which effectively reduced the vibration response;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Hole** & **Hole Number** & **Partition** & **Hole Quantity** & **Delay Time Range (ms)** \\ \hline Cut holes & 1–3 & I & 3 & 0–20 \\ Relief holes & 4–14 & I & 11 & 70–170 \\ Relief holes & 15–19 & I & 5 & 230–270 \\ Relief holes & 20–30 & II & 11 & 0–100 \\ Relief holes & 31–37 & II & 7 & 150–220 \\ Relief holes & 38–46 & III & 9 & 0–80 \\ Peripheral holes & 47–59 & III & 13 & 130–250 \\ Relief holes & 60–68 & IV & 9 & 0–80 \\ Peripheral holes & 69–80 & IV & 12 & 130–240 \\ \hline \hline \end{tabular}
\end{table}
Table 6: blasting construction parameters using electronic detonators.  
Figure 12: Layout of blast holes using electronic detonators.  
3. According to the safety standard [20], the maximum PPV under electronic detonator blasting is 28.32 cm/s, which exceeds the safe allowable vibration velocity. Further vibration reduction measures should be implemented to ensure that the PPV meets the safety requirements;
4. The vibration velocity waveform caused by the hole-by-hole blasting using electronic detonators fluctuates relatively gently. However, the vibration velocity produced by the simultaneous blasting of multiple holes using a non-electric detonator fluctuates in a pulse-type manner.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Genetic Approaches in Identification of mTOR Signaling Components
#### 4.2.5 The GTPase Rap1 Was Identified to Suppress mTOR1 Activation through a siRNA-Screen  
[PERSON] et al. revisited their previous siRNA-mediated screen for mTOR1 regulators using ICW (in-cell Western) [68]. Specifically, HeLa cells were transfected with siRNAs, fixed and stained with an anti-rpS6 phospho-S235/S236 antibody (to monitor mTOR1 activity) followed by incubation with an IRDye-800 CW-conjugated (infrared-excitable fluorophores) goat antirabbit secondary antibody. Resulting signals were quantitated by the LI-COR Aerius Infrared Scanner and normalized to cell number determined by staining cells with Alexa-680 succinimidyll ester. Using this technique, [PERSON] et al. found the GTPase Rap1A as a top hit as a negative mTOR1 regulator, which could localize to lysosome. Depletion of Rap1A in HEK293A cells by two independent siRNAs reduced mTOR1 activation [69]. More importantly, using confocal imaging, [PERSON] et al. observed that upon amino acid limitation, Rap1 suppressed lysosome abundance and its peripheral distribution. Thus, Rap1 suppresses mTOR1 activation under low amino acid conditions through limiting lysosome surface [69]. This provides an explanation for the low mTOR1 activity under amino acid deprivation conditions.
## 2 Materials and Methods
### Data Processing
#### 2.5.2 Data Fusion Based on Conditional Information Entropy Weight Allocation  
In the process of multi-sensor information fusion, different sensor data represent different attributes, and the importance of different attributes should be considered before fusion. To some extent, the weight of the data reflects the influence of attributes on the final prediction results; the greater the weight, the greater the influence. The target at the weight allocation of different sensor data and the change in information entropy, after taking data into account when making decisions, was analyzed according to the information entropy theory to realize an information-gain-based data weight allocation. In addition, the following formula is used to calculate information entropy:  
\[H=-\sum_{i=1}^{n}P_{i}\log_{2}P_{i} \tag{5}\]  
In this formula, \(P_{i}\) represents the corresponding decision probability of corresponding attribute data. In the process of weight allocation, the decision probability without considering attribute data is calculated first, and the original information entropy is obtained. Next was the information entropy after adding attribute data, namely conditional information entropy. According to the difference between original information entropy and conditional information entropy, the higher the information entropy, the greater the influence of attribute data on decision-making and the greater the fusion weight allocated.  
