esa-sceva/satcom-chunk-collection huggingface dataset:
# Confocal Laser Endomicroscopy for Bladder Cancer Detection: Where Do We Stand?
## 3 Results  
A total of 158 articles were retrieved, of which 79 were rejected and 38 were removed as duplicates. From the remaining 34 papers, 15 were revisions of the literature and 10 articles were related to urothelial carcinoma of the upper urinary tract. Therefore, nine papers were selected. Among them, one was excluded [14] as it was an ex vivo study on radical cystectomy specimens; another one [13] was not considered as it presented a depictional atlas of CLE imaging obtained from 66 patients. Seven original articles were included in this SR [15; 16; 17; 18; 19; 20; 21]. Those include five prospective clinical trials with 214 patients overall and 312 bladder lesions where CLE was tested. The detailed PRISMA flowchart is reported in Figure 1. Regarding quality assessment, three out five prospective studies have at least high risk of bias in one Quadas-2 domain, whereas applicability has always a low risk of bias (Table 1). Moreover, the study design is different and there are no uniform comparators among all studies. The Cellvizio probe (Cystoflex UHD-R probe, Mauna Kea Technologies, Paris, France) was adopted in all studies, with fluorescein as contrast dye. Fluorescein was administered intravenously or by bladder instillation. [PERSON] et al. [15] reported the advantage of the one way over the other. Overall, the correspondence rate between CLE and histopathological examination ranged from 54.6% to 93.6%. The work by [PERSON] et al. [16] outlined that the sensitivity and specificity of CLE was strictly related to observer experience. In fact, the percentage of agreement between observers for BCa diagnosis ranged from 90% for experienced CLE neurologists to 77% for nonclinical researchers. The same for CLE specificity, which ranged from 88% to 73%. Significative differences were also reported for cancer grading, with specificity ranging from 94% to 73% for low-grade lesions and from 64% to 60% for high-grade ones. However, all the retrieved articles outlined a high inter-observer variability, which cannot allow a clear depiction of CLE reliability for BCa diagnosis in this actual form. The results of the retrieved studies are schematically reported in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subulation** & **Patients’ Selection** & **Index Test** & \multicolumn{2}{c}{**Reference Standard**} & **Flow and Timing** \\ \hline  & Risk of bias & Applicability & Risk of bias & Applicability & Risk of bias & Applicability & Risk of bias \\ \hline [PERSON] 2019 [17] & Low & Low & High & Low & High & Low & Low \\ \hline [PERSON] 2019 [18] & Low & Low & Low & Low & Low & Low & Low \\ \hline [PERSON] 2019 [19] & Low & Low & High & Low & Low & Low & Low \\ \hline [PERSON] 2020 [20] & Low & Low & Low & Low & Low & Low & Low \\ \hline [PERSON] 2022 [21] & High & Low & Unclear & Low & High & Low & Unclear \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Risk-of-bias assessment according to Quada2 criteria [11].
## 4 Numerical Simulation
### Simulation Results
#### 4.2.3 Vertical Displacement Analysis of the Top of the Enclosure Wall  
The monitoring point W1 was selected in the constructed model for the vertical displacement analysis of the top of the enclosure wall, and its simulated and measured values were compared and analyzed under different construction stages, as shown in Figure 11. The curve representing the measured values of the monitoring point W1 is denoted as AW1, and the curve representing the simulated values is denoted as BW1.  
Figure 10: Comparison of the curves between simulated and measured values of horizontal displacement of the top of the enclosure wall.  
Figure 11 shows a noticeable difference between the trends of the simulated and measured values. The measured value stabilizes and then gradually increases, while the simulated value slowly increases, suddenly increases, and then gradually decreases. In actual construction, the top of the enclosure wall is subject to vertical displacement during initial excavation, and this displacement tends to stabilize after horizontal cross bracing is installed. However, with the gradual increase in excavation depth, the unbalanced force on the two measurements of the enclosure wall increases, the soil drainage behind the enclosure wall also increases, and the soil near the enclosure wall reconsolidates; therefore, the top of the enclosure wall will produce vertical displacement.  
When establishing the numerical model, it was assumed that the soil is impermeable and that the difference between water and soil pressure inside and outside the enclosure wall during the initial process of excavation would be maintained at a large level, resulting in the settlement of the enclosure wall. As the excavation progresses, the horizontal cross bracing plays a significant role in reducing the pressure inside and outside the enclosure wall, and the bottom of the foundation pit springs back, driving the enclosure wall to move upward, thus reducing the settlement of the enclosure wall.  
