esa-sceva/satcom-chunk-collection huggingface dataset:
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Anonymous Message Forwarding of Encountering Nodes  
Assuming that nodes \(N_{i}\) and \(N_{j}\) encounter, \(N_{j}\) plans to send a message \(m\) to \(N_{i}\). The forwarding process of node \(N_{i}\) (the forwarding process of \(N_{j}\) is similar) is as follows:  
1. For a message \(m\), if \(N_{i}\) is not the destination node, it gets the utility value calculated by the TA, and then goes to step 3, or else goes to step 2.
2. Set the utility value to the max value of 1.
3. \(N_{i}\) compares the utility value with \(N_{j}\) by the solution for Yao's Millionaire Problem [22].
4. \(N_{i}\) receives message \(m\) if it has a higher utility value.  
In step 3, \(N_{i}\) and \(N_{j}\) need to compare the utility values, but cannot know each other's real utility value. This can be solved by the solution for Yao's Millionaire Problem. Yao's Millionaire Problem is a secure multiparty computation (SMC) problem, which enables multiple participants with private data to collaborate on their private data without divulging their private information.  
When \(N_{i}\) is the destination node of message \(m\), if \(N_{i}\) receives \(m\) directly without comparing utility value, its real ID will be revealed. Therefore, in step 2, the utility value is set to the max value of 1, and the destination node \(N_{i}\) can receive message \(m\) anonymously by the comparing of values in step 3.
## 4 The Importance of Biomarkers in Monitoring RT Toxicity
### Cell-Intrinsic Radiosensitivity Biomarkers
#### 4.1.2 DNA Damage Response  
Numerous studies have examined DNA damage response (DDR-related) proteins as radiosensitivity biomarkers. These proteins are indicative of DNA damage and play a critical role in repairing the DNA damage caused by IR, and alterations in their expression levels have been clearly associated with radiosensitivity. In this respect, the histone H2 AX as a radiosensitivity biomarker in various types of cancer, including breast, prostate, and head and neck tumors, was investigated. H2 AX is a key protein that signals DNA damage (mainly double-strand breaks, DSBs) by its phosphorylation at serine139. This process is known as \(\gamma\)H2 AX formation, and practically results in the appearance of \(\gamma\)H2 AX foci at sites of DNA damage [64]. \(\gamma\)H2 AX foci help to recruit and coordinate the DDR, and, for this reason, are broadly utilized as a marker for the extent and location of DNA damage, allowing for the quantification and visualization of DNA repair events via immunofluorescence (IF) [65]. The main steps of the \(\gamma\)H2 AX IF assay protocol involve fixation, permeabilizing, and blocking of the cells. The primary antibody against \(\gamma\)H2 AX is then added, followed by a fluorescently conjugated secondary antibody and nuclear counterstaining while washing steps are taking place between labeling [66]. The final samples are analyzed via fluorescence microscopy. Moreover, the \(\gamma\)H2 AX IF assay can be combined with other techniques such as Western blotting or flow cytometry for quantitative analysis for measuring DNA damage. [PERSON] et al., 2011 examined peripheral lymphocytes of 31 patients with resected head and neck cancer, undergoing adjuvant RT or radio-chemotherapy (RCT), using the \(\gamma\)H2 AX assay to evaluate the in vivo impact of individual DNA double-strand break (DSB) repair mechanisms on the incidence of severe oral mucositis. \(\gamma\)H2 AX foci were quantified before and at 0.5 h, 2.5 h, 5 h, and 24 h after in vivo radiation exposure (the first fraction of RT). World Health Organization scores for oral mucositis were documented weekly and were found to correlate well with DSB repair. An important finding of this study was that, at 24 h after RT, patients with a proportion of unrepaired DSBs higher than the mean value plus one standard deviation had an increased incidence of severe oral mucositis. The study concludes that the in vivo DSB repair by evaluating \(\gamma\)H2 AX foci loss is reliable in clinical practice, allowing the identification of cancer patients with impaired DSB repair. In turn, the incidence of oral mucositis has not been found to be closely correlated with DSB repair under the evaluated conditions [57]. In a prospective study, [PERSON] et al., 2014 analyzed DNA DSBs and repair via microscopic \(\gamma\)H2 AX foci analysis in peripheral lymphocytes from 80 post-chemotherapy breast cancer patients with histologically confirmed cancer, without distant metastasis and
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods
### Automated Data Retrieval from LP-Portal
#### 3.1.2 Road List Extraction  
The script sent a request to the 'getInteractiveLegendByLayerId' endpoint operated on the specific county's LP-Portal URL. The HTML response was parsed using the're' library to extract road names based on Polish road prefixes ('A', 'E', 'K', 'W' or 'P') indicating the road type. These extracted road names were then stored in a JSON file for further processing, as presented in Figure 5.
