esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Impact of the Stents on the WSS-Related Indices: RRT  
The RRT is a very interesting variable, as it provides a measure of the relative increase of blood residence time within the aneurysm sac. As discussed, in the literature it has been often related to the tendency of thrombus formation within the aneurysmal sac and hence to the efficiency of the stent. For this reason, it can be used as a parameter for the hemodynamic environment promoted by the placement of a flow diverting device and the consequent desired occlusion of the sac [6; 28]. The obtained RRT spatial distributions seem different from those found in a previous study with a straight vessel configuration [28]. This confirms that the geometry of the artery is crucial for assessing the stent performances [10]. Due to the diverted flow and the induced change of the flow structure, the RRT within the aneurysm is higher for all the stented models compared to the non-stented cases, as visible from Figure 9. This increase can be explained by the large high-OSI regions on the aneurysmatic dome analyzed before. Large recirculations within the sac promote a higher residence time that should induce the required occlusion of the aneurysm. While all the stents promote an increase of RRT, it progressively increases in the order STENT 3 \(\gg\) STENT 2 \(\gg\) STENT 1. STENT 3 systematically promotes the highest RRT.  
When looking at the distribution of the RRT over normalized areas depicted in Figure 10a-f, the results can be seen more clearly. In all aneurysms, STENT 3 promotes a more pronounced increase of regions with higher RRT, especially for RRT \(\geq\) 200 Pa\({}^{-1}\) (see Figure 10a,c,e). These regions are of about 5% to 15% of the whole models. For this reason, STENT 3 seems to provide the most potential for  
Figure 8: Histograms of the normalized area associated to the OSI of the aneurysm and of the artery (and stent) for V-bent, C-bent and S-bent vessel configurations. The depicted areas are normalized with respect to the area of whole geometry model.  
aneurysm thrombosis. In contrast, for 200 Pa\({}^{-1}\)\(\geq\) RRT \(\geq\) 100 Pa\({}^{-1}\), STENT 1 and STENT 2 induce a more noticeable increase compared to STENT 3. In general, within the artery, as expected the RRT looks quite small independent of the presence of the stents (around 50 Pa\({}^{-1}\)). This tends to affect a range of area of about 32% to 55% of the entire models. The latter contributes to enhance the situation of the aneurysm where RRT is very small without stent (less than 50 Pa\({}^{-1}\) in about 38-51% of the models) but tends to drastically increase once the stents are introduced (compare Figure 10a,c,e). As commented, the highest increase is promoted by STENT 3 (for RRT greater than \(\geq\) 200 Pa\({}^{-1}\)).  
Figure 10: _Cont._  
Figure 9: Spatial distribution of the relative residence time (RRT) for the three vessel configurations (top row, V-bent vessel; middle row, C-bent vessel; bottom row, S-bent vessel) and the three stents: (**a**) no stent, (**b**) STENT 1, (**c**) STENT 2 and (**d**) STENT 3.
## 4 Results and Discussions
### Analysis Steps
#### 4.1.2 Improvement Step
Originally, both the case company and the design company had to provide design data and diagrams of single IC schematics, IC circuit schematics, and multiple IC integrated element schematics to be verified by the case company. After improvements were made, only the finalized schematics needed to be verified by the customer and the case company. Such an approach to improvements not only shortens the delay time during design examinations, but also reduces design risks from examinations by the customer and the case company, thereby increasing the competitiveness of IC semiconductors and PCB probe cards.
# Iterative Learning for K-Approval Votes in Crowdsourcing Systems
## 3 Setup  
### Problem Definition  
In this section, we define the setup of the problem. Suppose a crowdsourcing system where there are \(m\) tasks to be solved and \(n\) workers participating. For convenience, tasks and workers are indexed by \(i\in[m]\) and \(j\in[n]\), respectively, where \([m]\) means a set of integers from \(1\) to \(m\) such that \(\{1,...,m\}\).  