The maximum vertical displacements of the top of the enclosure wall, both measured and simulated, were within the limit value of 30 mm, indicating that the support structure remained stable and in a safe state throughout the excavation of the foundation pit.
## 3 Classical Methods for Generating OAM Waves in RF Domain
### Spiral and Twisted Reflectors  
The spiral reflector proposed in [32] was made from styrofoam blocks, covered with an electrically conductive and reflective surface and was illuminated by a seven-element Yagi-Uda antenna intended for 2.4-2.5 GHz wireless local area network (WLAN) communications (Figure 4). The radio beam is reflected off the discrete eight-step staircase phase mask representing a discrete approximation of a non-focusing spiral reflector designed for a total 2\(\pi\) phase shift spiral.  
Figure 3: Designs of several holograms for orbital angular momentum (OAM) vortex wave generation and the obtained radiation patterns in far-field at 60 GHz [29].  
In [11; 33], a helicoidal parabolic (twisted reflector) antenna was used to generate a vortex wave with a feeding source located at the focal point (Figure 5). The twisted reflector converts the field generated by the feeding source into a helical beam with a topological charge \(l\) that depends on the height of the step of the twisted reflector.  
Such helicoidal parabolic reflector was recently flattened by the use of the transformation optics (TO) mathematical concept [41; 42] and metamaterial structures. In [43; 44], a spatial TO-based OAM generation method was proposed to design a flattened perfect electric conductor (PEC) backed three-dimensional (3D) printed cylindrically shaped dielectric structure so as to mimic a twisted reflector (Figure 6). The TO-based generation method allows to transform a certain zone above the helicoidal parabolic antenna into an oblate cylindrical space. This transformation led to a medium characterized by a permittivity distribution varying from 1 to 2.8, allowing a 3D printing of the device. By transmitting through the calculated oblate cylindrical space, a plane wave emitted from the feeding source is transformed into a vortex beam after its reflection from the grounded planar surface rather than from the helicoidal parabolic reflector. The all-dielectric OAM generation device is capable of maintaining performance over a broad frequency range since all-dielectric non-resonant materials are used in the design, as illustrated in Figure 6.  
Figure 4: Photography of the spiral reflector and the near electric-field intensity and phase patterns measured (top) and simulated (bottom) at 2.4 GHz [32].  
Figure 5: Photography of the twisted reflector and the electric-field magnitude and phase patterns measured in the far-field at 17.2 GHz [33].
## 2 Rock-Drilling Robot Arm Modeling and Error Calculation
### Rock-Drilling Robot Arm Modeling  
The rock-drilling arm possesses seven degrees of freedom, which include five rotary joints and two telescopic joints. In its unextended state, the total length of the arm and the beam is 7835 mm. The arm can be extended by an additional 1600 mm, and the beam can be extended by 1800 mm. The structure of the rock-drilling arm is depicted in Figure 1.  
In this study, based on the mechanical structure of the rock-drilling robotic arm depicted in Figure 1, the DH method was utilized to establish the corresponding link coordinate system, as illustrated in Figure 2. The DH parameters of the robotic arm are presented in Table 1.  
The coordinates between coordinate system \(O_{i-1}\) and coordinate system \(O_{i}\) are transformed through rotation or translation to obtain the transformation matrix between the neighboring coordinate systems. The corresponding transformation matrix is presented in Equation (1).  
\[\underset{i}{i-1}T=Rot(z,\theta_{i})Trans(0,0,D_{i})Trans(L_{i},0,0)Rot(x,\alpha _{i}) \tag{1}\]  
where \(\underset{i}{i-1}T\) is the positional transformation matrix of coordinate system \(O_{i-1}\) relative to coordinate system \(O_{i}\), \(Rot\) represents the rotational transformation, and \(Trans\) represents the translational transformation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Joint** & \(\theta_{i}\)/\({}^{o}\) & \(D_{i}\)/mm & \(L_{i}\)/mm & \(\alpha_{i}\)/\({}^{o}\) \\ \hline
1 & \(\theta_{1}\) (0) & 0 & 200 & \(-90\) \\
2 & \(\theta_{2}\) (90) & 0 & \(-35\) & 90 \\
3 & 90 & \(D_{3}\) (4297) & 0 & 0 \\
4 & \(\theta_{4}\) (90) & 0 & 0 & 90 \\
5 & \(\theta_{5}\) (90) & 650 & 80 & 90 \\
6 & \(\theta_{6}\) (90) & 679.5 & 241 & 90 \\
7 & 0 & \(D_{7}\) (3058) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: DH parameters of the robotic arm.  
