esa-sceva/satcom-chunk-collection huggingface dataset:
# Lateral Loading of a Rock-Socketed Pile Using the Strain Wedge Model Based on Hoek-Brown Criterion
## 4 Analysis the Lateral Bearing Capacity of Rock-Socketed Piles Based on Strain Wedge Model
### Relationship between Pile Side Shear Stress \(\tau\) and Pile Displacement \(y\)  
[PERSON] et al. [23; 24] used the bilinear shear model and the slip-line field theory of the non-gravity obtuse wedge under unilateral pressure to obtain the shear stress of the pile-rock interface during the slip and shear process. [PERSON] et al. [25] used artificial rocks to simulate soft rock and plexiglass to simulate pile foundations and to study the influence of pile-rock interface roughness on the vertical bearing characteristics of pile foundations. The above studies all believe that the roughness of the pile-rock interface and the rock strength have a significant impact on the vertical bearing capacity of the pile foundation.  
However, there are few studies on the relationship among the lateral friction of the pile, the interface roughness, and rock strength under lateral load.  
Figure 9: The relationship among the lateral stress change of materials with cohesion–friction, the stress level, and the friction angle.  
In this study, the hyperbolic method proposed by [PERSON] et al. [26] and [PERSON] et al. [27] was used to express the relationship between the lateral friction of the pile-rock interface and the lateral displacement of the pile foundation.  
\[f=\frac{w}{2.5D/E_{m}+w/f_{\max}} \tag{47}\]  
According to [PERSON] et al. [28], a method for calculating the maximum lateral friction resistance was proposed based on the full-scale test pile data of 6 soft rock-socketed piles.  
\[f_{\max}=0.8\sigma_{ci}(RF)^{0.45},\ RF=\frac{\overline{\Delta r}}{r_{s}} \times\frac{L_{t}}{L_{s}} \tag{48}\]  
where \(RF\) is the roughness factor (Figure 10); \(\overline{\Delta r}\) is the average height of asperities; \(r_{s}\) is nominal socket radius; \(L_{t}\) is total travel distance along the socket wall profile; \(L_{s}\) is nominal socket length.  
The expression of pile side friction resistance can be obtained. However, only half of the pile foundation is calculated:  
\[f=0.5*\frac{y}{2.5D/E_{m}+y/0.8\sigma_{ci}(RF)^{0.45}} \tag{49}\]
## 3 Results
### Identification of RINX2 Transcription Factor Binding Sites within MDM2 Gene  
In silico examination of MDM2's promoter region predicted a total of six RUNX2 TFBS with core similarity scores ranging from 0.894 to 1 and matrix similarity scores of approximately 0.9. A summary of TFBS start and end positions (relative to transcription start site), along with respective sequences is shown in Table 7. Five out of six of the predicted TFBS sequences possessed the RUNX2 TFBS consensus sequence (AACCACAN) (Figure 8D and Table 7).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Factor Name** & \multicolumn{5}{c}{**RUNX2**} \\ \hline
**TFBS \({}^{*}\)** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
**Core similarity score** & 1.00 & 1.00 & 0.895 & 1.00 & 1.00 & 1.00 \\ \hline
**Matrix similarity score** & 0.998 & 0.918 & 0.906 & 0.894 & 0.893 & 0.913 \\ \hline
**Start position \({}^{**}\)** & 68799245 & 6879934 & 68806888 & 68807452 & 68808063 & 68809252 \\ \hline
**End position \({}^{**}\)** & 68799254 & 68799943 & 68806897 & 68807461 & 68808072 & 68809261 \\ \hline
**Sequence** & CAACCACAAG & GAACCACTTA & AAGCCACATA & CCACCACC & GAGCTGGTC & TAACCACCTC \\ \hline
**Reference** & ArrayExpress [47] & ArrayExpress [47] & GEO [48] & GEO [49] & ArrayExpress [47] & ArrayExpress [47] \\ \hline \hline \end{tabular}  
* Transcription factor binding sites; \({}^{**}\) Relative to transcription start site.  
\end{table}
Table 7: RUNX2 transcription factor binding sites within MDM2 promoter.  
