esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of Operating Model to the Simulator  
### Establishment of TBM Operation Procedures  
Before applying the interaction system to the TBM simulator, TBM operation procedures were established first, as shown in Figure 4. The procedures comprise two main operations: excavation and segment ring installation. Once a section of excavation is complete, a segment ring is installed in an erector operation. However, this simulator did not consider erector operation.  
Excavation involves three parts: preparation, execution, and completion. Preparation starts with the power supply operation. Once the numbers of shield jacks and cutterhead motors are selected with regard to setting the target earth pressure, TBM operators sequentially initiate the belt conveyor system, then cutterhead rotation, articulation jack set, shield jack extension, screw gate open, and screw conveyor rotation. Once operators conduct the pre-operation check procedure, they are ready to start the water-cooling circuit systems and greasing systems.  
As the TBM starts to move along its planned route, operators monitor its mechanical responses in real time, as displayed on the control panel. When abnormal mechanical responses trigger a warning signal during excavation, operators can handle them in two ways depending on the likelihood of re-establishing control. If the abnormal mechanical response can be controlled, operators adjust the relevant operational actions (represented by shield jack and screw conveyor rotation speeds). For mechanical responses associated with temperature, the cutterhead operation is suspended, and the operation restarts once the temperatures reduce to the normal range.  
Figure 4: Architecture of excavation procedures applied in the TBM simulator.  
However, if it is impossible to control the abnormal responses, operators should press the emergency stop button following the operator manual and call technicians to solve the problem. Pressing the emergency stop button simultaneously suspends all operations, and the excavation is resumed from the preparation stage once the problems are resolved.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis
#### 5.1.3 Asset Inventory  
The asset inventory builds and manages a list of resources or assets to be considered at the risk valuation step. Due to the ability of the 5G mobile networks to automate the deployment of new services and network devices depending on their status, this component plays a critical role in the analysis of the gathered information. The new assets are detected at the monitoring layer and are reported by discovery events. When the existing events are updated, the monitor layer emits modification or removal events. For the proper functioning of the proposed architecture, it is very important to ensure coherence between the list of assets and the real network resources.
## 3 Mathematical Formulation of the Reactive Power Supply
### The Cost of Generating Reactive Power from the Line Charge  
The total cost of generating reactive power to the total capacitors of the line charge is equal to the total cost of each line capacitance, which is the product of reactive power generation and cost of each unit's reactive power generation, as given in Equations (4) and (5).  
The parameter C is equal to the average cost of generating each unit's reactive power of all grid resources. Figure 1 shows the \(\pi\)-model of the transmission lines with tap changer.  
\[Q_{charg}=\sum_{charg^{-1}}^{N_{charg}}Q_{chargh_{i-j}}=\sum_{charg-1}^{N_{charg }}\left(V_{i}^{2}\frac{y\zeta_{ij}}{2}+V_{j}^{2}\frac{y\zeta_{ij}}{2}\right) \tag{4}\]  
\[Cost_{Qcharge}=Q_{charg}*C_{ave} \tag{5}\]  
In Equation (4), \(Q_{charg}\) is the reactive power generated by line capacitor charge, \(V_{i}\) and \(V_{j}\) are the voltage amplitude of buses \(i\) and \(j\) and the line admittance. In general, if a transformer exists at the beginning of a bus, the line charge admittance is calculated as follows:  
\[I_{i}=V_{i}\left(y_{s}(t-1)t+0.5y_{c:ij}t^{2}\right)+y_{s}t\left(V_{i}-V_{j}\right) \tag{6}\]  
\[I_{j}=V_{j}\left(y_{s}(1-t)+0.5y_{c:ij}\right)+y_{s}t\left(V_{i}-V_{j}\right) \tag{7}\]  
where, \(V_{i}\) and \(V_{j}\) are \(i\)th and \(j\)th terminal voltage, \(y_{s}\) and \(y_{c:ij}\) are series admittance and susceptance, \(I_{i}\) and \(I_{j}\) are line current.