Figure 1: Structure of the robotic arm.  
Figure 2: Coordinate system of each joint linkage in the robotic arm.  
Expanding (1) yields the positional matrix between the neighboring coordinate systems.  
\[\begin{split} i^{-1}T=\left[\begin{array}{cccc}\cos\theta_{i}&- \sin\theta_{i}\cos\alpha_{i}&\sin\theta_{i}\sin\alpha_{i}&L_{i}\cos\theta_{i}\\ \sin\theta_{i}&\cos\theta_{i}\cos\alpha_{i}&-\cos\theta_{i}\sin\alpha_{i}&L_{i }\sin\theta_{i}\\ 0&\sin\alpha_{i}&\cos\alpha_{i}&D_{i}\\ 0&0&0&1\end{array}\right]\end{split} \tag{2}\]  
The end position of the robotic arm is obtained by sequentially multiplying the coordinate systems using the corresponding parameters within the transformation matrices.  
\[\begin{split} 0\\ 7\\ T=\begin{bmatrix}0\\ 1\\ 2\end{bmatrix}T_{2}^{1}T_{3}^{2}T_{4}^{3}T_{5}^{4}T_{6}^{5}T_{7}^{6}T=\left[ \begin{array}{cc}R&P\\ 0&1\end{array}\right]\end{split} \tag{3}\]  
where \(R=\left[\begin{array}{cccc}n_{x}&o_{x}&a_{x}\\ n_{y}&o_{y}&a_{y}\\ n_{z}&o_{z}&a_{z}\end{array}\right]\) and \(P=[p_{x},p_{y},p_{z}]^{T}\). \(R\) denotes the end orientation of the robotic arm, and \(P\) represents the end position.
## 1 Introduction
Figure 2: Comparison of the critical state lines: (**a**) Test data [11] vs. Equations (1) and (2); (**b**) desired CSL vs. Equations (1) and (2). [PERSON] et al. [13] were the first to revise the shear yield and shear failure in the MCC model from the extended Mises criterion to the SMP criterion by introducing the transformed stress. [PERSON] et al. [14] successfully applied the transformed stress of [13] to model loose and dense sand, depending on two key factors, i.e., the initial density (\(e_{0}\)) and mean effective stress (\(p_{0}\)). Furthermore, [PERSON] et al. [15] proposed another transformed stress, in conjunction with a new hardening parameter, to the MCC model to model both clay and sand. Finally, in this study, a new curve of the CSL in the \(e\)-In(\(p\)) is derived, which has the ability to fit a straight line, curved line, and a curved line connected with a straight line. Therefore, the sand model developed by [PERSON] et al. [14] can be extended for both clean sand and silty sand. Additionally, we have made some revisions to the work of [PERSON] et al. [14] to take into account the effect of the participation of fines in sand by applying the concept of the equivalent intergranular void ratio (\(e^{*}\)) proposed by [PERSON] and [PERSON] [29] and the fines influence factor (\(\beta\)) proposed by [PERSON] [9]. The roundness of both sand and fines can settle the arguments in the load bearing mechanism of many silty sands in the literature, according to the work of [PERSON] [9]. With these ideas, it is possible to oversimplify the soil characteristics in the critical state model of silty sand.
## 3 Results and Discussion
### Cell Viability Assay  
In cytotoxicity assays, we used Calcein-AM to stain live cells for cell visualisation. When Calcein-AM enters the cytoplasm, esterases hydrolysate it to Calcein, which remains in the cell and emits a strong green fluorescence. Cell proliferation of BV2 on the surfaces of the four kinds of microparticles for 48 h was detected via CCK-8. It can be seen from Figure 10 that cells proliferated on several material surfaces to some extent in two days. When BV2 were cultured for 48 h, OD values of the five groups showed little difference. The results showed that none of the four materials were cytotoxic and the addition of rH did not have much effect on the cytotoxicity of the micron particles.  