# Design and Performance Evaluation of eLoran Monitoring System
## 2 Principle and Structure  
### Principle of eLoran Time Service System  
The principle of the eLoran system evolved from the Loran-C system; eLoran is an internationally standardized positioning, navigation, and timing (PNT) service system with the advantages of long range, good stability, high reliability, and strong anti-interference capability. The timing service can usually achieve a ground-wave timing accuracy of better than 1 us, and a timing accuracy of better than 100 ns can be realized by using differential technology [14].  
The structural components of the eLoran system generally include a broadcasting station, monitoring station, and differential enhancement station as shown in Figure 1. Among them, the broadcasting station adopts solid-state transmitter technology and datachain technology to realize high-precision time signal broadcasting; the monitoring station is set up to ensure the reliability and accuracy of time broadcasting; and the differential enhancement station is set up to improve the accuracy of time broadcasting.  
All broadcasting stations are time-synchronized, and trace back to the Chinese standard time UTC (NTSC) kept by the National Timing Center.  
The timekeeping system at the eLoran monitoring station is traceable to UTC (NTSC), an eLoran signal belongs to the long-wave band, and the propagation performance is stable; by comparing the time difference between the output time signal of the monitoring receiver and the time signal of UTC (NTSC), you can judge the working condition of the broadcasting station and make an early warning.  
The differential enhancement station monitors the correction amount of the timing deviation of an eLoran timing signal in a certain area and broadcasts it to the user by the system, and the user obtains high-precision timing service after deducting the correction amount of timing deviation.
## 4 Model Implementation  
### Determination of CPr Using RTO Data  
CPr depict the likelihood of a BE based on historical failure data. The historical failure incident data obtained from the RTO are utilized for this study. The data are divided into different categories based on the BE distribution explained in Section 3.1. After categorization, the data were reviewed carefully to ensure preprocessing, including cleansing and filling in the missing values. At this stage, the consistency of the values is also verified. The RTO data consist of power grid failure data from 14 states, which include Arkansas, Colorado, Iowa, Kansas, Louisiana, Minnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, and Wyoming. Table 2 shows the calculations for CPr based on the RTO data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Intermediate Event** & **Basic Event** & **Occurrences** & **CPr** \\ \hline \multirow{6}{*}{Equipment conditions} & Capacitor & 16 & 0.0159 \\ \cline{2-4}  & Transformers & 42 & 0.0417 \\ \cline{2-4}  & Circuit breakers & 188 & 0.1867 \\ \cline{2-4}  & Generators & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Reactor & 1 & 0.0010 \\ \cline{2-4}  & Transmission lines & 300 & 0.2979 \\ \hline \multirow{6}{*}{Weather conditions} & Rain & 9 & 0.0089 \\ \cline{2-4}  & Wind speed & 37 & 0.0367 \\ \cline{2-4}  & Weather & 62 & 0.0616 \\ \cline{2-4}  & Temperature & 28 & 0.0278 \\ \hline \multirow{6}{*}{Environmental factors} & Vegetation management & 5 & 0.0050 \\ \cline{2-4}  & Earthquakes & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Storms & 176 & 0.1748 \\ \cline{2-4}  & Fire risks & 31 & 0.0308 \\ \cline{2-4}  & Floods & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Lightning & 54 & 0.0536 \\ \cline{2-4}  & Tornados & 26 & 0.0258 \\ \hline \multirow{6}{*}{Operational parameters} & Power factor & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Voltage levels & 8 & 0.0079 \\ \cline{2-4}  & Frequency & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Current flow & \multicolumn{2}{c}{No historical data} \\ \cline{2-4}  & Maintenance & 5 & 0.0050 \\ \cline{2-4}  & Disconnect & 11 & 0.0109 \\ \hline \multirow{6}{*}{Cybersecurity} & SCADA (supervisory control and data & \multirow{2}{*}{8} & 0.0079 \\ \cline{2-4}  & acquisition) systems & & \\ \cline{1-1} \cline{2-4}  & Unauthorized access & \multicolumn{2}{c}{No historical data} \\ \cline{1-1} \cline{2-4}  & Cyberattacks & \multicolumn{2}{c}{No historical data} \\ \cline{1-1} \cline{2-4}  & & & \\ \end{tabular}
\end{table}
Table 2: CPr based on historical RTO data.
