esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Differences between Solutions  
_V. parvula_ (\(p<0.001\); \(\eta^{2}=0.208\)) and _S. oralis_ (\(p<0.001\); \(\eta^{2}=0.675\)) showed a significant interaction between the agents and alloys, with a larger effect in _S. oralis_, as shown by two-factor ANOVA. _S. mutans_ and _A. actinomycetemcomitans_ did not show significant interactions. CHX DG produced the lowest inhibition of adhesion and Perio Plus produced the highest inhibition among all bacteria. CHX DG caused less adhesion inhibition than Perio Plus and Curasept (100%; \(p<0.001\)) on both alloys (66-89%) in _S. oralis_, with a larger effect size on NiTi than SS (\(\eta^{2}=0.939\) and 0.708), and no difference between the last two solutions. A smaller inhibition of adhesion on both alloys (44-59%) was shown by CHX DG compared to Perio Plus and Curasept (100%) in _S. mutans_, with a larger effect size on NiTi (\(p<0.001\); \(\eta^{2}=0.871\)) than SS (\(p=0.003\); \(\eta^{2}=0.532\)) and with no difference between the two last solutions. Adhesion inhibition was high with all three agents (97-100%) in _A. actinomycetemcomitans_. Perio Plus had the greatest inhibition, and CHX DG had the smallest effect size on SS (\(p=0.003\); \(\eta^{2}=0.311\)) than NiTi (\(p<0.001\); \(\eta^{2}=0.618\)). After Perio Plus therapy, _V. parvula_ had the highest inhibition on NiTi alloy, and the lowest after using CHX DG (100% and 69%; \(p<0.001\); \(\eta^{2}=0.671\)). On SS, Perio Plus showed the highest inhibition  
Figure 5: Comparison of viability by fluorescence on confocal microscope. Total mean fluorescence is measured for propidium iodide and SYTO® 9 stained adherent bacteria before and after treatment with CHX DG. The experiment is repeated 2\(\times\), a minimum of three images are analysed, and the mean fluorescence with 95% confidence interval is shown. Significant differences between alloys for the same bacteria and condition are marked with an asterisk.  
of adherence but the lowest inhibition after Curasept therapy (99% and 78%; \(p<0.001\); \(\eta^{2}=0.604\)).
###### Abstract  
The objective of this study is to assess the impact of low-frequency electromagnetic fields (LF EMFs) generated by power infrastructure on the nearby environment. Measurements of electric (\(E\)) and magnetic (\(H\)) field intensities were conducted around high-voltage power lines, transformer stations and facilities related to them. Numerical simulations were also performed to model the distribution of the field values around real buildings in close proximity to power delivery systems. Given the ongoing scientific debate regarding the effects of EMFs on living organisms, the current analysis was based on the existing standards--particularly ICNIRP 2010 guidelines, which set the maximum allowable \(E\) and magnetic induction (\(B\)) values at 5 kV/m and 200 uT, respectively. Stricter national regulations were also examined, such as Poland's 1 kV/m \(E\) limit in residential areas and Belgium's 10 uT limit for \(B\). The results showed that while most cases complied with ICNIRP 2010 standards, certain stricter local regulations were exceeded. Specifically, 9 of 14 cases exceeded Poland's \(E\) limits, and 8 failed to meet Belgium's \(B\) requirements. Only in one place--a warehouse near 110 kV power lines (in a critical case)--the ICNIRP limit \(B\) was exceeded. These findings underscore the variability in regulatory standards and highlight the need for localized assessments of EMF exposure.  
electronic field; health hazards; power infrastructure; electric power distribution; electromagnetic compatibility +
Footnote †: journal: Applied sciences
## 4 Proposed Resource Allocation Scheme
### Tradeoff between Customer Satisfaction and Service Provider Profit  
Two first-fit greedy scheduling algorithms are invoked here for the system, when requests arrive in a scheduling interval; and the cloud service provider wants to maximize one objective parameter while maintaining the other one within a bounded value. In this work, the First Fit Satisfaction (FFS) algorithm is used to expand customer satisfaction while keeping a bound of unit profit, while the First Fit Profit (FFP) algorithm is proposed to escalate profit by affirming a target of customer satisfaction.