Figure 8: _S. triloba_ acetone extract and its combination with paclitaxel affect the steady-state mRNA expression of both U2 OS and SKOV3 cells. The steady-state mRNA expression of genes involved in cell proliferation and migration was examined using qPCR after cells were treated with _S. triloba_ acetone extract (IC\({}_{50}\)) for 48 h. (**A**) Significant decreases in U2 OS gene expression were observed for _RUNX2, PI3 KR1_, _N-cadherin_ and _vimentin_ genes, while _SETD7_ and _MDM2_ were significantly upregulated. The greatest increase (approx. 2 fold) in expression was observed for both _SETD7_ and _MDM2_ genes while _RUNX2_ exhibited the highest decline (91%) in expression. (**B**) Statistically significant declines in gene expression were observed for _RUNX2, BAX, PTEN, SETD7, MDM2_, and \(\beta\)-catenin_ in SKOV3 cells; none of the tested genes were upregulated after _S. triloba_ treatment. (**C**) When SKOV3 cells were treated with the combination treatment (19 \(\mu\)g/mL _S. triloba_ + 0.787 \(\mu\)g/mL paclitaxel), _RUNX2_ and \(\beta\)-catenin_ expression was significantly downregulated, while _p5_3 expression was significantly upregulated; _p5_3 exhibited notable upregulation with an expression fold change of approx. 8. Results are a representation of three independent experiments (\({}^{**}\)_p_\(\leq\) 0.001, \({}^{**}\)_p_\(\leq\) 0.01, \({}^{**}\)_p_\(\leq\) 0.05). (**D**) Consensus sequence of RUNX2 transcription factor binding site (TFBS) within the MDM2 promoter region obtained from the TRANSFAC\({}^{\text{\textregistered}}\) database.
## 2 Design and Methodology
### Structure Design  
The structural design and mechanical stress-induced voltage measurements are all performed in COMSOL Multiphysics. The design structure has a length of 50 cm, a width of 30 cm, and a thickness of 3 cm where 6 piezoelectric sensors are distributed equidistantly all over the structure as follows: 3 sensors to the right side of the structure and 3 sensors to the left (Figure 1). The structure and the sensors are made of wood and Lead Zirconate Titanate (PZT-5H) (piezoelectric material), respectively. The sensor design is brought from a commercial sensor of dimensions 3.18 cm length, 1.27 cm width, and 0.51 mm thickness. A rectangular shape of 2 cm length, and 2.5 cm width is defined on the surface of the structure to simulate the surface contact of the load to be applied. Thus, the 6 sensors measure the potential difference (voltage) for each change in the load magnitude and position.  
Two physics interfaces have been defined through COMSOL study, solid mechanics, and electrostatics. A multiphysics node is added to couple the two interfaces and studies the piezoelectric effect.  
Solving the equations of motion and constructing a parametric model of a solid material are the foundations of solid mechanics interface. Results such as displacement, stress, and strain are computed. In this interface, the two external boundaries of the structure along the x-axis are defined as fixed constraints, and the rectangular shape on the structure surface is defined as boundary load where a gravitational force towards the negative z-axis is applied \(F=mg\) and stress can be deducted as follows (Equation (3)):  
\[T=\frac{F}{A} \tag{1}\]  
where \(T\) and \(A\) are the stress and the structure surface area, respectively.  
The electrostatics interface is computing the electric displacement field and potential distributions in dielectrics under conditions. In this interface, the domains identifying the sensors are defined as charge conservation. The relative permittivity value from the \"piezoelectric material node\" in the solid mechanics interface is substituted into the \"charge conservation node\" in the electrostatics interface using the Multiphysics node. Piezoelectric effect is a term used to describe the change in electric polarization that occurs in piezoelectric sensor material under mechanical stress. Because of this stress-induced polarization shift, an apparent potential difference can be observed throughout the material (Equations (2) and (3)).  
\[T=C_{E}S-e^{T}E \tag{2}\]  
\[D=eS+\varepsilon_{S}E \tag{3}\]  
where \(T\), \(S\), \(D\) and \(E\) are the stress, strain, electric displacement, and electric field, respectively. The material properties \(C_{E}\), \(e\) and \(\varepsilon_{S}\) correspond to the elasticity, coupling, and permittivity matrices.  
Figure 1: Structure design embedding piezoelectric sensors and load pressure.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 4 Results and Discussion
### Sea State Comparisons
#### 4.2.2 Heaving Motion  
Figure 12a-e displays the speed polar diagrams of the heaving motion by combining sailing speeds with wave heading angles under different sea states (1-5). The results revealed that when the sea state worsened, the heaving motion grew substantially for wave heading angles between 90\({}^{\circ}\) and 180\({}^{\circ}\). This signified that for all sailing speeds, when wave heading angles ranged from beam to head seas, heaving motion increased as the sea state worsened.  