## 5 Conclusions  
In conclusion, the analysis of vibration testing in this section has provided valuable insights into its accuracy and potential for predicting damage and fracture. The behavior of two specimens was studied through experimentation and the finite element method, and their accuracy was evaluated. The results were analyzed using Simulink, providing a deeper understanding of the relationship between frequency and time and force and time. The natural frequencies obtained from the FE model were utilized to predict the onset of internal damage and carbon steel fracture. This study's findings have the potential to inspire further research and development in the field of V-shaped plate vibration testing. The study found that the two V-shaped plates exhibit varying natural frequency values. Specifically, the double-sided V-shaped plate demonstrated an increase in natural frequency, while the single-sided notched V-shaped plate showed a significant decrease in natural frequency. These observations were consistent across both oblique and straight position adjustments, indicating the reliability of the results. In addition, the experimental natural frequencies were compared to those calculated using the finite element method model. The comparative analysis revealed that the calculated frequencies from the model presented in Figures 18, 20, 24 and 26 are remarkably similar to the experimental measurements.  
Furthermore, the calculated results from the model demonstrate high consistency. However, the experiment is essential to take note of the significant changes in value once the shape reaches its seventh mode. Recent observations have shown that the calculated value generated by the finite element method model surpasses the actual experimental value, raising concerns regarding the model's reliability and accuracy in predicting values beyond the seventh mode shape. To establish more dependable and accurate models that can predict values at higher shape modes, further research is necessary to identify the underlying factors leading to this deviation. Acknowledging that finite element modeling is a precise and valuable tool for determining natural frequencies is essential.  
Conceptualization, K.C. and T.S.; methodology, K.C.; software, T.S.; validation, K.C. and T.S.; formal analysis, K.C.; investigation, K.C.; resources, K.C.; data curation, K.C.; writing--original draft preparation, K.C.; writing--review and editing, K.C. and T.S. All authors have read and agreed to the published version of the manuscript.  