In Figure 3, Each task is a multiple-choice question and consists of \(D_{i}\) alternatives (or options) with only one correct answer (correct alternative). Each task is duplicated \(l\) times to make redundancy for boosting performance, which is a common strategy in crowdsourcing systems. Therefore, in total, \(ml\) tasks are to be distributed. Since we use a random and equal assignment strategy to distribute tasks, each worker gets to solve \(r\) tasks such that \(ml=nr\). If we draw this scheme as a graph, a random \((l,r)\)-regular bipartite graph can be made by the pairing model with \(m\) task nodes, \(n\) worker nodes, and \(ml(=nr)\) edges representing tasks assigned to workers.  
Each task allows \(K_{i}\)-approval votes when workers make their own responses. Here, we need to consider how workers formulate their responses when solving tasks. For a simple probabilistic approach, we assume that worker \(j\) solves task \(i\), and gives a response including the correct answer with a probability of \(p_{ij}\in[0,1]\). This probability should be considered as a value that depends on the type of the given task, including \(D_{i}\) and \(K_{i}\), and how reliable the worker is. We will discuss its dependency on the task and worker in Section 3.2. We also assume that distractors (a set of alternatives apart from the correct answer) are independent from each other, and their levels of difficulty are the same. Thus, in the \(K\)-approval voting system, a response includes the correct answer with a probability of \(p_{ij}\), and the remaining alternatives in the response are randomly selected from the distractors.  
The goal of the problem is to infer the correct answer of tasks when workers' responses are given. The error performance can be easily measured by the ratio of the tasks incorrectly inferred such that \(\frac{1}{n}\sum_{i\in[m]}\mathbb{I}(t_{i}\
eq\hat{t}_{i})\), where \(t_{i}\) and \(\hat{t}_{i}\) are the correct and inferred answers of task \(i\), respectively, and \(\mathbb{I}\) is an indicator function.
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
## 3 Analysis and Design of the Probe  
### The Sensitivity Analysis  
The principle of detecting the \(Z\)-motion of the probe, when the ball tip is touched along this direction, is shown in Figure 3. The optical beam with an incidence angle of \(\alpha\) is reflected by the plane mirror and focused onto the QPD. The reflected beam is shifted from point D to point E on the focusing lens, and the focused spot is shifted from point B to point C on the QPD if the QPD is placed in front of the focal point of the lens. From the geometrical relationship in Figure 3, we have \(\sin\alpha=(\overline{FG}/\overline{FH})\) and \(\overline{FG}=\delta_{v}\) (\(\delta_{v}\) is the vertical displacement of the probe tip). The line of FI is perpendicular to the line of AH so that  
\[\sin\angle FHI=\sin(\pi-2\alpha)=sin\left(2\alpha\right)=\overline{FI}/ \overline{FH} \tag{3}\]  
Therefore,  
\[\overline{DE}=\overline{FI}=2\cdot\delta_{v}\cdot\cos\alpha \tag{4}\]  
Because \((\overline{AD}/\overline{AB})=(\overline{DE}/\overline{BC})\), and \(\overline{AD}\) (the focal length of the focusing lens) and \(\overline{BD}\) are donated by \(f\) and \(m\), respectively, we have  
\[\overline{BC}=2\cdot\delta_{v}\cdot(1-m/f)\cdot\cos\alpha \tag{5}\]  
The output voltage (\(U_{v}\)) from QPD is proportional to the displacement of the focused spot (\(\overline{BC}\)), namely, \(U_{v}=k\cdot\overline{BC}\) and \(k\) is a constant. Therefore, the sensitivity in the vertical direction can be obtained as:  
\[S_{v}=U_{v}/S_{v}=2k\cdot(1-m/f)\cdot\cos\alpha \tag{6}\]  
Figure 2: Optical paths of the sensor. (**a**) Mirror in vertical displacement and (**b**) mirror in tilt motion.  