Let the optimized feature parameter obtained by mRMR be \(x_{1}\), \(x_{2}\), and \(\ldots\), \(x_{m}\), and let the decision variable be \(I\). Then, the process of feature weighting should be calculated as follows:  
From the above derivation, the decision function can be obtained as:  
\[f(x)=\text{sgn}[\sum_{i=1}^{n}\alpha_{i}y_{i}K(\mathbf{x}_{i},\mathbf{x}_{j})+b] \tag{14}\]  
where \(y_{j}\) is the corresponding expected output, \(\mathbf{x}_{j}\) is the input vector, \(n\) is the number of training samples, \(\varphi(\cdot)\) is the mapping function, and \(K(\mathbf{x}_{i},\mathbf{y}_{i})\) is the kernel function. Generally, the radial basis kernel function (RBF) is used, and \(\gamma\) is the kernel parameter:  
\[K(\mathbf{x}_{i},\mathbf{x}_{j})=\exp(-\gamma\big{|}\mathbf{x}_{i},\mathbf{x}_{j}\big{|}^{2}) \tag{15}\]  
According to Equations (10)-(15), the SVM's performance is mainly affected by the penalty factor \(C\) and RBF kernel parameter \(\gamma\) after selecting the appropriate kernel function. Therefore, if an efficient optimization algorithm is adopted to adjust the above parameters, the effectiveness of the SVM can be greatly improved.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 6 Strategies for Optimal and Scalable Code Selection
### Performance Analysis of Surface Codes with Code Distance  
We conducted an experiment to determine the amount of logical errors produced when certain parameters of a surface code are applied at a fixed physical noise level. We used a linear regression model to plot a line fit of code distance against the log of the logical error rate. By plotting the collected points alongside the line fit, we projected the distance required to achieve the desired logical error rate. This experiment was executed in two sets: one accounting for different errors, and the other considering various noise models. We maintained the physical noise at \(0.5\times 10^{-2}\), a value that exceeds the noise level of current state-of-the-art technology. For these experiments, we set \(rounds=distance\times 3\) and varied the distance from 3 to 9 to extrapolate the line fit till distance 30. The results of these experiments are exhibited in Figure 8a,b. In our subsequent experiment, as depicted in Figure 9, we also assessed the number of qubits required to attain a specified logical error rate for rotated surface codes. In this assessment, we extended our projections to consider systems utilizing up to 1000 qubits. This step was crucial as it allowed us to observe the required resource for the targeted logical error rate, which varies based on different error and noise models. The following conclusions can be drawn:  
Even with the most realistic circuit-level noise model, an elevated distance yields a notably low logical error rate at such high physical error levels. As exemplified in Figure 8a, if we are dealing with a quantum hardware system predominantly affected by depolarizing errors and we aim to achieve a logical error rate of \(10^{-10}\), a surface code of approximately distance 12 with 48 rounds \((12\times 3)\) will be necessary. Conversely, if the system is more heavily affected by gate errors, a surface code exceeding a distance of 30 (and 90 rounds) would be required to achieve the same logical error rate.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Error or Noise Model** & **Minimum Distance** & **\# Qubits** \\ \hline Depolarizing error & 7 & 118 \\ Gate error & 9 & 170 \\ Reset error & 5 & 64 \\ Readout error & 5 & 64 \\ \hline Code-capacity model & 7 & 118 \\ Phenomenological model & 5 & 64 \\ Circuit-level model & 9 & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum resource requirement for fault tolerance at physical noise \(=10^{-3}\) in surface codes.
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
## 3 Results and Discussion
### Purification of SiC
The elemental compositions for different points in Figure 12 are shown in Table 2. Before the carbon removal treatment, the amount of excess C in the synthesized SiC was high (point 1 and 2),  
Figure 11: Comparison of attenuated total reflectance (ATR) profiles for Si sludge and SiC powder products before and after the carbon removal treatment, (**a**) normal scale, (**b**) enlarged profile around the C\(\equiv\)C peaks in (**a**). whereas, after heat treatment, Si and C were found to be 69.05 and 30.95 in wt%, respectively. This is close to the theoretical value of pure SiC (Si:C = 70:30 in wt%).
## 3 Results and Discussion
### Effect of the Contact Time  
The contact time effect, which ranged from 30 to 180 min, on the Cu\({}^{2+}\) adsorption by the AgNPs was investigated. Figure 6c shows that the adsorption percentage of Cu\({}^{2+}\) increases for both the low and high concentrations (10 and 50 mg/L). The percentage of Cu\({}^{2+}\) adsorption increased rapidly during the initial stage (up to 60 min). This is because the active sites on the AgNPs' surface were available. Subsequently, the adsorption of Cu\({}^{2+}\) for both the concentrations decreased as the availability of the active sites for adsorption reduced over time [25].  
