esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Invasive Graphene ECG Electrodes  
While a wide variety of epidermal graphene ECG electrodes offer a low-cost, high-comfort, and non-invasive solution to long-term ECG monitoring, there are still limitations in the quality of the collected ECG signals. For example, epidermal electrodes tend to move relative to the skin in daily wear, which may introduce motion artifacts [78]. At the same time, non-invasive electrodes are more susceptible to external noise interference, which reduces the signal-to-noise ratio [79]. In addition, skin conditions, such as sweat, skin thickness, and cleanliness will greatly affect electrode-skin impedance [80]. To address the disadvantages of epidermal electrodes, some invasive graphene electrodes have been developed to produce higher-quality ECG signals.
###### Abstract  
This study presents a robust numerical framework for evaluating the upper-bound ultimate bearing capacity of shallow foundations in cohesive and C-phi soils using a self-developed finite element method. The model incorporates multi-segment zero-thickness interface elements to accurately simulate soil discontinuities and progressive failure mechanisms, based on the Mohr-Coulomb failure criterion. In contrast to optimization-based methods such as discontinuity layout optimization (DLO) or traditional finite element limit analysis (FELA), the proposed approach uses predefined failure mechanisms to improve computational transparency and efficiency. A variety of geometric failure mechanisms are analyzed, including configurations with triangular, circular, and logarithmic spiral slip surfaces. Particular focus is given to the transition zone, which is discretized into multiple blocks to enhance accuracy and convergence. The method is developed for two-dimensional problems under the assumption of elastic deformable-plastic behavior and homogeneous isotropic soil, with limitations in automatically detecting failure mechanisms. The proposed approach is validated against classical theoretical solutions, demonstrating excellent agreement. For friction angles ranging from 0\({}^{\circ}\) to 40\({}^{\circ}\), the computed bearing capacity factors N\({}_{c}\) and N\({}_{q}\) show minimal deviation from the analytical results, with errors as low as 0.04-0.19% and 0.12-2.43%, respectively. The findings confirm the method's effectiveness in capturing complex failure behavior, providing a practical and accurate tool for geotechnical stability assessment and foundation design.  
shallow foundation; ultimate bearing capacity; upper-bound limit analysis; zero-thickness interface elements; finite element analysis 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2018-2019. External Links: Document Cited by: SS1.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Rocks and Soil Migration Factors
#### 2.2.2 Diffusion  
Diffusion is the process by which radon moves from areas of higher concentration to areas of lower concentration through the pores and fissures of rocks until it reaches a uniform concentration [45]. The rate of diffusion depends on various factors, such as the size of the pores and the pressure gradients across them. As radon diffuses through rocks, it can become trapped in soil, water, or even buildings, where it can accumulate to dangerous levels [46]. The diffusion of radon is also affected by other factors, such as the temperature and moisture content of the rocks [47]. In general, higher temperatures and moisture content can increase the diffusion of radon, while lower temperatures and drier conditions can slow down its diffusion [48]. A pioneer study was presented by [PERSON] (1992) explored the factors that govern the rate at which two radon isotopes, \({}^{222}\)Rn and \({}^{220}\)Rn, migrate from soil into the atmosphere, both outdoors and indoors [49]. According to the article, radon is created by the radioactive decay of radium in the first few metres of the Earth's crust, and during its brief lifetime, it can migrate into the atmosphere. As the respiratory tract's lining cells are exposed to the short-lived decay products of radon by inhalation, this poses a serious health risk. The investigation also shows that the average amount of radium in surface soils in the US is between 10 and 100 Bq\(\cdot\)kg\({}^{-1}\). The amount of radon produced in a material that diffuses into the pore fluids is referred to as the emanation coefficient, which is normally about 0.2 but can vary. Radon may exist in pore air, be dissolved in pore water, and be absorbed by soil granules in soil pores. The author discovered that radon primarily migrates through soil pores via molecular diffusion, with the exception of areas near buildings. For the radioactive elements \({}^{222}\)Rn and \({}^{220}\)Rn, the observed average flux densities from undisturbed soil into the atmosphere are 0.015-0.048 Bq\(\cdot\)m\({}^{-2}\).s\({}^{-1}\) and 1.6-1.7 Bq\(\cdot\)m\({}^{-2}\).s\({}^{-1}\), respectively. Soil is the main source of radon in the majority of structures. The study also showed that the advective flow of soil gas across building substructure penetrations is an essential part of the transport process and is influenced by weather patterns and the functioning of HVAC systems. Although the study found that advection was more important in structures with high concentrations of radon, it is unclear how much diffusion through soil pores contributes to radon entrance into buildings.