The figures indicate that when the Froude number was small the heaving motion was strongest at a wave heading angle of 90\({}^{\circ}\). When the Froude number was large or in the middle range, the strongest heaving motion was produced at the wave heading angles of 135\({}^{\circ}\) and 180\({}^{\circ}\). These results suggested that at low sailing speeds, heaving motion was strongly affected by beam waves and that at medium and high sailing speeds, heaving motion was strongly affected by bow and head seas.  
Figure 11: Speed polar diagrams of swaying motion in combination with sailing speeds and wave heading angles under sea states of (**a**) 1, (**b**) 2, (**c**) 3, (**d**) 4, and (**e**) 5, respectively.
## 1 Introduction
Crack detection continues to evolve due to the abundance and accessibility of training data, which enhances performance in deep learning models. However, comprehensive structural safety assessment requires consideration of a wider variety of damage types. Accordingly, this study aims to detect not only line-type cracks but also surface-type damage such as concrete scaling/spalling, exposed rebar, water leakage, and paint peeling (damage or detachment of paint coating layers applied to steel components). [PERSON] [14] proposed a multi-class damage detection method suitable for field conditions using U-Net, comparing its performance with Light Head-RCNN, FPN, and PSP-Net for detecting cracks, concrete scaling/spalling, water leakage, and exposed rebar in railway bridges. In related research, [PERSON] [15] also proposed a crack detection method usingGabor filter banks applied to line-scan camera images for detecting damage in railway concrete slabs. In the road infrastructure domain, [PERSON] [16] presented a novel algorithm for automated crack detection in 2D pavement images, while [PERSON] [17] proposed the ICGA method, which enhances U-Net with dual attention gates and improves generator segmentation capacity in pix2 pix for road surface crack detection. [PERSON] and [PERSON] [18] developed a road crack detection approach based on deep dictionary learning and encoding networks (DDLCNs), introducing a new activation function (MeLU) and a novel differentiable computation method. Numerous studies have shown that deep neural networks significantly outperform traditional pattern recognition methods in learning-based damage detection. Therefore, the goal of this study is to develop a deep-learning-based detection system capable of identifying multiple damage classes on structural surfaces, enabling a more objective and reliable alternative to conventional visual inspections and facilitating practical deployment in industrial field applications.
## 4 Experimental Demonstration
### Result
#### 4.4.3 Experiment III: Noise Immunity Experiment of the Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Accuracy \%** & **1 DCNN+LSTM1 DCNN+GRU** & **1 DCNN+BiGRU** & **ACN\_BM** \\ \hline \multirow{3}{*}{HUST} & Training-set & 87.7 \(\pm\) 0.23 & 97.68 \(\pm\) 0.14 & 98.60 \(\pm\) 0.09 & 99.89 \(\pm\) 0.06 \\ \cline{2-6}  & test-set & 86.76 \(\pm\) 0.35 & 96.28 \(\pm\) 0.36 & 96.13 \(\pm\) 0.21 & 98.78 \(\pm\) 0.15 \\ \hline \multirow{3}{*}{CWRU} & Training-set & 86.87 \(\pm\) 0.33 & 97.96 \(\pm\) 0.26 & 96.24 \(\pm\) 0.11 & 97.82 \(\pm\) 0.21 \\ \cline{2-6}  & test-set & 86.64 \(\pm\) 0.29 & 97.38 \(\pm\) 0.49 & 94.57 \(\pm\) 0.57 & 98.64 \(\pm\) 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Noise immunity experimental data. Figure 12: (a) The noisy data. (b) The classified data. noise conditions, the multi-head self-attention mechanism outperforms its single-head counterpart due to its capacity to learn a broader range of representations. With an increase in the number of heads, there is a slight but incremental improvement in the test set's prediction accuracy at an SNR of 0. However, at an SNR of \(-8\), the rise in the number of heads leads to a substantial increase in prediction accuracy. However, the accuracy gain from expanding from four to eight heads is marginal. Despite the unchanged volume of parameters, we opted to set the number of heads to four, taking into account the computational overhead associated with the parallel processing of multiple heads.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 4 Experimental Results and Discussion
### Sea Area Characteristic Parameters  
The currently known environmental parameters are as follows: the depth of the sound source was 141 m, the distance between the sound source and the receiving array was 274-340 km, and 200 sampling points were taken at equal intervals in this range. The signal frequency was 360 Hz, the sea depth was 2000 m, the sedimentary layer thickness was 50 m, the sea bottom was sand-mud-clay, that is, the sedimentary sound velocity was 1579 m/s, the sedimentary density was 1.596 g/cm\({}^{3}\), and the sedimentary attenuation was 0.113 dB/m-kHz. We placed a CTD at the receiving array to measure the sound velocity profile. The measured sound velocity profile is shown in Figure 10.  