Figure 5: SEM images of AgNPs (0.2 g) before (**a**) and after Cu\({}^{2+}\) (10 mg/L) was adsorbed (**b**) for 3 h at 298 K.
# Safety Challenges in Battery Swapping Operations of Electric Underground Mining Trucks
## 3 Identification of Hazards and Risk Assessment During Battery Swapping
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \
\begin{tabular}{c} **Hazard** \\ **Number** \\ \end{tabular} & **Probability** & **Consequence** &
\begin{tabular}{c} **Risk** \\ **(Priority** \\ **Rating**)** \\ \end{tabular} & **Measure** \\ \hline
1b & 3 & 5 & Critical & Restrict access to the site. \\  & & & & Prevent unexpected \\
4 & 2 & 5 & Critical & movement of the \\  & & & & machine (parking brake, \\  & & & wheel blocks, etc.). \\  & & & & Prevent unexpected \\
12 & 3 & 5 & Critical & movement of the \\  & & & & machine (parking brake, \\  & & & & wheel blocks, etc.). \\  & & & & Proper training. \\
20 & 3 & 4 & Critical & Adhering procedures. \\  & & & & Regular inspection of \\
21 & 3 & 5 & Critical & installation and \\  & & & & insulation. Proper \\  & & & & training. \\
1a & 2 & 5 & High & in confined space. Clear \\  & & & & instructions. \\
9 & 3 & 3 & High & Rested workers, enough \\  & & & & breaks during shifts. \\
10 & 3 & 4 & High & Training for works on \\  & & & & heights. Use of personal \\
14 & 3 & 4 & High & protective equipment. \\  & & & & Restrict access to the site. \\  & & & & Including distress caller \\  & & & & and underground locator \\
15 & 2 & 5 & High & as mandatory personal \\  & & & & protective equipment. \\  & & & & Video monitoring site. \\  & & & & Sufficient ventilation of \\
16 & 4 & 2 & High & the site. Protective \\  & & & & equipment that reduces \\  & & & & heat. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Risk assessment and suggested measures for reducing the risk. A risk relevant to battery-powered machines is battery fires. This risk is not analysed in detail in this paper, since it requires research of a completely different approach. The risk of battery fires must be assessed and proper safety procedures must be developed with the introduction of battery-powered machines into underground mining.
## 4 Proposal for Data Transformation to Resolve Incompatibilities between Medical Formats
### Unit Discrepancy Identification and Automatic Conversion  
The most intuitive way to solve the discrepancy between different units in data would be to assign each possible unit certain limits and check at the beginning of each processing which one the first data rows fit most. However, that would be not the most reliable approach, since developers and medical professionals cannot always account for all the units that ever exist in the world and ensure their correct conversion. In addition, even then, the difference in some units may not always be clear to a machine.  
Figure 2: Transformation of text with patient notes to table with features represented as column (**left**) and application of NLP for analysis of table labels (**right**).  
Alternatively, semantic analysis could be used on the labels to extract the possible units that may have been described in the label itself. This is not a very reliable method either, since column labels may not always indicate any units, leaving the system with no hints. In addition, still, we might have to deal with unfamiliar units.  
For the biggest likelihood of catching such errors, multiple precautions, but better resolutions of this problem are still a point for future research.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 7 Conclusions  
In this study, we addressed the problem of recovering data from noise-affected WSN systems. In contrast with previous investigations that only focused on internal noise, our study incorporates a consideration of external influences, which enhances the feasibility of our research findings in real-world scenarios. The noise that impacts the WSN system is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Algorithm** & **Training** & **Testing** \\  & & **Error (\%)** & **Error (\%)** \\ \hline \multirow{2}{*}{Temperature} & CAE & 0.57 & 0.69 \\ \cline{2-4}  & **SCAE** & **0.36** & **0.44** \\ \hline \multirow{2}{*}{AQI} & CAE & 1.25 & 1.43 \\ \cline{2-4}  & **SCAE** & **0.89** & **1.03** \\ \hline \multirow{2}{*}{\(O_{3}\)} & CAE & 1.72 & 1.71 \\ \cline{2-4}  & **SCAE** & **1.23** & **1.241** \\ \hline \multirow{2}{*}{PM 10} & CAE & 3.12 & 3.34 \\ \cline{2-4}  & **SCAE** & **2.82** & **3.01** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between the SCAE and CAE with different datasets.  