## 3 Experimental Setup and Result Analysis
### Experimental Results
#### 3.4.2 Contrast Experiment  
To assess the effectiveness of the improved MobileNetV3 model, its performance was evaluated against that of the ResNet18 [23] and EfficientNetB0 [24] models. These models are widely used for image classification, especially in the context of breast cancer histopathology images. The evaluation was performed across four different magnification levels, providing a thorough and reliable comparison.  
Table 7 presents the performance metrics for each model across datasets with different magnifications, including test accuracy, precision, and recall. The proposed network structure outperforms other models in overall classification performance for all four magnification levels. The multi-stage information fusion strategy employed here significantly enhances the model's accuracy by capturing rich image information in breast cancer histopathology images. In the initial two stages of the feature extraction process, the SK attention mechanism is introduced to extract multi-scale features adaptively, improving the learning of texture, color, morphology, and other underlying features. In the latter two stages, the improved SCA mechanism captures spatial context information more precisely. This targeted extraction of underlying and deep semantic features, followed by a weighted fusion of the rich features from different stages, enables the model to effectively learn the subtle differences among various subtypes of lesions in breast cancer histopathology images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **FLOPs** & **Parameters** \\ \hline MobileNetV3 & 233.56 M & 4.21 M \\ MobileNetV3\_CA & 234.16 M & 4.26 M \\ MobileNetV3\_SCA & 234.25 M & 4.32 M \\ MobileNetV3\_SK & 338.49 M & 4.29 M \\ MobileNetV3\_Strategy & 233.85 M & 4.34 M \\ MobileNetV3\_All & 339.46 M & 4.53 M \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of different improved models and FLOPs comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Magnification Factor** & **Model** & **ACC** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{40\(\times\)} & MobileNetV3 & 91.272 & 89.485 & 93.397 & 91.168 \\  & ResNet18 & 85.037 & 87.803 & 80.170 & 83.619 \\  & EfficientNetB0 & 89.027 & 88.447 & 87.483 & 87.884 \\  & MobileNetV3\_All & 96.259 & 95.729 & 95.789 & 95.726 \\ \hline \multirow{4}{*}{100\(\times\)} & MobileNetV3 & 89.526 & 89.604 & 88.551 & 88.627 \\  & ResNet18 & 83.226 & 84.108 & 81.286 & 82.759 \\ \cline{1-1}  & EfficientNetB0 & 86.267 & 86.15 & 85.923 & 85.872 \\ \cline{1-1}  & MobileNetV3\_All & 94.763 & 94.091 & 94.457 & 93.95 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative experimental results of datasets at different magnification factor (%).
## 4 Parameter Discussion
### Effect of Number of Bridge Spans  
In the above analyses, only single-span simply supported beams have been considered. Through the above discussion, using variational modal decomposition-continuous wavelet transform method is effective for the damage identification of simply supported beams. And in reality, it is mostly continuous bridges. Therefore, in this section, two-span and three-span beams will be investigated to verify the effectiveness of the proposed method for continuous bridges, assuming that the length of each span is equal to that of a single span. A two-span bridge is considered in Case 8, where Element 7 and Element 13 of the first span experience damage levels of 30%, respectively, and Element 38 of the second span experience damage levels of 30%. Figure 9a shows the damage identification results for the Scale = 253. From the figure, it can be seen that there is a clear prominent peak at the positions of Element 7, 13, and 38. In Case 9, three span bridges are considered, where Element 13 of the first span experience damage levels of 30%, Element 32, Element 38, and Element 44 of the second span experience damage levels of 20%, 15%, and 20% respectively, and Element 63 of the third span experience damage levels of 30%. Figure 9b shows the damage identification results for the Scale = 253. It can be seen from the figure that there are distinct prominent peaks at the positions of Element 13, Element 32, Element 38, Element 44, and Element 63. Case 8 and Case 9 show that the method using variational modal decomposition-continuous wavelet transform is also effective for damage identification in continuous beams.
## 4 Application Characteristics
### Other Application Fields  
The applications of the HSC can be broadened to other key fields, including ocean observations (e.g., maritime emergency searches and facilitating rescue operations, port development and monitoring, and marine logistics and transportation), natural disaster detection (e.g., oil spills, red tides, and storm surges), and coastal zone development monitoring (e.g., delta development and offshore wetland protection).