Figure 9: Low-Frequency acquisition and ranging (LOFAR) spectrum.  
Figure 10 shows that it is a typical deep-sea sound speed profile curve. It can be seen that below 1000 m, the temperature distribution was mainly affected by sunlight, which presents a relatively stable negative gradient. Above 1000 m, the seawater temperature is constant, which is called the deep-sea isothermal layer. The sound velocity distribution of this layer mainly depends on the increase in static pressure, presenting an almost linear positive gradient (0.16 m/s/m) distribution.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
###### Abstract  
This paper presents the first successful drone detection results using a 5G network as a source of illumination in a passive radar system. Furthermore, a novel adaptive strategy for signal integration is shown. The proposed approach is based on the Renyi entropy. It allows one to select time frames with a densely allocated downlink channel both in the time and frequency domains. The resource allocation is strongly related to a network load and has a crucial influence on 5G-based passive radar range resolution and detection capabilities. The proposed technique was validated using simulated and real-life signals, confirming the possibility of detecting unmanned aerial vehicles (UAVs) in 5G-network-based passive radars. Moreover, the proposed methodology can be directly used in passive radar systems where the illuminating signal duration and bandwidth are content-dependent, and the radar resolution may vary significantly.  
passive bistatic radar (PBR); 5G; passive coherent location (PCL); entropy; UAV 1  
[PERSON]; [PERSON]; [PERSON] Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection. _Remote Sens._**2022**, 14, 6146. [[https://doi.org/10.3390/rs14236146](https://doi.org/10.3390/rs14236146)]([https://doi.org/10.3390/rs14236146](https://doi.org/10.3390/rs14236146))
## 3 ABA and Drought Tolerance: From Stomata Closure to the Modulation of Plant Metabolism
### Primary and Secondary Metabolic Pathways Regulated by ABA  
ABA signaling is involved in transcriptional and post-transcriptional regulation of genes encoded for enzymes related to carbohydrate metabolism (Table 1) [96; 97; 98]. ABA can regulate sucrose metabolism through the induction of vacuolar invertase activity and the up-regulation of vacuolar invertase gene expression [97; 99]. Furthermore, [PERSON] et al. [100] showed that ABA promotes leaf starch degradation under osmotic stress by the synergistic action of \(\beta\)-AMYLASE1 (BAM1) and \(\alpha\)-AMYLASE3 (AMY3). Both mechanisms lead to the release of hexoses (i.e., fructose, glucose, and maltose) that function as compatible solutes, preserving membrane integrity and maintaining leaf turgor [101]. The accumulation of soluble carbohydrates concomitantly with the up-regulation of ABA biosynthesis in stem of _Populus nigra_ exposed to drought has been recently observed [38; 102]. Stem soluble carbohydrates may help to preserve xylem hydraulic integrity and act as a buffer when photoassimilation decreases under drought, thus ensuring an adequate energy source for stress defense and viable seed production [103; 104]. In addition, ABA has been demonstrated to regulate foliar proline accumulation under osmotic stress (Table 1), although a direct action of ABA on the transcriptional regulation of the key genes and enzymes involved in proline biosynthesis is still unclear [105; 106; 107; 108; 109]. Similar to other compatible osmolytes, significant levels of proline can accumulate in the cytoplasm and organelles allowing osmotic adjustment, stabilizing protein structure and cell membranes, and acting as a free radical scavenger [110]. Furthermore, proline may provide regulation of cytosolic acidity and act as a signaling molecule able to activate further stress responses [111].  