# Virtual Reality as an Innovative Tool for Numerosity Perception
## 3 Results
An initial plateau at low and moderate numerosities is followed by a second phase where response times are faster [34]. In this study, a similar approach was followed, analyzing the response times for numerosity discriminations in VR. Figure 5 illustrates the response times recorded for each participant with a box plot, and the median values in the three conditions. The box plot visually represents data distribution by displaying the median (Q2) and the interquartile range (IQR), which is the difference between the third quartile (Q3) and the first quartile (Q1). The whiskers extend to the maximum and minimum values within 1.5 times the IQR from Q3 and Q1, while outliers beyond this range are shown as individual black dots. The frequentist statistical analysis with one-way repeated measures ANOVA resulted in the presence of statistically significant differences among the three numerosity conditions (F(10,2) = 10.4, \(p\) = 0.004). It is also confirmed by the Bayesian statistics, showing strong evidence in favor of the alternative hypothesis (BF\({}_{\text{M}}\) = 11.17). [PERSON] corrected post hoc _t_-test for the three conditions founds again a slight descending pattern, with the two lower numerosities presenting a similar response time (663 and 643 ms t(5) = 1.27 \(p\) = 0.69, BF\({}_{10}\) = 0.40) and the higher numerosity leading to faster responses by about 60 ms (average 593 ms), which is different from the two lower numerosities (low vs. high t(5) = 4.42, \(p\) = 0.004, BF\({}_{10}\) = 6.1; intermediate vs. high t(5) = 3.1, \(p\) = 0.031, BF\({}_{10}\) = 1.8). Also with the response times, there is no significant difference between low and intermediate conditions, while high is significantly different from both intermediate (limited evidence) and low conditions (strong evidence). Figure 5: Response times of participants for the three numerosity conditions. Box plot of the response times plotted individually with raw values overlapped (**a**). Bar chart showing median values averaged with individual trend lines (**b**). This is also confirmed by the [PERSON]'s \(d\) values for the pairwise effect sizes. The results indicate a huge effect for the comparisons between low vs. high (d = 1.50) and a very large effect for intermediate vs. high (d = 0.93), suggesting relevant differences in means. In contrast, the comparison between low and intermediate conditions yielded a small effect size (d = 0.33).
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
###### Abstract  
NTRK gene fusions are drivers of tumorigenesis events that specific Trk-inhibitors can target. Current knowledge of the downstream pathways activated has been previously limited to the pathways of regulator proteins phosphorylated directly by Trk receptors. Here, we aimed to detect genes whose expression is increased in response to the activation of these pathways. We identified and analyzed differentially expressed genes in thyroid cancer samples with _NTRK1_ or _NTRK3_ gene fusions, and without any NTRK fusions, versus normal thyroid gland tissues, using data from the Cancer Genome Atlas, the DESeq2 tool, and the Genome Enhancer and geneXplain platforms. Searching for the genes activated only in samples with an NTRK fusion as opposed to those without NTRK fusions, we identified 29 genes involved in nervous system development, including _AUTS2_, _DTNA_, _ERBB4_, _FLRT2_, _FLRT3_, _RPH3A_, and _SCN4A_. We found that genes regulating the expression of the upregulated genes (i.e., upstream regulators) were enriched in the \"signaling by _ERBB4_\" pathway. _ERBB4_ was also one of three genes encoding master regulators whose expression was increased only in samples with an NTRK fusion. Moreover, the algorithm searching for positive feedback loops for gene promoters and transcription factors (a so-called \"walking pathways\" algorithm) identified the ErbB4 protein as the key master regulator. _ERBB4_ upregulation (_p_-value = 0.004) was confirmed in an independent sample of _ETV6-NTRK3_-positive FFPE specimens. Thus, ErbB4 is the potential key regulator of the pathways activated by NTRK gene fusions in thyroid cancer. These results are preliminary and require additional biochemical validation.  
thyroid cancer; NTRK; ErbB4; pathways; gene fusions +
Footnote †: journal: _Sciences_
# A Unified Nonlinear Elastic Model for Rock Material
## 5 Experimental Validation of the New Model  
### Experimental Methods  
The fitting effect of the unified nonlinear elastic model and whether it could express the nonlinear elastic deformation of different rock types required verification by indoor experiments. Four different types of middle to hard rocks were selected for uniaxial compression testing with the rock mechanics test system. The four types of rock were dolomite, granite, limestone and sandstone. Cylindrical samples were prepared according to the suggested methods proposed by the International Society of Rock Mechanics and Rock Engineering (IRSM) [38]. The samples were 50 mm in diameter and 100 mm in length. The appearance of the samples was checked to ensure that they had no foliation,  
Figure 6: Fitting results of different strain ranges.  