In the same way, the principle of the detecting probe in a horizontal tilt motion, when the probe tip is touched in the horizontal direction and results in a \(\delta_{h}\) displacement is shown in Figure 4. The rotated angle and the length of the stylus are denoted as \(\theta\) and \(l\), respectively. From the geometrical relationship of the optical path, we have \(\overline{MN}\approx\overline{DE}\) and \(\overline{ON}\approx\overline{OD}=n\). Because, in practice, \(\theta\) is very small, it is assumed \(\sin\theta=(\delta_{h}/l)\approx\theta\). The stylus and the floating plate can be regarded as a rigid body and \(\angle NOM=2\theta\), so that  
\[\tan 2\theta=(\overline{MN}/\overline{ON})=(\overline{DE}/n)\approx 2\theta \tag{7}\]  
\[\overline{DE}=2n\delta_{h}/l \tag{8}\]  
Because \(\overline{AB}/\overline{AD}=\overline{BC}/\overline{DE}=(f-m)/f\) and \(U_{h}=k\cdot\overline{BC}\), the horizontal sensitivity can be obtained:  
\[S_{h}=U_{h}/\delta_{h}=(2 nk/l)\cdot(1-m/f) \tag{9}\]  
In order to have the characteristic of uniform sensitivity, we can let Equation (6) be equal to Equation (9), yielding:  
\[\cos\alpha=n/l \tag{10}\]
## 3 A Framework for the IoT-Edge-Cloud Continuum
### The Runtime Layer
#### 3.3.2 Edge Level
* **IoT edge gateway (GW):** Based on a modular multi-interface physical platform, this module is designed to facilitate heterogeneous networking in the IoT-edge-cloud continuum. It acts as an intermediate element connecting low-capable devices at the far edge with the rest of the fog/cloud infrastructure. Beyond providing flexible and transparent interactions among diverse devices and technologies, the IoT edge GW can also offer additional processing capabilities, such as data filtering, managing flow priorities, and executing low-latency actions based on locally available information. This enhances scalability, overall efficiency, and responsiveness of IoT systems, enabling more effective and intelligent data handling at the edge of the network.
* **ARCA Embedded:** ARCA Embedded is the distribution of ARCA Trusted OS designed for ARM architecture. The distribution is based on the same security principle: providing a trusted execution environment (TEE) for sensitive edge workloads. Similar to ARCA Trusted OS, ARCA Embedded features a TPM-based secure boot, immutable file systems, and a seamless system updater. ARCA Embedded leverages the use of ARM Trustzone [57] whenever this hardware extension is available, providing a confidential computing environment for sensitive cryptographic operations.
* **Edge monitoring agent:** Telemetry data, encompassing static resources (e.g., processor capacity, memory availability, storage, and network capabilities), dynamic performance metrics, and the energy consumption (distinguishing between renewable, or _green_, energy sources and those associated with carbon emissions) of edge nodes, are collected by the monitoring agent and relayed to the already presented monitoring server.
## 2 Evolution of NMR Methodologies  
As NMR methodologies evolved from simple 1D experiments to advanced multidimensional approaches, they became increasingly optimized for specific polysaccharide states and research requirements. Table 1 provides a unified classification of these techniques, organized by their applicability to solution- or solid-state samples, while highlighting their unique capabilities for polysaccharide characterization.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample State** & **Techniques** & **Key Features** & **Optimal Applications** \\ \hline \multirow{4}{*}{Solution-state} & \multirow{2}{*}{1D \({}^{1}\)H/\({}^{13}\)C} & Rapid chemical shift assignment; & Purity assessment, monosaccharide fingerprinting (e.g., herbal polysaccharides) \\  & & simple operation & Homopolysaccharide validation (e.g., starch, cellulose) \\  & & \({}^{1}\)H-\({}^{13}\)C correlations; glycosidic linkage mapping mapping & Heteropolyosaccharide structure (e.g., microbial EPS) \\  & & Diffusion-based component separation; & interactions in mixtures (e.g., pectin–protein complexes) \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 1: NMR techniques in solid-state and solution-based samples for polysaccharides.