## 4 Experiments
### The CuBERT Transformer  
In our experiments, the model we chose to analyze was the CuBERT [1] transformer, which is derived from the popular BERT [9] transformer for NLP, designed to effectively deal with source code. Transformers are becoming increasingly popular and powerful, for more and more applications different from the original NLP tasks they have been designed for (e.g., image [28] and source code [8] processing), and when using such a kind of deep architecture, the benefit is that they can be trained once on large corpora of data associated to generic and self-supervised tasks, and later they can be fine-tuned on more specialized supervised tasks. This allows to use them with high flexibility. In this work, we appointed the cybersecurity domain and fine-tuning to be on the detection of software vulnerabilities by using a CuBERT model pre-trained on a Java corpus (Available: [[https://github.com/google-research/google-research/tree/master/cubert](https://github.com/google-research/google-research/tree/master/cubert)]([https://github.com/google-research/google-research/tree/master/cubert](https://github.com/google-research/google-research/tree/master/cubert)), version of 11 July 2021).  
To this end, we used the Juliet Test Suite v1.3 for Java [29; 30], a dataset developed by the NSA that contains a corpus of programs labeled according to the common weakness enumeration (CWE) ([[https://cwe.mitre.org/index.html](https://cwe.mitre.org/index.html)]([https://cwe.mitre.org/index.html](https://cwe.mitre.org/index.html)), accessed on 22 September 2022) list. For each CWE, the dataset consists of a set of Java files defining both flawed methods and, correspondingly, a number of non-flawed constructs. To build our training sets, we preprocessed the files labeled CWE-369 and CWE-789 representing the \"divide by zero\" and \"memory allocation with excessive size value\" vulnerabilities, respectively. The set of positive instances was populated with the methods that self-contain the flaw; in fact, we removed from the dataset all the methods that, to exhibit the vulnerability, need to  
Figure 3: Simplified Java grammar that allows the emergence of the considered possible sub-concepts.  
call other methods or to use support classes. Moreover, the set excluded all the methods whose tokenization yields vectors having more than 512 dimensions, the maximum input dimension for the CuBERT model, so as to avoid truncation of the input instances. Finally, we considered for the set of potential positive instances all the non-flawed methods labeled with any CWE, except CWE-369 and CWE-789, from the Juliet dataset.
## 9 Discussion, Contributions, and Future Directions
### Future Directions  
Looking ahead, several key areas warrant further exploration:  
1. Machine Learning for Predictive Maintenance: Incorporating online or streaming ML algorithms (e.g., incremental random forests) could facilitate proactive fault detection and adaptive maintenance strategies.
2. Edge-Focused Computations at Higher Sampling Rates: Investigating frequencies above 100 Hz may be necessary for processes with extremely transient dynamics, where real-time classification or anomaly filtering is best handled directly at the edge.
3. Hybrid Multi-Criteria Optimization: Extending the comparison of methods such as composite desirability versus TOPSIS can refine decision-making under multiple, potentially conflicting objectives (e.g., maximizing data fidelity versus minimizing computational overhead).
4. Enhanced Semantic Modeling: Future work could explore ontology-based reasoning and semantic web technologies (RDF, SPARQL) for deeper knowledge extraction and broader enterprise-wide data reuse.
5. Integration of Digital Twins: Real-time data from the proposed architecture can serve as a foundation for digital twin platforms, enabling virtual commissioning, \"what-if\" scenario analyses, and closed-loop feedback for continuous improvement.  
Overall, the flexible and modular nature of the proposed system setup ensures adaptability to rapid technological advances and evolving industrial requirements. By combining robust data acquisition, scalable analytics, and domain-focused knowledge modeling, manufacturers can not only optimize today's processes, but can also proactively prepare for tomorrow's industrial challenges.
## 4 Results and Discussion
### Effect of Vibration on the Pressure Drop
#### 4.1.3 0.35 g Vibration at Different Frequency  
The comparison of the pressure drop within ME obtained respectively by DMMFB and FEA is shown in Figure 6. No matter without or with vibration applied (the amplitude is 0.35 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz), the analytical and simulated pressure drop have a good agreement with each other.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 (No vibration) & 16.1 & - & 15.7 & - \\
100 & 13.7 & 14.9 & 13.3 & 15.3 \\
200 & 11.7 & 27.3 & 11.4 & 27.4 \\
300 & 10.3 & 36.0 & 10.0 & 36.3 \\
400 & 9.4 & 41.6 & 9.1 & 42.0 \\
500 & 8.5 & 47.2 & 8.3 & 47.1 \\ \hline \hline \end{tabular}  
* DMMFB means the dynamic model of the melt flow behavior.  