Figure 26: Natural frequencies of one-sided V notch slide shape on the first models.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Damage Model of a Karst Tunnel
\begin{table}
\begin{tabular}{l l l l} \hline
**Symbol** & **Meaning** & **Symbol** & **Meaning** \\ \hline \(c\) & Soil cohesion & COV & The coefficient of variation \\  & The internal friction of the & & The self-weight of rock and \\ \(\varphi\) & geotechnical mass & \(\gamma_{0}\) & soil mass \\ \(D_{0}\) & The tunnel diameter & \(F_{s}\) & Safety factors \\ \(H_{0}\) & The height of the collapsing mass & \(H_{3}\) & The buried depth of tunnel \\ \(D_{1}\) & The diameter of the karst cave & \(E\) & Elastic modulus \\ \(H_{1}\) & The clear distance betweenthe & & \\  & karst cave and the tunnel & & \\ \(q\) & The medium pressure of karst cave & & \\ \hline \end{tabular}
\end{table} 
Table 1: Symbol annotation table. The PH\(-2\) failure mode, based on the upper bound theorem of limit analysis and energy dissipation theory, consists of three failure zones: P\(-1\), P\(-2\) and P\(-3\). The P\(-1\) region consists of the triangular block \(A_{T,0}A_{T,1}A^{\prime}_{T,1}\), \(n-1\) triangular blocks \(A_{T,0}A_{T,i-1}A_{T,i}\) and \(n-1\) triangular blocks \(A_{T,0}A^{\prime}_{T,i-1}A^{\prime}_{T,i}\). The P\(-2\) block consists of the triangle block \(B_{T,0}B_{T,1}A_{T,0}\) and \(n-1\) triangular blocks \(B_{T,0}B_{T,i-1}B_{T,i}\). The left side of the P\(-3\) region consists of the polygonal block \(B_{T,n}B_{T,i}C_{T,1}C_{T,0}O_{T,0}\) and \(n-1\) triangular blocks \(C_{T,0}C_{T,i-1}C_{T,i}\), and the right side of the P\(-3\) region consists of the polygonal block \(B_{T,n}O_{T,0}C^{\prime}_{T,0}C^{\prime}_{T,1}O^{\prime}_{T,0}\) and \(n-1\) triangular blocks \(C^{\prime}_{T,0}C^{\prime}_{T,i-1}C^{\prime}_{T,i}\), as shown in Figure 1b. In view of the complexity of the practical projects, in order to facilitate the calculation of the analytical solution of deeply buried tunnel excavation when the cavern is at the bottom of the tunnel, the complicated and diverse karst caves are simplified into a circular cavity, and the tunnel model could be simplified to the plane strain problem. Consequently, the following basic assumptions should be considered for the model of the deeply buried tunnel:  
1. The constitutive model of the rock and soil masses follows the Mohr-Coulomb strength criterion and the geotechnical body obeys the associated flow rule when the plastic yielding occurs. 2. The tunnel diameter is \(D_{0}\), the diameter of the karst cave is \(D_{1}\), the buried depth of the tunnel is \(H_{3}\), the heights of the collapsing bodies \(A_{t,0}B_{t,1}B_{t,0}\) and \(A_{T,0}B_{T,1}B_{T,0}\) are \(H_{0}\), the clear distance between the karst cave and the tunnel is \(H_{1}\), the geotechnical weight is \(\gamma_{0}\), the internal friction angle is \(\varphi\), and the cohesion of the rock and soil masses is \(c\). 3. The karst cave would be assumed to be empty or filled with the filling medium. 4. The physical property parameters of the geotechnical body have been assumed to conform to the Gaussian distribution.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 6 Clinical Applications and Implications
### Treatment Planning and Monitoring  
Volumetric assessment methods offer insights into the severity and distribution of edema in the affected limb, aiding in treatment planning and monitoring. By quantifying the limb volume, clinicians can determine the extent of the edematous tissue and establish baseline measurements for comparison throughout the treatment course. This information allows for individualized treatment approaches, including the selection of appropriate compression therapy, manual lymphatic drainage, or surgical interventions [1; 45; 71].  
In this context, different rehabilitation treatments have been proposed, including skin care, manual lymphatic drainage (MLD), intermittent pneumatic compression (IPC), compression therapy, exercise therapy, education and self-care, and lifestyle modifications [1; 45; 71]. These treatments might be effectively combined and vary based on patients' characteristics [30]. By periodically measuring the limb volume, clinicians can objectively evaluate the response to interventions and make necessary adjustments to the treatment plan [43]. Reductions in limb volume indicate successful edema management, while stable or increasing volumes may necessitate modifications to the treatment approach. Moreover,the long-term monitoring of limb volume changes can also provide valuable data on the natural history of lymphedema and the efficacy of different treatment modalities.  
Moreover, specific assessment methods might provide insights into tissue composition and anatomical anomalies, offering valuable information for informed treatment planning [80]. For instance, BIS might reveal a substantial increase in extracellular fluid in the lymphedematous limb compared to the unaffected limb [81, 82]. On the other hand, MRI can identify anatomical abnormalities such as fibrotic tissue or lymphatic vessel obstructions [83], providing further insight to clinicians for tailoring specific rehabilitation interventions based on tissue characteristics.  