Figure 7: Approximating to a single stress-strain curve.  
points or big fractures and that, to some extent, they were intact rock specimens. The differences between the four rock types were directly attributed to the mineral components, porosity and microfractures. The samples of the four rock types have the same brittle failure characteristics.  
The tests were performed with a loading speed of 0.05 m/s in the axial direction and with no confining pressure. The displacement sensors were installed at the end and side surface of the rock specimens in order to measure their deformation, and their precision was not higher than 0.01 mm. The rock mechanics test system and the rock specimens are shown in Figures 8 and 9, respectively. During the experiments, the uniaxial stress-strain curves were recorded as listed in Figure 10. According to the values of the uniaxial compressive strength, the stress-strain curves of the ten rock specimens from every rock type were selected for analysis.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion  
### Results  
#### 5.1.1 Experimental Result on SNLI Dataset  
Table 3 shows different SNLI data models' performance, including the neural network-based model, attention-mechanism-based model, and sentence representation method proposed in this paper.  
The models based on attention mechanism include (1) Multi-head [23]: using only an eight-layer multi-head attention mechanism; (2) DiSAN [24]: 300-dimensional bidirectional self-attention network, including forward and backward self-attention network. The models based on the neural network include (1) 300D LSTM [41]: composed of a 300-dimensional unidirectional LSTM network; (2) BiLSTM [42]: composed of a 600-dimensional BiLSTM, including 300-dimensional forward LSTM and 300-dimensional backward LSTM; (3) BiLSTM Intra-attention [41]: combining a single layer self-attention mechanism based on BiLSTM network; and (4) BiLSTM Deep-Gated attention [22]: Based on the BiLSTM network, it combines the depth threshold attention mechanism.  
The experimental results show that the multi-layer semantic representation network's accuracy rate in this paper reaches 91.7% on the SNLI training set and 86.1% on the test set. The highest accuracy rate of the other two models is only 85.6%. The model's performance in this chapter is better than that of the other two types of models. It shows that the accuracy of sentence representation can be effectively improved; by adding multi-layer semantics in the representation, the reasoning result is improved.
## 2 Gaussian Process Data Modelling and Maximum Likelihood-Based Data Fusion Method  
A schematic diagram of the proposed Gaussian process data modelling and maximum likelihood data fusion method is shown in Figure 1. It starts with the registration of two datasets to a common coordinate system. The measured data of the CMM is presented in a point cloud format while the global coordinate information for different sensors is determined in advance in the calibration process, by using a standard calibration ball. However, [PERSON] et al. [17] reported that there still existed small residual error regarding the sensor's related position at the micrometre level. To further improve the overall accuracy of the measurement process, the datasets are first registered to a single coordinate system with the ICP (Iterative Closest Point) algorithm [18]. This aims to perform fine registration since the coordinate information of the two datasets is calibrated before the measurement so coarse registration is undertaken. After registration, the datasets are aligned in a common coordinate system. The measurement datasets are then modelled using the Gaussian process, and the mean surfaces and the covariance surfaces of the measurements are obtained at the same locations. Hence, the mean surfaces and the covariance surface are fused together using the maximum likelihood data fusion algorithm. In the end, the best estimated underlying surface and its associated measurement uncertainty are determined. In this method, Gaussian process modelling is used to determine the underlying surface and its measurement uncertainty for each measurement from different sensors. With the measurement data and the uncertainty information, the maximum likelihood data fusion is used to determine the best estimated result in a statistical manner. The details of the proposed generalized data fusion method are explained in the following sections.
## 4 Conclusions  
In this study, the DeltaSpot process, in which different process tapes were applied to the upper and lower electrodes, was used for dissimilar welding between an AA6061-T6 aluminum alloy and galvannealed steel. The lap shear tensile strength of the welded specimen was evaluated under a corrosive environment for the DeltaSpot-welded and weld-bonded conditions, to which the adhesive was applied, and the following conclusions can be drawn.  