# The Influence of Immersive and Collaborative Virtual Environments in Improving Spatial Skills
## 2 Literature Review
### Multi-User Collaborative Applications  
In our review of the literature, the earliest platform we found for the development of multi-user VR applications was described by [PERSON] [61]. The DIVE platform permits a high number of users to participate and interact in the same virtual environment. Since then, the development of scalable architectures that can be adapted to virtual environments has been a great challenge in spite of the advances in computing [62]. However, such applications are now being developed. For example, [PERSON] et al. [63] presented a prototype of a networked virtual environment that centered on the combination of a Minority Game [64] and an Online Induction Algorithm with lossless data transfer functions. Furthermore, many popular games on consumer VR headsets, such as the Meta Quest 2, allow users to play together remotely over the internet with their avatars inhabiting the same virtual space and the capability of audio communication. Two examples of such games include \"Rec Room\" and \"Walkabout Mini Golf.\"  
Beyond entertainment, virtual collaborative environments have the potential to enhance skills training and productivity [27]. User experience is also highly important in virtual collaborative environments [65]. Although other researchers have created spatial skills training applications using immersive VR, as described in Section 2.2, we were not able to find any examples of such applications which leveraged a multi-user collaborative environment for this purpose. Thus, we consider our application to be novel in the sense that it leverages a virtual collaborative environment for a spatial skills training activity.
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 6 Proposed Calculation Method of Equivalent Blockage Ratio  
The above analysis in this paper shows that the blockage effect will be significantly affected by the location, number, and height of the surrounding models, and this mechanism is very complex. It is difficult to propose a correct method for the blockage effect in a wind tunnel test for grouped buildings. However, it is of great significance to put forward a calculation method to evaluate the severity of the blockage effect, which can help researchers determine the scale ratio of wind tunnel test models. The calculation method for the blockage ratio will be presented with the drag coefficient as the topic for the following sections of this paper.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 6 Methodology
### Outlier Detection  
The detection of the outliers is univariate. First, let us represent the dataset as a matrix. The dataset matrix is transposed so that the rows represent the features (the attributes) and the columns are the samples. This is because the outliers can be found in the feature set of the original dataset (samples of the transposed dataset). Figure 2 shows an example of PPFS processing.  
After transposing the dataset matrix, the PP index is chosen (either Friedman or Kurtosis) as well as the optimization method (either GA or Tribes). Then, the number of iterations and the number of runs are set (defined in the next section). Later, the optimization is applied to find the best projection \(a\) (see Section 4) associated with the best value of the PP index \(I(a)\). The next step is to determine the outliers. For that, purpose the coordinate of the projected data are calculated according to Equation (1). In order to point out an observation (in the transposed dataset matrix) as an outlier, the rule based on k-sigma principle was applied. In fact, an observation is considered as an outlier when its distance to the mean is larger than 3 times the standard deviation of the projected data. Thus, the parameter k is set to 3 based on the recommendation of [37,38].  
The optimization methods are performed several times (number of runs) to ensure that the obtained result is the best and cannot be changed. When selecting the best projection and determining the outliers, a threshold is set to confirm the outlying status  
Figure 2: An example of PPFS Processing.  
of the recognized outliers. This threshold is set to half the number of runs. In fact, the proposed framework checks how many times these observations were detected as outliers throughout the runs. Thus, each outlier is associated with the frequency number showing how many times it was detected as outlier. In case of the previous example displayed in Figure 2, the last observation of the transposed matrix can be considered as an outlier because it is far from data points as displayed in Figure 3.  
By applying PPFS, suppose that the number of runs is set to 100, and the two samples number 8 and 9, from the previous transposed dataset matrix example (see Figure 2) are detected as outliers. Therefore, the proposed technique will detect these two outliers with the number of times they were recognized as outliers after performing 100 runs. The sample number 8 will not be detected as an outlier, or it will be detected but with a frequency number less than the threshold (=50), while sample 9 will be detected with a frequency greater than 50 as it is considered as outlier. This frequency number ensures the status (outlier or not) of each observation. In this study, an outlier is confirmed if it is detected more than the fixed threshold (half the number of runs). The list of confirmed outliers corresponds to the non-relevant features in the original dataset.
## 3 Testing Experiment
### Comparison and Evaluation of EFSDAF with STARFM, STRUIM, and FSDAF  
In this study, we compared EFSDAF with three other fusion methods: STARFM, STRUM, and FSDAF. Because these three fusion methods have been widely used, and each of them only needs one pair of Landsat and MODIS images at \(T_{B}\) and one MODIS image at \(T_{P}\) as input data.  
