esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
## 2 Stress Analysis of Surrounding Rock
### Stress Solution of the Surrounding Rock
In addition, the \(a_{n}\), \(b_{n}\) are calculated as follows: \(a_{6}=0.14p(\lambda+1)\), \(a_{5}=-0.17p(\lambda+1)\), \(a_{4}=-(0.245\lambda+0.50)p\), \(a_{3}=(1.109\lambda+1.404)p\), \(a_{2}=-(1.325\lambda+0.636)p\), \(a_{1}=(-10.436\lambda+12.636)p\), \(a_{7}=a_{8}=\cdots=a_{n}=0\), \(b_{1}=-(11.135\lambda+15.535)p\), \(b_{2}=(0.888\lambda+0.033)p\), \(b_{3}=(-9.705\lambda+11.837)p\), \(\cdots\), \(b_{99}=(1.608\times 10^{-5}\lambda-3.649\times 10^{-6})p\), \(b_{100}=(-7.402\times 10^{-6}\lambda+1.510\times 10^{-5})p\). \[\left\{ \begin{array}{l}\varphi_{2}(\zeta)=p[6.325p(\lambda+1)\zeta-0.578p( \lambda+1)+\frac{p(-11.036\lambda+12.036)}{\zeta}-\frac{p(0.853\lambda+0.163)} {\zeta^{2}}+\\ \frac{p(0.499\lambda+0.794)}{\zeta^{3}}-\frac{p(0.065\lambda+0.320)}{\zeta^{4 }}-\frac{0.085p(\lambda+1)}{\zeta^{5}}+\frac{0.07p(\lambda+1)}{\zeta^{6}}]\\ \psi_{2}(\zeta)=p[12.65p(\lambda-1)\zeta-1.155p(\lambda-1)-\frac{p(1.2335 \lambda+14.335)}{\zeta}+\frac{p(1.833\lambda-0.912)}{\zeta^{2}}+\\ \frac{p(-10.925\lambda+10.617)}{\zeta^{2}}+\ldots+\frac{p(1.608\times 10^{-5} \lambda-3.649\times 10^{-6})}{\zeta^{99}}+\frac{p(-7.402\times 10^{-6}\lambda+1.510\times 10^{-5})}{ \zeta^{100}}]\end{array} \right. \tag{24}\]  
The results for a three-centered arch with a rise-span ratio of \(f/B=1/4\) and an aspect ratio of \(B/H=1.33\), \(D_{6}\), \(D_{5}\), \(D_{4}\), \(D_{3}\), \(D_{2}\), \(D_{1}\), \(D_{0}\) and \(d_{k}\) (\(k\) = 1, 2, 3, \(\cdots\), \(\infty\)) are as follows: \(D_{6}=-0.00861\), \(D_{5}=0.01926\), \(D_{4}=0.0279\), \(D_{3}=-0.10827\), \(D_{2}=0.05479\), \(D_{1}=-0.13477\), \(D_{0}=-0.099\), \(d_{1}=1.0649\), \(d_{2}=-0.02708\),..., \(d_{100}=-4.935\times 10^{-7}\). The \(a_{n}\) and \(b_{n}\) are calculated as follows: \(a_{6}=0.105p(\lambda+1)\), \(a_{5}=-0.235p(\lambda+1)\), \(a_{4}=-(0.244\lambda+0.434)p\), \(a_{3}=(1.111\lambda\[\left\{ \begin{array}{l}\varphi_{3}(\zeta)=p[6.10p(\lambda+1)\zeta-0.438p(\lambda+1 )+\frac{p(-10.272\lambda+11.803)}{\zeta}-\frac{p(0.658\lambda+0.029)}{\zeta^{2}} +\\ \frac{p(0.47\lambda+0.888)}{\zeta^{3}}-\frac{p(0.082\lambda-0.272)}{\zeta^{4}} -\frac{0.1175p(\lambda+1)}{\zeta^{5}}+\frac{0.0525p(\lambda+1)}{\zeta^{6}}]\\ \psi_{3}(\zeta)=p[12.20p(\lambda-1)\zeta-0.875p(\lambda-1)-\frac{p(11.46\lambda +14.523)}{\zeta}+\frac{p(1.463\lambda-0.742)}{\zeta^{2}}+\\ \frac{p(-10.148\lambda+10.476)}{\zeta^{2}}+\ldots+\frac{p(-4.332\times 10^{-6} \lambda+1.319\times 10^{-5})}{\zeta^{99}}+\frac{p(9.556\times 10^{-6} \lambda+1.997\times 10^{-6})}{\zeta^{100}}]\end{array} \right. \tag{25}\]
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### GPT-2 Model for Contextual Word Embeddings (CWE)  
The generative pre-trained model-2 was developed by GoogleAI in February 2019 [99]. The model is recognized as one of the deep neural language models, which uses fine-tuning and pre-training for the majority of NLP downstream tasks. Contextualized word representations are internal word representations. These representations are used as a function for the whole input sentence. Similar to the BERT model, GPT-2 is a uni-directional as well as a bi-directional language-based transformer model. For each input sentence, the GPT-2 utilizes the 12 layers as part of the tokenization [100]. In comparison to the ELMO, the GPT-2 uses multi-encoder transformer layers to capture the features from sentences; however, the ELMO uses Bi-LSTM architecture to encode the sentences. Another key difference is that the ELMO utilizes an unsupervised approach for the selection of features from the text, while the GPT-2 uses a fine-tune approach for all end-tasks. The objective of the GPT-2 is quite similar to that of the BERT model: to predict the word from a set of all possible words based on context. The model is context-dependent, as is the case with other transformer models. [PERSON] et al. [101] compared the performance of the BERT, ELMO, and GPT-2 models for the purpose of contextualized word embeddings.  