# Comparative Analysis of Kazakhstan and European Approaches for the Design of Shallow Foundations
## 1 Introduction
Even after the introduction of SP RK EN and even with an understanding of the advantages of applying the European approach, the national SNiP-based regulations are still widely applied for the design of buildings and structures in Kazakhstan. Moreover, the regulations developed in accordance with the Soviet approach are widely used for geotechnical design in the territory of the Commonwealth of Independent States (CIS). Nowadays, the CIS countries, including the Republic of Belarus, the Republic of Kazakhstan, the Russian Federation, and Ukraine, are at various stages of incorporating the Eurocode into their own technical regulations (Figure 1). The harmonization of EC 7 and SP RK in Kazakhstan still requires careful study and further consideration of their design applications. As the transition from SP RK to EC 7 has already been initiated, it is important to understand the differences between the two codes of practice for the design of shallow foundations. Several previous studies on the comparison of EC 7 and SNiP-based regulations have revealed the differences between the methods applied for the geotechnical design of shallow foundations [8; 9; 10; 11; 12; 13; 14]. For example, [PERSON] et al. [13] showed that a SNiP-based approach provides significantly fewer conservative results compared to the European approach for geotechnical design. [PERSON] and [PERSON] [12] stated that EC 7 applies higher safety factors compared to SNiP for the design of shallow foundations. With reference to [PERSON] and [PERSON] [10] and [PERSON] [11], the traditional Soviet and European approaches were compared based on the calculation results of the elastic settlement of shallow foundations, indicating higher values for EC 7. However, the previous studies did not consider both the ultimate and serviceability limit states in the comparison of EC 7 and SNiP-based regulations for the complete design of shallow foundations. The objective of this article is to compare SP RK and EC 7 in terms of the conservativeness of their shallow foundation design by performing calculations for both bearing capacity and elastic settlement. The comparative analysis of the design codes is performed by the determination of bearing capacity and elastic settlement for two design cases in Nur-Sultan city.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 5 Agent-Based VM Migration
### VM Acceptance Policies  
A VM acceptance policy defines the criteria for accepting VMs through live migration from other overloaded SAs based on their resource usage levels or from other security-level-violated SAs based on the security level mismatching with a certain VM.  
The policy comprises three elements: (i) idle computing resources including CPU and memory; (ii) after accepting the migrated VM, whether the resource utilization level reaches the specified threshold; and (iii) a security level. Both resource-based and security-based acceptance thresholds are provided to enable SAs to accept or reject the migration proposal.  
The idle computing resources and resource utilization level of a VM acceptance policy outlines the remaining resources of the host and the resource utilization of the host after the VM occupies its required resources. For example, if a VM with a high CPU resource requirement is to be accepted, the host must not only have sufficient idle CPU resources, but after accepting the VM, its resource utilization level should not exceed the source host of the VM. The resource utilization levels must be evaluated by the cloud computing administrator as part of this project.  
To enhance the security of cloud computing, the VM acceptance policy incorporates a security level. Specifically, when a host receives a VM, it will only accept VMs with a matching security level. The security level of the VM must be the same as the security level of the host, irrespective of whether the VM's security level is higher or lower than that of the host. This policy is designed to prevent unauthorized access or data breaches resulting from a mismatch in security levels between the host and the VM. By adopting this security matching level, cloud computing can better protect its resources and data from security risks.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## Appendix A Proof of Theorem 1  