Integrating specific methods enriches the evaluation by showcasing how a precise assessment might offer actionable insights beyond mere volume measurements. These insights into tissue composition and anatomical nuances enable clinicians to develop interventions that align more closely with the unique needs of individual patients with lymphedema.
## 4 Results and Discussions
### Result Analysis and Performance Comparison
#### 4.2.2 Comprehensive Performance of Comparative Experiments  
In this section, the proposed DeepFMP model is compared with several classical models using the extracted textual and visual features as the data foundation first. Then, to evaluate the impact of price features, the price feature vectors generated by the designed price feature module are incorporated. The overall performance and experimental results of all models on the constructed training set are presented in Table 2. For a comprehensive comparison, representative models for first-order, second-order, and high-order feature interactions are included.  
The experimental results presented in the table demonstrate that the DeepFMP model outperforms other baseline models across all evaluation metrics when utilizing only textual and visual feature vectors. Specifically, the AUC value of DeepFMP shows an improvement ranging from 1.6% to 12.2% compared to the baseline models, with the most significant enhancement observed in the NDCG@10 metric, where it achieves a 3.2% improvement over the second-best model, DeepFM. These results indicate that the DeepFM model, enhanced with a multi-head self-attention mechanism, effectively improves the quality and accuracy of recommendation outcomes. Specifically, the DeepFMP model introduces a  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **AUC** & **Recall@5** & **Recall@10** & **NDCG@5** & **NDCG@10** \\ \hline \multirow{4}{*}{Image, text} & LR & 0.680 & 0.231 & 0.267 & 0.298 & 0.419 \\  & FM & 0.759 & 0.415 & 0.516 & 0.388 & 0.483 \\  & Wide\&Deep & 0.763 & 0.461 & 0.549 & 0.405 & 0.501 \\  & GP-BPR & 0.779 & 0.486 & 0.586 & 0.438 & 0.530 \\  & DeepFM & 0.786 & 0.519 & 0.601 & 0.455 & 0.553 \\  & DeepFMP & 0.802 & 0.536 & 0.622 & 0.476 & 0.585 \\ \hline \multirow{4}{*}{Image, text} & LR & 0.709 & 0.239 & 0.278 & 0.311 & 0.436 \\  & FM & 0.786 & 0.432 & 0.538 & 0.405 & 0.503 \\ \cline{1-1}  & Wide\&Deep & 0.794 & 0.479 & 0.572 & 0.422 & 0.526 \\ \cline{1-1}  & GP-BPR & 0.811 & 0.506 & 0.610 & 0.456 & 0.552 \\ \cline{1-1}  & DeepFM & 0.816 & 0.545 & 0.635 & 0.483 & 0.596 \\ \cline{1-1}  & DeepFMP & 0.833 & 0.558 & 0.648 & 0.495 & 0.609 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experimental results of various recommendation models.
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 1 Introduction
On the other hand, it was necessary to decrease extra expenses by changing the joint line geometry, using ultra-hard materials for the tool, and producing the FSW tool with specific profiles. Recently, assisted friction stir welding processes, such as laser assist [20], ultrasonic assist [21], rapid cooling assist [19], fed friction stir welding [22], arc assist, and electrical-assisted friction stir welding have been introduced by various research groups to increase the efficacy of FSW for the joining of high-melting-temperature materials. The application of a laser resulted in lower residual stress [20]. It is reported that the application of ultrasonic vibrations has no impact when a low welding speed is used, while it is useful when a high-speed welding process is used [21]. Rapid cooling also affects almost all aspects of the joint, including grain size, microhardness, etc. [20]. Fed friction stir welding is a novel welding method used to join polymers to metals, where reinforcing particles are injected into the nugget during welding, which improves the mechanical properties of the joint [21]. In this paper, the details and advances of the electrical-current-assisted friction stir welding (EA-FSW) method will be discussed [23]. Electrical current assistance can help improve joint microstructure, increase the flow ability of base materials at dissimilar joints, and decrease the welding time to increase the process efficacy in industries.