All fusion images are qualitatively and quantitatively evaluated by comparing the predicted Landsat-like image with true images at \(T_{P}\). The qualitative evaluation is to visually compare the predicted images with the true images, and the differences between the predicted images of the four fusion methods are magnified and highlighted by using yellow elliptical circles and black squares. Representative assessment indexes as quantitative evaluation are used to objectively evaluate the predicted images and true images pixel by pixel, and they are Average Difference (AD), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Structure Similarity (SSIM) [45], respectively. The evaluation criteria are as follows: the closer AD and RMSE are to 0, the closer the predicted value to the true value; the closer CC and SSIM are to 1, the more similar the predicted images are to the true images.
## 3 Pile-Soil Numerical Model
### Pile-soil Numerical Model  
#### 3.2.1 Model and Boundary Conditions  
The finite difference (FD) software [30] was used to establish a geometric model identical to that of the actual engineering project, simulating the interaction between new piles, soil, and existing piles. Figure 3 shows the dimensions of the model and pile distribution used for the study analysis (length of the existing pile: \(h\) is 42 m, diameter of the existing pile: \(d\) is 1 m). The stratigraphic conditions of the model were established based on actual borehole data, as shown in Table 1. The pile foundations are divided into the existing pile under the original bridge and a new pile, consistent with the actual working conditions. All displacements at the bottom of the model are constrained, and normal displacements at the side boundaries of the model are restricted. The pile heads of the new and existing piles are connected by a pile cap with dimensions of 12 \(\times\) 6 \(\times\) 2 m (length \(L\)\(\times\) width \(B\)\(\times\) depth \(H_{\text{p}}\)). The spacing between the new pile and existing piles is set to 2\(d\).  
Figure 3: Three-dimensional numerical simulation model.  
Figure 2: Pile-soil-pile interaction in the pile group foundation.  
The soil behavior in the model was assumed to follow an elastic-plastic constitutive relation based on the Mohr-Coulomb criterion, which is commonly applied in analysis models of both new and existing piles [6; 10; 30; 31; 32], and is widely used in rock and soil materials and has also been applied in the analysis models of new and existing piles in recent years [33; 34; 35], and could effectively reflect the pile-soil interaction. The material characteristic parameters of the soil layers used in the model are shown in Figure 4. The piles and pile caps are modeled as elastic solid elements [36]. The model parameters are based on measurements from the actual engineering project, and the specific pile parameters and conditions are summarized in Table 2.
# Targeted Protein Profiling of In Vivo NIPP-Treated Tissues Using DigiWest Technology
## 3 Results  
### Establishment and Evaluation of DigiWest from FFPE Samples after In Vivo NIPP Treatment  
Due to the very small sample size of cervical punch biopsies, multiplex protein profiling from FFPE tissue obtains valuable molecular insights into NIPP treatment. To analyze protein expression levels after in vivo NIPP treatment we established DigiWest technology to FFPE tissue slides before (\(n\) = 5 patients), directly after (0 h; \(n\) = 3 patients), and 24 h after in vivo NIPP treatment (\(n\) = 4 patients). The tissue specimens used weremounted on slides for histopathological assessment, and the tissue area ranged from 10 mm2 to 400 mm2 (Figure 1).  
Using DigiWest, 69 analytes covering apoptosis machinery, DNA damage response (DDR), and cell cycle control were analyzed. Resulting median fluorescence intensity (MFI) values were compared between samples before, 0 h after treatment, and a control, as well as 24 h after treatment and a control. Twenty-nine antibodies delivered a detectable signal in all samples. After median centration and log2 transformation, hierarchical clustering (complete linkage, Euclidean distance) revealed a similar protein expression pattern of all preNIPP samples (Figure 2). Yellow indicates a high signal level, whereas blue indicates a low signal level when compared to the median of all samples. Samples are clustered horizontally and analytes are clustered vertically. Most of the samples 0 h and 24 h postNIPP have a similar protein expression profile. This indicates a good sample quality and protein yield.