Regarding the regulation of secondary metabolites under water deficit, several studies have shown the impact of exogenous application of ABA on flavonoid biosynthetic pathway (Table 1) [59; 112; 113; 114]. In addition, many key genes of the flavonoid biosynthetic pathway were demonstrated to be up-regulated both in fruits and leaves in parallel to increases in the ABA content during drought [115; 116; 117]. However, whether these genes are activated by ABA or by the stress conditions remains under debate. The possible relationship between ABA and flavonoid could be important due to the ability of flavonoids to modulate the reactive oxygen species-signaling cascade and to buffer stress-induced alterations in metabolic homeostasis [118]. Future studies focusing on ABA-flavonoid interaction utilizing ABA-deficient mutants or any additional tools to manipulate the endogenous levels of ABA are necessary in order to confirm the role of ABA on flavonoid biosynthesis both at the transcriptional and post-transcriptional levels.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 5 Opportunities for Further Research  
### Collection of Training Data  
It has been noted that obtaining enough training data is crucial for the accuracy and generalization of the machine-learning-based model. Considering the cost of carrying out measurement campaigns, the question is how many samples are enough for a given prediction accuracy. Evaluation metrics and tools need to be developed for judgment.  
Meanwhile, what we need may not be \"bigger data\", but \"better data\". The diversity and uniformity of the samples should be considered. In a single scenario, the data should be evenly distributed in the measured region. To build a model with good generalization property, measurement routes should be carefully designed to acquire enough data in different scenarios. Therefore, the methodology of channel measurement should be carefully considered.  
Additionally, we have offered two schemes to make the most use of existing measured data and classical models. Similar methods can also be investigated to enlarge the training dataset in the future.
## 2 Materials and Methods
### Materials Properties and Selection
#### 2.1.3 Ti6 Al4V  
Ti6 Al4V is a versatile titanium alloy used extensively for medical, aerospace, and automotive applications. Its desirability is in its lightweight combination with high-yield strength, which is usually not so for other metals. However, this property is improved through the addition of other materials, which adds to its hardness, hence the consideration of the alloy with Aluminum and Vanadium to form Ti6 Al4V.
# Crystallization Kinetics of Sr-Modified Precipitation Hardening Al-Si-Cu Alloys
## 4 Conclusions  
Sr additions refine the eutectic silicon phase in Al-Si alloys, transforming it from a coarse to a more desirable fibrous or lamellar structure. This refinement improves the alloy's mechanical properties. Increasing the Sr content in the investigated alloys leads to improved mechanical properties by reducing porosity (Table 7) and refining the silicon structure. The addition of strontium induces a prolongation of the solidification time and changes the crystallization temperature of the silicon eutectic and copper phases while increasing the subcooling. The strontium addition causes the crystallization of Al\({}_{2}\)Si\({}_{2}\)Sr phases before the nucleation of \(\alpha\)-phase dendrites begins. Crystallization of the secondary Al\({}_{2}\)Si\({}_{2}\)Sr phase occurs at 683 \({}^{\circ}\)C.  
The modification of the Al-Si-Cu alloy with strontium causes a change in the derivative curve in the area of endothermic crystallization of the eutectic \(\alpha+\beta\), which is confirmed by structural studies and a change in the morphology of the eutectic silicon. The added strontium in the form of 10% Al-Sr mortar dissolves in the solution and crystallizes in the form of secondary Al\({}_{2}\)Si\({}_{2}\)Sr phases, which is visible on the derivative curve.  
Calculation of the value of the derivation curve and determination of the calorimetric curve made it possible to calculate the latent heat of crystallization and to calculate the share of the solid fraction. The summed area between the derivation curve and the calorimetric curve indicates that the addition of Sr causes a change in the solid fraction curve in the eutectic \(\alpha+\beta\) solidification range.  
In future studies, we plan to determine the optimum combination of the Al-Si-Cu alloy with the addition of Sr. For future perspectives, we want to focus on the effect of the phase size and shape of the precipitates on the physical properties of the alloy.  
Conceptualization, M.K. and M.G.; methodology, W.P. and M.K.; software, L.W.; validation, K.L.; formal analysis, L.W.; investigation, W.P. and M.G.; resources, M.K.; data curation, M.G.; writing--M.K., W.P. and M.G.; writing--K.L. and L.W.; visualization, M.G.; supervision, M.K.; project administration, K.L. and L.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 10: Wear resistance results of the investigated alloys after the strontium addition.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Plasma System S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.