We square Formulas (6) and (7), respectively, to obtain the following Equations (A1) and (A2), i.e.,  
\[\mathcal{D}_{i}^{2}(t+1)= \Big{[}\mathcal{D}_{i}(t)-\sum_{j=1}^{N}\delta_{ij}(t)\gamma_{i}(t )\tau\Big{]}^{2}+\varphi_{i}^{2}(t)\tau^{2}+2\Big{[}\mathcal{D}_{i}(t)-\sum_{j =1}^{N}\delta_{ij}(t)\gamma_{i}(t)\tau\Big{]}^{+}\varphi_{i}(t)\tau\] \[\leq \mathcal{D}_{i}^{2}(t)+\sum_{j=1}^{N}\delta_{ij}^{2}(t)\gamma_{i }^{2}(t)\tau^{2}+\varphi_{i}^{2}(t)\tau^{2}-2\mathcal{D}_{i}(t)\sum_{j=1}^{N} \delta_{ij}(t)\gamma_{i}(t)\tau+2\mathcal{D}_{i}(t)\varphi_{i}(t)\tau\] (A1) \[\leq (\tau\xi_{\max})^{2}+(\tau\varphi_{\max})^{2}+\mathcal{D}_{i}^{2 }(t)+2\tau\mathcal{D}_{i}(t)\Big{(}\varphi_{i}(t)-\sum_{j=1}^{N}\delta_{ij}(t )\gamma_{i}(t)\Big{)}\]  
\[\big{(}E_{i}(t+1)-\mathcal{B}\big{)}^{2}= \big{(}(E_{i}(t)-\mathcal{B})-E_{i}^{c}(t)+E_{i}^{h}(t)\big{)}^{2}\] (A2) \[\leq (E_{i}(t)-\mathcal{B})^{2}+\big{(}E_{i}^{c}(t)\big{)}^{2}+\big{(} E_{i}^{h}(t)\big{)}^{2}+2(E_{i}(t)-\mathcal{B})\big{(}E_{i}^{h}(t)-E_{i}^{c}(t) \big{)}\] \[\leq (\tau\rho_{\max})^{2}+(E_{\max}^{c})^{2}+(E_{i}(t)-\mathcal{B})^{ 2}+2(E_{i}(t)-\mathcal{B})\big{(}E_{i}^{h}(t)-E_{i}^{c}(t)\big{)}\]  
Then, according to (11) and (12), we can derive the following formula  
\[\Delta(t)= \frac{1}{2}\sum_{i=1}^{M}\big{[}\mathcal{D}_{i}^{2}(t+1)+(E_{i}( t+1)-\mathcal{B})^{2}-\mathcal{D}_{i}^{2}(t)-(E_{i}(t)-\mathcal{B})^{2}\big{]}\] (A3) \[\leq \frac{M}{2}\Big{[}(\tau\xi_{\max}^{c})^{2}+(\tau\varphi_{\max})^ {2}+(\tau\rho_{\max})^{2}+(E_{\max}^{c})^{2}\Big{]}+(E_{i}(t)-\mathcal{B}) \big{(}E_{i}^{h}(t)-E_{i}^{c}(t)\big{)}\] \[+\sum_{i=1}^{M}\Big{[}\tau\mathcal{D}_{i}(t)\Big{(}\varphi_{i}(t )-\sum_{j=1}^{N}\delta_{ij}(t)\gamma_{i}(t)\Big{)}\Big{]}\]
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 3 Photonics-Based Subsystems for All-Optical Processing
### All-Optical Coherent and Simplified Coherent Solutions: Challenges and Opportunities
The horizontal and vertical signals are the outputs of the polarization beam splitter. In this architecture, ADCs and DSP can be completely removed; therefore, only the On Off Keying (OOK) or PAM4 signal can be detected by the receiver. The most recent work in the literature is called quasi-coherent [35, 36], also classified in the category of simplified coherent approaches. Here, the signal and LO are mixed using a coupler. Then passing them through a PBS, the two resulting optical signals are fed into two PDs and, next, by performing envelope detection, they are inserted into the electronic amplifier. Based on these aforementioned techniques, we conclude that dual-polarization schemes or Quadrature Amplitude Modulation (QAM) formats can be dispensed with under certain conditions, for the sake of minimization of optical and/or electrical components, albeit the reduced data rates. That is why these coherent-like approaches are best suited for extremely low-cost and low-power consumption solutions in short-reach applications but do not typically qualify for either long-haul transmission or for high-end (inter) datacenter interconnects. It is for this reason that new coherent approaches emerge in order to cover the white areas of the optical communication market. Based on the complexity, which directly impacts the cost and power consumption, Figure 8 maps the different options with the network domain where they can be applied.
## 2 Materials and Methods
### Imaging Post-Processing and Feature Extraction  
The following parameters were calculated on the baseline PET/CT (PET-1): the number of FDG-avid lesions, maximum and mean standardized uptake value (SUVmax and SUVmean), total MTV, and TLG. In each patient, the primary cutaneous tumor was segmented by applying a threshold of 40% of the SUVmax within the lesion's bounding box using dedicated software (PET VCAR; Advantage Workstation 4.7, GE Healthcare), in accordance with standard practice (Figure S1). In cases of multiple skin lesions, the one with the highest uptake value (SUVmax) was classified as the index lesion and segmented.  