In the case of the weld bond condition, where the structural adhesive was applied, the tensile shear strength was higher at room temperature than when only DeltaSpot welding was applied, and this tendency was maintained even after the corrosion resistance test.  
With the same weld bond condition, the shear tensile strengths after the salt spray test at 1920 h were 3.25 kN and 5.88 kN with and without the faying interface roughened specimen, respectively. Namely, when the structural adhesive was applied by increasing the surface roughness of the specimen, the shear tensile strength increased by 81%.  
The specimen to which the adhesive was not applied showed rapid corrosion, and Zn and O were detected using SEM and EDAX analysis. Corrosion proceeded on the steel side, resulting in very low tensile shear test results.  
Figure 11: EDAX analysis for Specimens 3 and 4 after the salt spray test (1920 h).  
Figure 10: Energy dispersive X-ray (EDAX) spectrometer analysis of the rusted area for Specim 1 after the salt spray test (1920 h). The horizontal axis of each graph is energy (KeV), and the vertical axis is intensity.  
At the fracture site of the welding condition where the adhesive was applied, it was confirmed that structural adhesive components such as C and Al were detected and Zn was not, so the corrosion hardly proceeded. This confirms the reason why the shear tensile strength was higher when compared with other welding conditions.  
It was established that the application of a structural adhesive for dissimilar welding between aluminum alloy and steel by the DeltaSpot process was effective in preventing a sudden decrease in strength in a corrosive environment, and the effect can be enhanced by applying an adhesive with increased surface roughness.  
Conceptualization, Y.-G.K.; data curation, S.-M.J.; investigation, S.-M.J., Y.-G.K. and M.-S.O.; methodology, S.-M.J. and Y.-G.K.; validation, M.-S.O.; writing--original draft, Y.-G.K.; writing--review and editing, S.-M.J. All authors have read and agreed to the published version of the manuscript.  
This study was supported by research fund from Chosun University (207179006-1).  
The authors declare no conflict of interest.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 4 Experimental Results and Analysis
### Comparisions with State-of-the-Arts
#### 4.5.3 Impact of Multi-Task Learning with Parameters \(\lambda_{1}\) and \(\lambda_{2}\)  
We further explore the influence of hyperparameters \(\lambda_{1}\) and \(\lambda_{2}\) within our loss function, aiming to identify the optimal balance among local regression loss and the interval penalty in the two-stage estimation scenario. We conduct experiments on Morph II by fixing one parameter at its optimal value and varying the other to study its impact. It is important to note that \(\lambda_{1}\) cannot be set to 0, as this would result in the disappearance of the local regression module, leading to the loss of local adjustment information. Figure 4a illustrates the performance trend as \(\lambda_{1}\) increases from 1 to 8, while \(\lambda_{2}\) is fixed at 4. It is clear that the network achieves its lowest MAE around 2.2 and highest CS(3) of approximately 80% when \(\lambda_{1}\) = 4. This indicates that an appropriate weight on the regression loss effectively fine-tunes the local age predictions, while excessively large or small values may impair the model's capability to accurately capture fine-grained age-related features. Figure 4b shows the model performance variation as \(\lambda_{2}\) changes from 0 to 8 with \(\lambda_{1}\) fixed at 4. The results demonstrate a clear initial improvement in MAE and CS(3) as \(\lambda_{2}\) increases up to 4, after which the performance declines. This trend suggests that an appropriate interval penalty effectively constrains predictions to reasonable local age intervals, while overly strict or overly relaxed interval penalties reduce prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Radius Parameter**} & \multicolumn{2}{c}{**Morph II**} & \multicolumn{2}{c}{**MegaAge-Asian**} & \multicolumn{2}{c}{**CACD**} \\ \cline{2-7}  & **MAE** & **CS(3)** & **MAE** & **CS(3)** & **MAE** & **CS(3)** \\ \hline r = 5 & 2.43 & 70.69 & 3.13 & 61.21 & 4.72 & 50.30 \\ r = 10 & 2.18 & 79.93 & 3.09 & 63.11 & 4.47 & 52.91 \\ r = 15 & 2.21 & 79.12 & 3.18 & 64.55 & 4.63 & 50.12 \\ r = 20 & 2.23 & 78.41 & 3.10 & 63.13 & 4.  