They further dictated that the representations of the GPT-2 model in comparison with the BERT and ELMO are more context-specific. Furthermore, [PERSON] et al. [102] provided a brief history of pre-trained transformer models (PTMs). Models like the BERT, ELMO, and GPT-2 develop a rich context in terms of efficiency and predicting various interpretations. [PERSON] et al. [103] proposed the GPT-2 to identify a Portuguese biomedical text. They used a fine-tuning approach for transfer learning; however, they manually annotated the public dataset for classification tasks. Unlike the BERT model, the GPT-2 is based on stacked decoder blocks, whereas the BERT uses encoder blocks. The GPT-2 receives the input information in the form of word vectors and then finalizes the prediction probability for the next words. However, the BERT model is not autoregressive. It takes the entire context all at once. As for the attention mechanism, the BERT and ELMO use a self-attention mechanism for contextualization, but the GPT-2 utilizes the masked attention mechanism for next-word predictions [104; 105; 106].
# Time-Dependency in the Corrosion Process of Reinforced Concrete Subjected to a Chloride Solution
## 2 Reviews for Corrosion Detection Techniques
### Galvanostatic Pulse Technique  
The GP technique is a polarisation transient technique that uses the time domain with a potentiostat in the galvanostatic mode. This technique involves applying a small constant current ranging from 10-100 \(\upmu\)A to the working electrode. The potential perturbation of the working electrode with respect to the corrosion potential (vs. the reference electrode) is recorded over a short duration (<10 s) [12]. A previous study suggested that a small perturbation of less than 100 \(\upmu\)C/cm\({}^{2}\) should be applied to preserve the system between concrete and steel and eliminate the diffusion process [13]. For data analysis, a numerical procedure based on an electrical circuit, such as the Randle circuit, is typically employed. Owing to the non-exponential relaxation in the parameters, a capacitor in the Randle circuit is replaced with a constant phase element (CPE) representative of a non-ideal capacitor [14], or the time-domain equation for a Randle circuit is modified with the Kohlrausch-Williams-Watt (KWW) function [15], as shown in Equation (1). The potential perturbation during electrode charging can be expressed as follows:  
\[V_{t}(t)=I_{app}R_{oblmic}-I_{app}R_{p}\bigg{[}1-\exp\bigg{(}\frac{-t}{\tau} \bigg{)}^{\beta}\bigg{]}, \tag{1}\]  
where \(V_{t}(t)\) is the potential of steel with measuring time \(t\), \(I_{app}\) is the applied current, \(R_{p}\) is the polarisation resistance, \(R_{oblmic}\) is the ohmic resistance, \(\tau\) is the time constant (\(=R_{p}C_{dl}\)), \(C_{dl}\) is the double-layer capacitance, and \(\beta\) is the exponent.  
Two different methods have been proposed for the fitting procedure: linearisation and exponential curves [16]. The ohmic resistance is estimated with a sudden change in the corrosion potential when a constant current is applied (at \(t\approx 0\) s). Figure 1 shows an example of the measured data and fitting for the active and passive states of the steel. This technique is beneficial to engineers owing to its rapid response and the direct elimination of ohmic resistance. In addition, because the time-domain response is transformed into the frequency domain using Laplace transformation [17; 18], the results based on the GP technique are complementary to that of the EIS technique. Commercial products, such as GalvaPulse, have also been developed as portable equipment for practical use. However, common limitations in corrosion measurements remain, such as the estimation of the polarised area [19]. This technique requires controlling the potential perturbation because steel is polarised by current rather than voltage [20].