Figure 9: (a) Photographs and (b) haemolysis rates of rH/CS/PCL MPs at different concentrations, from 0.0625 to 4 mg mL\({}^{-1}\), \(n=3\).
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 3 Methodology of the Proposed Bangla Alphabet Detection Algorithm
### Overview of YOLOv11 and ResNet50
#### 3.1.2 ResNet50 Integration  
ResNet50 is integrated as the backbone of the YOLOv11 framework to further strengthen the model's capacity to recognize complex visual features. ResNet50 employs residual learning to address the vanishing gradient problem, allowing for deeper architectures without compromising gradient flow.  
Residual Learning and Spatial Hierarchy: The ResNet50 architecture consists of multiple convolutional and identity blocks interconnected through skip connections. This structure preserves spatial hierarchies and facilitates the extraction of fine-grained visual patterns essential for distinguishing nuanced character shapes.  
Transfer Learning: The model is initialized using ResNet50 pre-trained on the ImageNet dataset. These pre-trained weights are fine-tuned on our custom Bangla alphabet dataset, enabling the model to leverage robust low- and mid-level visual features. This transfer learning strategy accelerates convergence and improves recognition accuracy, particularly when training data are limited.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 2 Method  
This study constituted a mixed-method review, employing a narrative-oriented approach to investigate the research topic. It employed a systematic retrieval methodology to meticulously gather data from credible and reliable sources. The extent of a systematic review depends largely on the scope and quality of the research institute. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines are a set of standard specifications established for the quality of systematic reviews [18]. It is applicable to reviews of published literature containing original data materials, and aims to improve the scientific rigor and comparability of systematic reviews. In the process of reviewing articles, it can help the review team to report clearly and transparently, and provide sufficient details [19]. We used the PRISMA 2020 flowchart for systematic review, which includes searches from the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) sources. As shown in Figure 1, We searched for terms such as \"EEG\", \"eye-tracking\", \"ET\", \"neuroscience\", \"user research\", \"product design\", \"interface research\", and \"spatial environment\" as keywords, including studies that contribute to the development of this field. We expanded our search to ensure the reliability of articles through title and abstract, data identification, preliminary screening, and qualification determination. Finally, 253 articles were identified, and the main results were summarized and classified according to the research content. Among them, 115 articles were applied in this paper.  
On the basis of the review, we used the bibliometric software Citespace 6.2.R3 to conduct the research analysis of keywords for the 253 screened articles. Citespace has the properties and characteristics of \"graph\" and \"spectrum\" in its literature measurement research, and is mainly used to visualize the knowledge landscape of a research field [20].  
Figure 1: Data collection process.  
We used it to obtain the scope of application of EEG and eye-tracking technology in user research, and performed a visual analysis. During the review process, we found that articles that used EEG or eye-tracking technology with user research as the key topic were very limited. Therefore, we carefully studied the content of these articles and selected those that involved user research. Based on these literature contents, this review was refined and summarized, focusing on the main research indicators and their significance of EEG and eye-tracking technology in user research, as well as a series of applications of user research in industrial products, digital interfaces, and different spatial environments; the details of the entire research concept framework are shown in Figure 2. Finally, based on these summary conclusions, we proposed suggestions and directions for future research on the shortcomings of current EEG and eye-tracking research methods.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
###### Abstract  
H-section columns with random pitting corrosion exhibit localized damage, making it imperative to conduct a thorough assessment in order to guarantee their long-term structural safety and integrity. This paper presents a method for constructing finite element models of randomly pitted H-section stub columns. The accuracy of the models was first validated against existing experiments. Various scenarios, accounting for different levels of pitting intensities and locations of corrosion occurrence, were considered, to elucidate the mechanisms of the reduction in ultimate strength and stiffness, as well as the failure of columns. Additionally, the influence of the width-to-thickness ratio of the plate on the ultimate strength of stub columns was also taken into account. A method to address the thickness loss resulting from random pitting corrosion was proposed for the ultimate strength assessment of randomly pitted stub columns, and its accuracy was verified based on the Chinese and European Standards. An empirical formula was proposed and verified upon the results of numerous stochastic simulations of randomly pitted H-section columns. The results demonstrated that for axially loaded H-section stub columns, both ultimate strength and stiffness decrease significantly and nonlinearly with the increase in the degree of pitting damage. Corrosion can change the failure mode of a stub column by inducing local buckling in a plate that initially satisfies the buckling criterion before the overall column failure.  