Based on the above analyses, we finalize our choice as \(\lambda_{1}\) = 4 and \(\lambda_{2}\) = 4, striking an optimal balance between local regression precision and interval constraint stability, thus yielding the best overall age estimation results.
## 3 Proposed Method
### Graph Convolutional Network Clustering (GCNC)
```
1:Input: Graph A, Feature X, Label Y
2:Output: Node representation \(\hat{X}\)
3: Partition graph nodes into \(c\) clusters \(V_{1},V_{2},\dots\dots\), \(V_{c}\)
4:For\(iter=1,\dots\) max \(\mathbf{do}\)
5: Randomly choose \(q\) clusters, \(t_{1}\dots t_{q}\) from V without replacement;
6: From the subgraph \(\hat{G}\) with nodes \(\hat{V}=\Big{[}V_{t_{1}},V_{t_{2}},\dots,V_{t_{q}}\Big{]}\) and links \(A_{\hat{V},\hat{V}}\);
7: Compute \(g\leftarrow\
abla L_{A_{\hat{V},\hat{V}}}\) (loss on the subgraph \(A_{\hat{V},\hat{V}}\));
8: Conduct adam update using gradient estimator \(g\)
9:Output \(\{W_{l}\}_{l=1}^{L}\)
```  
**Algorithm 1**Boost Graph Convolutional Network Clustering  
The data is divided into groups using the Boost Graph Convolutional Network Clustering technique, and each group is then handed to a faster parallel process to increase the computational complexity in terms of time and memory measurements. The magnify pinpointing-based encryption method is then applied to the data groups, which transforms the data into a different format based on the key value generated. To save time, the encryption, decryption, and key creation processes all use the magnify pinpointing-based approach.
# Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks
## 3 Risk Definition and Identification of Space Station
### Risk Identification  
Risk identification often uses preliminary hazard analysis (PHA) [37; 38] and system hazard analysis (SHA) [39] methods. For the space station configuration, the multi-layer and multi-type risks identifications use a combination of these methods. According to the PHA and SHA methods, 457 risks are identified and shown in Table 1.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.7 Reliable Multi-Path Energy-Efficient Routing Protocol (RME2 RP)  
Similar to the aforementioned routing protocol (RE2 CRP) in [152; 153], where the author proposed another multi-route reliable and energy-efficient routing protocol (RME2 RP) for UWSNs. The proposed routing protocol have also two phases: (i) Data packet forwarding phase, (ii) Path/route development phase. A special type of communicating node which is called a courier communicating node, where the sink node is directly connected, and deployed with each layer in the network. During the first phase of RME2 RP, the regular sensor Rx nodes design a route from the Tx anchor node to the forwarding node and following the multi-path disconnected technique. The courier communicating nodes broadcast hello message, and after acquiring it, the adjacent communicating nodes join the multi-path disjoint technique and change its neighbor table. Furthermore, during the second phase of the RME2 RP, the Tx anchor node broadcasts a path request through various links. When receive a path request, the routing table is updated by the nearby nodes. The path is selected on the basis of low cost of a link. However, in contrast with the aforementioned routing protocol RE2 CRP in [152], the RME2 RP uses the multi-path routing technique. Although the proposed protocol can achieve the reliability but cannot perform data packet redundancy.
## 3 Research Methodology  
The methodological process comprises several sequential steps, as shown in Figure 1:  
* Literature review: the process begins with a comprehensive review of the relevant literature to identify the key benefits and challenges associated with adopting DTs and AI-based DT technologies in the construction industry.
* Survey development and validation: initial survey questions are formulated based on the findings of literature review. These questions are subsequently refined through semi-structured interviews with five field experts, each with over 10 years of experience, to ensure the clarity and relevance of the questions to achieving the study aim. This step is followed by a pilot survey to improve clarity and ultimately strengthen the validity and credibility of the final research findings.
* Data collection: the validated questionnaire is distributed to the target respondents via a web link to gather the necessary data.
* Data preparation: the collected survey data are then prepared for analysis. This step ensures that the data are complete, consistent, and properly formatted for processing.
* Data analysis: finally, the quantitative data are analyzed using correspondence analysis (CA) to identify and prioritize the most significant benefits and challenges of adopting AI-based DT technologies in the construction sector.