## 6 Biomarker Detection  
Biomarkers are cell components that are highly expressed in body fluids or tumor cells at the beginning of illness. In the context of OC, the biological cells exhibited in blood or saliva act as cancer-detection indicators. Specifically, the progression of cancer consists of three stages: beginning, promotion, and advancement. All three steps are associated with substantial modifications in the cell's processes, including metabolome, transcriptome, and proteome processes [56]. These changes occur as a result of mutations in certain genes or proteins, which result in the modification or stopping of important metabolic and structural pathways. Some of the most promising biomarkers related to the progression of OC are Ki-67 antigen, TSG p16, TSG p53, and DNA ploidy. Early identification of OSCC is aided by salivary biomarkers such as circulating tumor DNA, miRNAs, and extracellular vesicles [57]. In OSCC, p53 expression is regarded as a key indicator of carcinogenesis. The advent of the p53 mutant gene is confirmed by using immunohistochemical staining [58]. Apart from that, many advanced techniques and high-throughput molecular methodologies are used to detect the biomarkers (Figure 1 and Table 2). Figure 1: Various molecular methods involved in the detection of biomarkers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SI. No.** & **Biomarker** & **Sample Type** & **Technique Used** \\ \hline \multicolumn{4}{c}{**Protein biomarkers**} \\ \hline \hline
1 & Collagen & & \\ \hline
2 & Elastin & & \\ \hline
3 & Keratin & Oral tissue & Diffuse reflectance spectroscopy \\ \hline
4 & FAD & & \\ \hline
5 & NADH & & \\ \hline
6 & Cytokin-19 & Oral tissue & Incisional biopsy \\ \hline
7 & Secretory leukocyte protease inhibitor & Oral cells & Brush biopsy \\ \hline
8 & Epidermal growth factor receptor (EGFR) & Oral epithelial cell linings & Exfoliative biopsy \\ \hline
9 & Serum proteins & Serum & HPLC \\ \hline
10 & Proteases & & \\ \hline
11 & ANXA2 & & \\ \hline
12 & CA2 & & \\ \hline
13 & CD44 & & \\ \hline
14 & CRNN & & \\ \hline
15 & CST3 & & \\ \hline
16 & CSTA & & \\ \hline
17 & DSG3 & & \\ \hline
18 & FLNA & & \\ \hline
19 & FSCN1 & & \\ \hline
20 & GANAB & & \\ \hline
21 & GSTP1 & & \\ \hline
22 & HMGCS1 & & \\ \hline
23 & HSPA5 & & \\ \hline
24 & IGFBP3 & & \\ \hline
25 & ISG15 & & \\ \hline
26 & KNG1 & & \\ \hline
27 & LDHA & & \\ \hline
28 & LGALS3 BP & & \\ \hline
29 & MMP1 & & \\ \hline
30 & MMP3 & & \\ \hline
31 & MMP9 & & \\ \hline
32 & PRDX2 & & \\ \hline
33 & S100A9 & & \\ \hline
34 & SPARC & & \\ \hline
35 & STAT1 & & \\ \hline
36 & TIMP1 & & \\ \hline
37 & TYMP & & \\ \hline
38 & YWHAB & & \\ \hline
39 & Interleukin-6 (IL-6) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Biomarkers associated with the data source and technologies used to identify oral cancer and OSCC.
# Comparison of Image Normalization Methods for Multi-Site Deep Learning
## 4 Discussion
### Limitations  
In this study, only two networks were tested for their performance using different normalization methods; while U-Net and ResNet are widely used, there are many other architectures. For segmentation, most follow the encoder-decoder structure and should behave similarly.  
When predicting the Dworak score, there are not sufficient data to achieve a performance which would be sufficient to be used for clinical decision making. The best method achieved an AUC of \(0.69\pm 0.01\). This is not sufficient to inform treatment decisions. Even in studies with a larger training cohort of 592 patients, only an AUC of 0.82 was achieved [27].  
One of the issues is that only about 20% of patients achieve a pathological complete response [2]. In the data set, there were only 37 patients with a pathological complete response. Therefore, to obtain clinically usable networks, the training data set would probably have to be larger by orders of magnitude. Collecting that much data faces many regulatory and technical challenges.  