H-section column; pitting corrosion; ultimate strength; failure mode; equivalent thickness
# Development of Hybrid Airlift-Jet Pump with Its Performance Analysis
## 2 New Concept of Hybrid Airlift-Jet Pump
### Operating Principle of Airlift Pump  
The schematic design of an airlift pump is described in Figure 2. Based on the dotted line in Figure 2, the pressure of water, and that of the water/air mixture are the same, which can be written as:  
\[\rho_{water}gh_{1}=\rho_{mix}gh_{2} \tag{3}\]  
Because the mixture density is lower than the pure water density, Equation (3) results in \(h_{2}~{}>~{}h_{1}\). This difference causes water to be pumped up.
## 3 Ridge Regression with the Ordered \(\ell_{2}\) Regularization
### The Ordered Ridge Regression  
We propose an ordered ridge regression in Equation (5) and call it the ordered ridge regression because we used an ordered \(\ell_{2}\) regularization with an objective function instead of a standard \(\ell_{2}\) regularization. The ordered ridge regression is commonly used for parameter estimation and variable selection, particularly when data are strongly correlated and highly dimensional. The ordered ridge regression can be defined as follows:  
\[\begin{array}{c}\underset{x\in\mathbb{R}^{p}}{\min}\frac{1}{2}\|Ax-b\|_{2}^{2 }+\frac{1}{2}J_{\lambda}(x)\\ =\underset{x\in\mathbb{R}^{p}}{\min}\frac{1}{2}\|Ax-b\|_{2}^{2}+\frac{1}{2} \underset{k=1}{\overset{p}{\sum}}\lambda_{k}|x_{(k)}|^{2}\end{array} \tag{5}\]  
where \(x\in\mathbb{R}^{p}\) denotes an unknown regression coefficient, \(A\in\mathbb{R}^{n\times p}(p\gg n)\) is a known matrix, \(b\in\mathbb{R}^{n}\) represents a response vector, and \(J_{\lambda}(x)\) is the ordered \(\ell_{2}\) regularization. The optimal parameter choice for the ordered ridge regression is much more stable than that for a regular lasso; also, it achieves adaptivity in the following senses.  
* For decreasing \((\lambda_{k})\), each parameter \(\lambda_{k}\) marks the entry or removal of some variable from the current model (therefore, its coefficient becomes either zero or nonzero); thus, coefficients remain constant in the model. We achieved this by putting some threshold values for \(\lambda_{k}\) (Reference [5], Section 1.4).
* We observed that the price of including new variables declines as more variables are added to the model when the \(\lambda_{k}\) decreases.
## 5 Validation and Results
### Method Validation
#### 5.3.2 Verification Process  
In this section, we initially employ the Pearson correlation coefficient to analyze the linear correlations among scenario complexity, driving risk, and efficiency. The higher the Pearson correlation coefficient, the stronger the linear relationship between the scenario's complexity and the performance indicators. This indicates a higher effectiveness of the evaluation method. The specific formula for this calculation is as follows:  
\[r=\frac{\sum(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum(x_{i}-\bar{x})^{2}\sum (y_{i}-\bar{y})^{2}}} \tag{16}\]  
where \(x_{i}\) and \(y_{i}\) are the observed values of the variables, and \(x\) and \(y\) represent the mean values of these variables. A value of \(r\) close to \(+1\) or \(-1\) indicates a strong positive or negative linear relationship between the variables. The correlations between scenario complexity and both risk and efficiency are assessed independently.  
Second, we employed linear regression analysis to thoroughly explore the specific impact of scenario complexity on driving risk and efficiency. The following linear regression model was employed:  
\[y=\beta_{0}+\beta_{1}x+\epsilon \tag{17}\]  
where \(y\) represents the measured value of driving risk or efficiency, while \(x\) denotes the complexity of the scenario. The coefficients \(\beta_{0}\) and \(\beta_{1}\) represent the intercept and slope, respectively, and \(\epsilon\) signifies the error term. Additionally, a goodness-of-fit test is employed to assess the accuracy of the model's predictions and the explanatory power of the explanatory variables. The specific calculation formula is provided below:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat{y}) ^{2}}, \tag{18}\]  
where \(y\) represents the measured value of risk or efficiency, \(\hat{y}\) denotes the mean value of risk or efficiency, and \(\hat{y}\) indicates the predicted value from the regression.