Radiomics features (RFts) were extracted from the VOIs of tumors using the Pyradiomics package (version 3.1.0). Supplementary Text S1 shows the parameters used for radiomics feature extraction from CT and PET images. In our pipeline, to control multi-collinearity, among radiomic features, we applied a two-step filter-based selection strategy. The features significantly associated with outcome (best clinical response or tumor grade) were identified using the Mann-Whitney U-test with \(p\)-value thresholds of 5.8 \(\times\) 10\({}^{-5}\) and 0.05 for primary and secondary aim points, respectively. Subsequently, to reduce redundancy, pairwise Spearman rank correlation coefficients were calculated among the selected features. These were the statistical criteria for labeling a feature as robust. Inter-observer variability was minimized through the use of a standardized semi-automatic segmentation approach. Specifically, each primary tumor was segmented using a threshold of 40% of SUVmax within the lesion's bounding box, as implemented in PET VCAR (Advantage Workstation 4.7, GE Healthcare). This threshold-based method, normally used in clinical practice, limits variability due to operator-dependent contouring.  
Complete details of the selected features and their associated \(p\)-values are provided in the Supplementary Material (Tables S1-S4).  
Features were extracted from the original and wavelet-filtered images, resulting in a set of quantitative biomarkers for each 3D VOI in both the PET and CT datasets. Radiomic features can be classified into three classes: shape-based features, statistical features (including histogram-based and texture-based features), and transform-based features. The Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run-Length Matrix (GLRLM), Gray-Level Size-Zone Matrix (GLSZM) and Gray-Level Distance-Zone Matrix (GLDZM), and, finally, Neighborhood Gray-Tone Difference Matrix (NGTDM) and Neighborhood Gray-Level Dependence Matrix (NGLDM) were calculated.  
For the wavelet-filtered images using Coiflet1, only second-order texture features were extracted.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Route Planning
Since the number \(n\) of RSUs in a CA domain is not very large, and the number \(k\) of RSUs on the path of a vehicle during a certain trip is relatively small, generally less than 50. The time complexity in this section falls within an acceptable range.
# PDF Malware Detection Based on Fuzzy Unordered Rule Induction Algorithm (FURIA)
## 3 Research Methodology
### Fuzzy Unordered Rule Induction Algorithm (FURIA)  
The FURIA is a new algorithm introduced by [PERSON] and [PERSON] that is responsible for generating fuzzy logic rules from a given database and classifying it using the obtained rules [22]. Fuzzy logic algorithms are well-known for their properties, such as classification rules that are simply understood by the reader, the capacity to analyze linguistic input, and the ability to enable expert judgment. They can also be used as a tool for classification purposes [25; 26]. FURIA is the advanced version or derivative of the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm. The RIPPER Algorithm is a classification algorithm based on rules. The training set is used to generate a set of rules. It is a famous rule induction algorithm. It generates fuzzy rules rather than traditional rules to replicate more flexible classification parameters. The fuzzy rules are constructed by substituting fuzzy intervals with a trapezoidal relevance function in association with the original RIPPER algorithm's advanced rule induction approach [27].  
It has an appealing feature, which is the rule's extension. The generalization of the laws to include every option is the extension. It is a local technique that looks for information near the query. The simplest way to find the smallest generalization of a rule is to exclude those antecedents that are not met by the query [22]. The pseudo-code for a single rule, r, is shown in Algorithm 1 below, and the flowchart of FURIA is presented in Figure 2.  
```
Let \(A\) be the set of numeric antecedents of \(r\)  While \(A\ \
eq\ \varnothing\) do \(a_{max}\ \leftarrow\ null\ (a_{max}\) denotes the antecedent with the highest purity ) \(pur_{max}\ \leftarrow\ 0\ (pur_{max}\) is the highest purity value, so far) \(for\ i\ \gets\ 0\) to size \((A)\) do  Compute the best fuzzification of \(Al[i]\) in terms of purity \(pur_{A[i]}\ \leftarrow\ to\ the\ purity\ of\ this\ best\ fuzzification\) \(if\ pur_{A[i]}>pur_{max}\ then \(pur_{max}\ \leftarrow\ pur_{A[i]}\) \(a_{max}\ \leftarrow\ A[i]\)  end end \(A\ \leftarrow\ A\backslash a_{max}\ Update\ r\ with\ a_{max}\) end
```  
**Algorithm 1**Generation of single ruler [22].