categorized into two main sources: internal noise, such as thermal noise, and external noise, which encompasses adverse weather conditions. The use of data from multiple sensors concurrently is suggested in order to maintain the temporal characteristics of the data and the interdependencies among the sensors. Moreover, the SCAE model, as described, demonstrates efficiency in effectively extracting data characteristics while also addressing the limitations of the original CAE in terms of network structure and weight initialization. Thorough experiments were conducted using both WSN simulations and real-world sensor data. The experimental findings demonstrate that the SCAE has superior performance compared with existing models in noise-affected data reconstruction.  
In the future, we intend to expand the performance evaluation of the proposed model to WSN systems implementing additional protocols and real-world datasets. Additionally, we also desire to fine-tune the hyperparameters in order to enhance the performance of the model.  
Conceptualization, T.T.L.; Methodology, T.T.L. and T.P.T.; Formal analysis, T.T.L.; Writing--original draft, T.T.L.; Writing--review & editing, T.P.T., J.C. and M.Y.; Supervision, M.Y.; Funding acquisition, J.C. and M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Environmental Industry and Technology Institute (KEITI) through a grant funded by the Korean government (Ministry of Environment) (Project No. RE202101551, Development of IoT-Based Technology for Collecting and Managing Big Data on Environmental Hazards and Health Effects).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
###### Abstract
[15] investigated the mechanical behavior and failure pressure of a pipe with a single internal corrosion damage location, multiple interacting internal corrosion damage locations, and multilayered structural internal corrosion damage locations using nonlinear finite element analysis. [PERSON] et al. [16] investigated the effects of tension on collapse pressure by creating a finite element model of a pipeline with uniform symmetric axial corrosion. They conducted an extensive parametric investigation of the effects of loading path, initial damage length, yielding anisotropy and corrosion damage length on the collapse behavior under combined loads. [PERSON] et al. [17] developed a methodology to evaluate the annual failure probabilities of a pressurized pipeline with corrosion defects and determined the sensitivity of the calculated failure probabilities with respect to the time dependency of the internal pressure. [PERSON] et al. [18] investigated the time-dependent performance of pipelines subjected to external corrosion using reliability analysis and proposed a power-law function of time model to probabilistically predict the growth of corrosion maximum defect depth and defect length. [PERSON] et al. [19] calculated the relative contribution of the random variables and the sensitivity of the reliability index of the nonlinear corrosion model to conduct the probabilistic analysis of underground pipelines subject to combined stresses and corrosion. Although the above studies have done a lot of research on the mechanical properties of concrete pipes and the failure of corroded pipes, there has been limited research on the mechanics of the response of buried corroded concrete pipes under traffic loads. In this study, a three-dimensional finite element model and a full-scale experiment of buried corroded concrete pipes under traffic loads (the fatigue phenomena due to repeated load is neglected) were conducted to investigate the mechanical properties of buried corroded concrete drainage pipes. The effects of the depth of corrosion (corrosion depth) and the width of corrosion (corrosion width) in the pipe wall, traffic load, and cover depth, on the distribution of stress and strain in the pipe were analyzed. The results of this study provide a strong theoretical basis for evaluating the performance of buried corroded concrete drainage pipes.
## 3 Proposed Algorithm  
In this section, the concept of the standard PF criterion for the scheduling of packets is first described in brief. Next, the proposed efficient packet scheduling algorithm for which said PF criterion provides the basis is presented. Thereafter, a baseline scheduler design is introduced for the purposes of comparison. Moreover, we assume that on each RB the transmitted power is the same. It is further assumed that the users in the cell can periodically estimate and then send those estimates of the average signal-to-noise ratios for all the RBs on the \(c\) CCs back to the BS. Additionally, in order to obtain the best possible transmission data rate for each transmitted packet, the adaptive modulation and coding mechanism is further utilized in the physical layer [29]. The corresponding downlink data rates are performed with table lookup.