\end{table}
Table 3: Effective value of the pressure drop without and with 0.25 g vibration applied at different frequencies.  
Figure 5: The internal pressure drop of the extrusion liquefier without and with 0.3 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.  
Table 5 details the effective value of the above pressure drop for each condition. Again, the value decreases with vibration applied, and further decreases with the increase of vibration frequency.  
From the above, applying vibration onto the ME extrusion liquefier can reduce the internal pressure drop, and it will be further reduced with the increasing frequency of applied vibration. This can help improve the continuity and uniformity of the extrudate, and thereby enhance the forming quality of ME products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 (No vibration) & 16.1 & - & 15.7 & - \\
100 & 11.9 & 26.1 & 11.6 & 26.1 \\
200 & 10.0 & 37.9 & 9.8 & 37.6 \\
300 & 9.0 & 44.1 & 8.8 & 44.0 \\
400 & 8.3 & 48.4 & 8.1 & 48.4 \\
500 & 7.7 & 52.2 & 7.5 & 52.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effective value of the pressure drop without and with 0.3 g vibration applied at different frequencies.  
Figure 6: The internal pressure drop of the extrusion liquefier without and with 0.35 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 1 Introduction
This study proposes the REWin Transformer module, which combines the shifted window multi-head self-attention mechanism (SW-MSA), the gated feature refinement feedforward neural network (GFRN), and the gated convolution-based local enhancement feedforward neural network (GCEN). By refining feature maps, removing redundant information, and enhancing detail recovery, the denoising performance and texture reconstruction ability are significantly improved. The structure of this paper is arranged as follows: Section 2 provides an overview of the dataset used in this study. Section 3 elaborates on the methods employed. Section 4 presents a comparative analysis of the experimental results for different methods, highlighting their distinctions and key differences, along with an ablation study to analyze the contribution of each proposed improvement. Section 5 discusses the significance and versatility of the proposed method, as well as the limitations of the physical noise model used in this study. Finally, Section 6 discusses and summarizes the main contributions and findings of this study.
# Quantification of Liver Fibrosis--A Comparative Study
## 3 Literature Review
### Histological Samples
[48], only 12 large biopsy samples were collected, due to the lack of biological material with a thickness greater than 50 \(\upmu\)m. The patients included in this study presented chronic liver disease (CLD) with various degrees of severity, mainly due to excessive alcohol consumption and hepatitis B or C viruses. [PERSON] et al. [49] applied the Beijing classification to evaluate 71 paired biopsies with chronic hepatitis B, before and after Entecavir-based therapy. All 71 patients were primarily diagnosed with significant fibrosis or cirrhosis (Ishak \(>\) 3). A set of 260 samples (50 normal and 210 fibrotic) was used by [PERSON] et al. [50] to build an automated system that would determine whether a sample has notable fibrosis content or not. 40 male Wistar rats were used in [PERSON] et al. [51]. Thirty-five rats were treated with thioacetamide (TAA) over a period of 14 weeks to develop liver fibrosis. [PERSON] et al. [52] included 25 non-human tissues (rats with TAA-induced liver fibrosis) and 162 human CHB biopsy specimens. [PERSON] et al. [53] included a total of 162 enrolled CHB patients with paired liver biopsies pre and post-antiviral (Entecavir) treatment. In [PERSON] et al. [54], 175 total biopsy samples from patients with chronic HBV-infection were taken into consideration. To clarify the ability of SHG/TPEF microscopy imaging in fibrosis, the scanned biopsy images were divided into training (\(n=105\)) and testing (\(n=70\)) cohorts to develop and evaluate the fibrosis quantification system. In recent years, the employment of machine learning techniques has flourished as a necessary computer vision tool, but the published methodologies [55; 56; 57; 58; 59] were evaluated using a relatively limited dataset (8-79 biopsy specimens). This is due to the fact that the semi-quantitative assessment and the annotation of various histological structures is a time-consuming but also a necessary process for the employment of each method. Notably, [PERSON] et al. [55] conducted an experiment based on a dataset consisting of a total of 34 images. A subset of 10 images was used to train a Bayesian classifier, whilst 24 additional images were used to test the accuracy of the proposed method. [PERSON] and [PERSON]'s [56] biopsy image analysis prioritized the segmentation of fibrous tissue to determine the severity of liver damage. To achieve this goal, all experiments have been conducted with a dataset including 10 training and 22 test images. On the contrary, CNN deep architectures require a large amount of data samples compared to conventional machine learning algorithms. For this particular reason, about 10 images per patient (107 samples) were exported in [PERSON] et al. [60]. In [PERSON] et al. [61], the methodology's discrimination capability was tested in 25 Wistar rat biopsy samples, which were randomly distributed in two groups--(a) 21 subjects with TAA treatment and (b) a group of four as controls. In each case, a total of 100 slides coming from 5 \(\upmu\)m needle liver samples were analyzed.