For segmentation, this is less of a problem because there are a lot more data points. There are approximately ten million voxels in the tumor class. Although these are not independent samples, there are still much more data available than for the classification. The maximum Dice of \(0.69\pm 0.01\) is in the range of the interobserver Dice of \(0.71\pm 0.13\) observed in [26] and \(0.83\pm 0.13\) in [4]. The performance could probably be further increased with more parameter tuning.  
The difference in available data for the different tasks is probably also the reason why there are much larger differences in performance using different normalization methods for classification and regression than for segmentation.
# Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models
## 3 Performance Metrics of V2X Technologies
### Data Rate
#### 3.3.1 DR for 802.11p Protocol (DR\({}^{11p}\)) and 802.11 bd (DR\({}^{11 bd}\))  
DR\({}^{11p}\) is determined by the number of bits transmitted, for a particular payload size (\(P_{b}\)), in the time defined by the latency, \(L^{11p}\), as determined by Equation (3), such as  
\[DR^{11p}=\frac{P_{b}.8}{L^{11p}} \tag{10}\]  
The DR for 802.11 bd (DR\({}^{11 bd}\)) can be obtained using Equation (10) but substituting L\({}^{11p}\) for L\({}^{11 bd}\).
###### Abstract  
This study thoroughly investigates the influence of anisotropy on the formability of 2024-T3 aluminum alloy sheets using advanced techniques such as digital image correlation (DIC) and Marciniak tests. A key finding is the relatively small variation in anisotropy values across different strain paths and orientations, contrasting with more significant variations reported in other studies. Tests were conducted on nine samples with various geometries to induce specific strain paths, including uniaxial, plane, and balanced biaxial strains, oriented in different directions relative to the rolling direction. The study also provides a detailed analysis of microstructural and mechanical characteristics, such as precipitate distribution and anisotropy behavior, which are crucial for understanding the relationship between microstructure and material formability. The results show that while anisotropy impacts deformation capacity, the differences in formability among the directions were minimal, with slightly greater formability observed in the diagonal direction. These findings are compared with forming limit curves (FLCs), offering an integrated view of how relatively uniform anisotropic properties influence formability. These insights are essential for optimizing the processing and application of 2024-T3 alloy in industrial contexts, emphasizing the importance of understanding anisotropy in the design of metal components.  
2024-T3 aluminum; formability; anisotropy; microstructural and mechanical properties Article  
[PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Evaluating the Plastic Anisotropic Effect on the Forming Limit Curve of 2024-T3 Aluminum Alloy Sheets Using Marciniak Tests and Digital Image Correlation  
1 Escuela de Ingenieria Mecanica, Pontificia Universidad Catolica de Valparaiso, Av. Los Carrera 01567, Quilpae 2430000, Chile; sergio.nunnez@pucvd (S.N.S.); jose.valin@pucvd (J.L.V.)  
1 Scuela de Ingenieria Mecanica, Pontificia Universidad Catolica de Valparaiso, Av. Los Carrera 01567, Quilpae 2430000, Chile; sergio.nunnez@pucvd (S.N.S.); jose.valin@pucvd (J.L.V.)  
3 Metlurgical Engineering Department, University of Santiago, Santiago 8320000, Chile; [EMAIL_ADDRESS]  
4 Programs, Research and Development Directorate, Chilean Navy, Valparaiso 2360035, Chile  
5 Department of Mechanical Engineering (DIM), Faculty of Engineering (FI), University of Concepcion, Concepcion 4030000, Chile; [EMAIL_ADDRESS]  
*Correspondence: roberto.iquilio@pucvd
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### Future Research in AloT-Enhanced Aviation Health Monitoring
Understanding how to best integrate AI recommendations into human decision-making processes will be key to maximizing the benefits of these technologies. The adoption of AIoT in aviation will raise regulatory and ethical questions. Research should address the development of regulatory frameworks that ensure safety and fairness while promoting innovation. Ethical considerations, such as the impact of AI on jobs and the potential for algorithmic bias, must also be carefully examined. Digital twins, which are virtual replicas of physical systems, offer immense potential for predictive maintenance and operational optimization. Future research should focus on developing high-fidelity digital twins that accurately simulate the behavior of aircraft systems under various conditions. These simulations can provide valuable insights into system performance and potential failure points. Integrating digital twins with AI models can enhance predictive capabilities. Research should explore how to effectively combine these technologies to provide real-time insights and optimize maintenance strategies. This includes developing algorithms that can continuously update digital twins based on real-time data and AI predictions. As the aviation industry continues to embrace above mentioned innovations, ongoing research will be essential to fully realize the potential of AIoT and 6G in transforming aviation health monitoring.