## 4 Performance Evaluation
### Traffic Production  
We defined two types of traffic generation profiles: periodic generation and burst generation. In the periodic traffic generation, nodes generate data in a periodic manner similar to a CBR (Constant Bit Rate) traffic. In the burst traffic generation, nodes generate packets by doubling the rate of periodic generation in the first second and then refrain from generating traffic in the following second. This type of traffic generation emulates sudden data bursts. With both traffic generation profiles we reach the same number of generated packets. But, in the burst generation, nodes compete much more to access to the medium for 50% of the time compared to the periodic generation and spend the other 50% to forward packets accumulated in the packet queue. This results in higher offered load during the first 50% compared with the periodic traffic. Indeed, the same number of packets is generated in 50% of the time. Figure 6 shows how traffic is generated in both modes.
## 4 Experimental Evaluation
### Results
As could be expected, the worst performance is obtained by the random channel assignment, which obtains very low utility values. In the case of HC (which can be seen as an SA optimizer with \(T=0\)), we can note that it is able to obtain good results for the simplest scenarios (Table 3), but its performance decreases in more complex scenarios (Tables 3-5), due to its tendency of getting stuck in local maxima. Figure 6: SA evaluation in Scenario 9. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario 4**} & \multicolumn{3}{c}{**Scenario 5**} & \multicolumn{3}{c}{**Scenario 6**} \\ \cline{2-10}  & **avg** & **SD** & **CI** & **avg** & **SD** & **CI** & **avg** & **SD** & **CI** \\ \hline Random & 92.96 & 6.59 & 4.71 & 78.05 & 16.95 & 12.13 & 105.08 & 13.70 & 9.80 \\ SCS & 165.60 & 9.00 & 6.44 & 151.74 & 10.24 & 7.33 & 181.27 & 9.14 & 6.54 \\ ALHSO & 203.62 & 5.76 & 4.12 & 184.01 & 7.46 & 5.34 & 221.96 & 5.37 & 3.84 \\ HC & 196.82 & 9.94 & 7.11 & 179.11 & 11.06 & 7.91 & 211.76 & 11.05 & 7.90 \\ SA & **215.37** & 2.75 & 1.97 & **199.52** & 2.98 & 2.13 & **234.59** & 1.46 & 1.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Utility in scenarios with 50 APs and 500 sensors. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario 1**} & \multicolumn{3}{c}{**Scenario 2**} & \multicolumn{3}{c}{**Scenario 3**} \\ \cline{2-10}  & **avg** & **SD** & **CI** & **avg** & **SD** & **CI** & **avg** & **SD** & **CI** \\ \hline Random & 59.36 & 9.61 & 6.87 & 73.74 & 10.68 & 7.64 & 65.81 & 13.76 & 9.84 \\ SCS & 106.74 & 4.30 & 3.08 & 121.20 & 7.18 & 5.14 & 122.78 & 4.46 & 3.19 \\ ALHSO & 121.64 & 4.17 & 2.98 & 141.09 & 3.56 & 2.55 & 132.92 & 3.95 & 2.83 \\ HC & 123.30 & 3.50 & 2.50 & 140.37 & 4.93 & 3.53 & 138.02 & 5.31 & 3.80 \\ SA & **130.89** & 2.65 & 1.90 & **149.16** & 1.65 & 1.18 & **149.13** & 2.27 & 1.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Utility in scenarios with 50 APs and 350 sensors. SCS, sequential channel search; ALHSO, augmented Lagrangian harmony search optimization; HC, hill-climber. Figure 7: SA evaluation with different numbers of network providers (\(p\)). Regarding the standard deviation of the solutions, the algorithm that shows the lowest deviation is SA in almost every studied case (except in the most complex Scenarios 8 and 9, where SA is very similar to ALHSO), followed by ALHSO, SCS and random. As a conclusion, we can state that outperforming an optimization algorithm like ALHSO with negotiation techniques like SA shows that the use of negotiation techniques is advisable for the problem under study. Since the SA algorithm requires, as seen previously, the configuration of two parameters (initial temperature and the number of iterations), we finally include a comparison of all of the algorithms in a relative manner, with the purpose of analyzing the behavior of SA if it is not configured properly. Figure 8: Results relatives to the maximum. \begin{table}
## 3 Results and Discussion
### Determination of the Viable Cells of the Probiotic Strain  
The highest level of viable bacterial cells (7.0 \(\pm\) 0.7 \(\times\) 10\({}^{8}\) CFU/g) was recorded for the final product prepared from chickpeas (Ch-72), while the lowest count of _L. plantarum_ 299v (1.8 \(\pm\) 0.4 \(\times\) 10\({}^{8}\) CFU/g) was noted in GL-72. Also, a higher number of viable cells of the probiotic strain in the fermented beverages was noted in the products characterized by lower pH levels (Tables 1 and 6).  
Given the broad spectrum of health-promoting properties attributed to the tested strain, extensively documented in clinical research, it is reasonable to anticipate that the obtained final products may exhibit characteristics akin to functional foods. The findings suggest that the fermented beverages could serve as a carrier for the probiotic strain, as they adhere to the FAO/WHO stipulation that the concentration of probiotic bacteria in a 100 g portion should be at least 10\({}^{-6}\) CFU/g [49]. However, it is also indicated that fermented products should contain a higher count of live bacterial cells, ranging from 10\({}^{-7}\) to 10\({}^{-9}\) CFU/mL or g of product, and should be consumed daily to elicit a probiotic effect [50]. While all the final products in this study met these recommendations, it is imperative to conduct further in-depth investigations to validate their health-promoting effects.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tested Final Product** & **Survival CFU/g)** \\ \hline Ch-72 & 7.0 \(\pm\) 0.7 \(\times\) 10\({}^{8}\) \\ RL-72 & 3.1 \(\pm\) 0.5 \(\times\) 10\({}^{8}\) \\ GL-72 & 1.8 \(\pm\) 0.4 \(\times\) 10\({}^{8}\) \\ \hline \hline \end{tabular}  
* Explanatory notes: Ch-72—beverage obtained from chickpea after 72 h of fermentation; RL-72—beverage obtained from red lentil after 72 h of fermentation; GL-72—beverage variant obtained from green lentil after 72 h of fermentation; the mean values (\(n=3\)) \(\pm\) standard deviation followed by different lowercase letters (a–c) indicate a significant difference (\(p<0.05\)) between the final products.  
\end{table}
Table 6: Comparison of changes in pH and total extract content among the tested legume-derived beverages.  
Figure 1: Dendrogram showing the results of hierarchical cluster analyses of similarities of products in terms of the antioxidative activity and Fe\({}^{2+}\) chelating capacity. Explanatory notes: Ch—beverage variant obtained from chickpea; RL—beverage variant obtained from red lentil; GL—beverage variant obtained from green lentil; “-0” indicates the control (unfermented) beverage, while “-72” refers to products obtained after 72 h of fermentation (final product). The tested maximum concentration was 0.2 g/mL.