## 3 Experimental Results
### Analysis of Radiation Noise Characteristic  
#### 3.2.1 Sound Pressure Level at Different Flow Rates  
The sound pressure level is a physical quantity that describes the response of the human ear to the intensity of the sound. The sound pressure level of the radiation noise from the model pump is measured at two rotational speeds of 5875 and 6750 r/ min, respectively, with the range from 0.3 to 1.0 \(Q_{\text{d}}\). As can be seen from Figure 9, the sound pressure level curve increases slightly with the increase of the flow rate, then increases sharply after a small drop of the amplitude. Associated with the efficiency characteristic curves, the flow rate point that presents the minimum value of the sound pressure level corresponds to the highest efficiency area (0.7 \(Q_{\text{r}}\)). In general, the rotor-stator interaction, especially close to the volute tongue, may be considered a very significant energy dissipation mechanism and also the main fluid-borne noise source. When the pump is operating at a low flow rate, there is a clear wake-jet flow structure at the impeller outlet area. The greater extent in the velocity field fluctuation inside the blade channels would result in bigger fluid-borne noise. Increasing efficiency has a certain effect on reducing noise under this condition. When the flow rate exceeds the optimal working condition, the sound pressure level is increased greatly. On the one hand, this is due to the higher velocity level of the fluid in the pump at high flow rates, which causes a stronger rotor-stator interaction effect. Moreover, the coupling effect between the fluid and the structure is enhanced and the fluid load is also increasing, which could cause higher mechanical noise.
# Dictionary Encoding Based on Tagged Sentential Decision Diagrams
## 3 Tagged Sentential Decision Diagrams
### Syntax and Semantics  
TSDDs have different constraints compared to SDDs and ZSDDs based on the syntax and semantics of decision diagrams. Here, we give the following constraints.  
**Definition 3**.: _Let \(\mathbf{T}^{1}\) and \(\mathbf{T}^{2}\) be two vtrees s.t. \(\mathbf{T}^{2}\preccurlyeq\mathbf{T}^{1}\) and \((\mathbf{T}^{1},\mathbf{T}^{2},\alpha)\) is a TSDD. Then,_  
* _If_ \(\alpha\) _is a terminal node, then it must be one of the following:_
* \((0,0,\mathbf{0})\)_, and_ \(\langle(0,0,\mathbf{0})\rangle=\mathbf{0}\)_._
* \((\mathbf{T}^{1},0,\mathbf{1})\)_, and_ \(\langle(\mathbf{T}^{1},0,\mathbf{1})\rangle=\bigwedge\limits_{v(\mathbf{T}^{1 })}\overline{x}\) _(specifically, if_ \(\mathbf{T}^{1}=0\)_, then_ \(\langle(0,0,\mathbf{1})\rangle=\mathbf{1}\)_)._
* \((\mathbf{T}^{1},\mathbf{T}^{2},\overline{\varepsilon})\) _and_ \(\mathbf{T}^{2}\) _must be a leaf vtree node, and_ \(\langle(\mathbf{T}^{1},\mathbf{T}^{2},\overline{\varepsilon})\rangle=( \bigwedge\limits_{v(\mathbf{T}^{1})\setminus v(\mathbf{T}^{2})}\overline{x}) \wedge(\bigvee\limits_{v(\mathbf{T}^{2})}x)\)_._
* _If_ \(\alpha\) _is a decomposition node, then_ \(\mathbf{T}^{2}\) _must not be a leaf vtree node._  
We can see that for a TSDD \((\mathbf{T}^{1},\mathbf{T}^{2},\alpha)\), \(\mathbf{T}^{2}\) must be a leaf vtree node or \(0\) if \(\alpha\) is a terminal node. We should note that we can directly construct some TSDDs. (1) The TSDDs \((0,0,0)\) and \((0,0,1)\) can be constructed. (2) The TSDDs \((\mathbf{T},0,1)\) and \((\mathbf{T},\mathbf{T},\overline{\varepsilon})\), where \(\mathbf{T}\) is a leaf vtree node, can be constructed. These two kinds of TSDDs are special TSDDs; that is, they are the foundation for constructing a TSDD to represent a Boolean function. For the second type of TSDDs, we know that \(\mathbf{T}\) is a leaf vtree node; that is, \(v(\mathbf{T})\) contains only one variable. Suppose that \(v(\mathbf{T})=x\); then, \(\langle(\mathbf{T},0,1)\rangle=\overline{x}\) and \(\langle\mathbf{T},\mathbf{T},\epsilon\rangle=x\).