esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings and Discussions
### Research Trends and Patterns  
#### 4.2.1 Analysis of the Titles  
t-SNE is an unsupervised \"nonlinear dimensionality reduction technique that aims to preserve the local structure of data\" [14] (p. 2580), used for exploring and visualizing high-dimensional data. To reveal the foci of the publications, t-SNE analysis was conducted  
Figure 1: Time trend of AI in online distance education publications.  
using textual data extracted from the titles of the publications included in the research corpus (Figure 4). Accordingly, there are three dominant clusters. The green cluster highlights that AI technologies are used in online teaching and learning processes. The patterns in the green cluster are in line with the Horizon Report [1], which highlights the widespread use of AI technologies, especially in higher education, and further reports that \"AI is appearing throughout higher education teaching and learning, in domains such as learning management systems, proctoring, grading/assessment, student information systems, office productivity, library services, admissions, disability support, and mobile apps to name a few\" (p. 13). The blue cluster indicates how algorithms are used for the recognition, identification, and prediction of students' behaviors. For instance, [PERSON] [22] studied emotion recognition in college students' online learning engagement and reported that deep learning techniques can be effective. [PERSON] and [PERSON] [23] argue that deep learning techniques through face recognition can be used for m-learning online exam proctoring. [PERSON] et al. [24] found that academic emotion recognition can be performed in online learning environments as a psychological indicator of the learners. [PERSON] et al.'s [25] study argued that learning performance can be predicted by using machine learning techniques. Likewise, [PERSON] et al. [26] also reported that machine learning techniques can be used effectively to predict learning outcomes in a blended learning environment. Finally, [PERSON] and [PERSON] [27] found that machine learning can be used for early dropout prediction among learners in online learning environments. The final, pink cluster is about adaptive and personalized learning empowered through artificial intelligence technologies. [PERSON] and [PERSON] [28] noted that machine learning can be effectively used for enabling personalized learning. [PERSON] et al. [29] argue that artificial intelligence technologies promise a lot for adaptive learning and, confirming this view, [PERSON] et al. [30] found that deep learning techniques demonstrate a significant effect on adaptive learning. In brief, all these clusters indicate that artificial intelligence can be used for online distance education and further imply that there is an algorithmic future that requires approach with caution and conducting of more multidimensional research before fully integrating these technologies into online distance education processes.  
Figure 2: Geographical distribution of AI in online distance education publications.
## 3 Results and Discussion
### Qualitative Results  
In the original CT images of soil, pores appeared as black regions, signifying areas of interest for segmentation. Other soil constituents, such as solid particles, were represented in shades of gray and white. Figure 5 illustrates the segmentation outcomes from various network models. Although the results from the majority of the models are predominantly precise, a number of them tend to underestimate the pore size to different extents when  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Soil Type** & **Number of Images** & **Accuracy** & **Precision** & **Recall** & **F\({}_{\text{1}}\)-Score** \\ \hline Thawed & 3 & \(98.93\pm 2.94\times 10^{-3}\) & \(90.74\pm 1.90\times 10^{-1}\) & \(92.68\pm 6.16\times 10^{-1}\) & \(91.46\pm 1.76\times 10^{-1}\) \\  & 5 & \(98.89\pm 3.03\times 10^{-3}\) & \(90.11\pm 1.97\times 10^{-1}\) & \(92.74\pm 6.14\times 10^{-1}\) & \(91.17\pm 1.80\times 10^{-1}\) \\  & 7 & \(98.95\pm 3.03\times 10^{-3}\) & \(90.83\pm 2.05\times 10^{-1}\) & \(92.85\pm 6.09\times 10^{-1}\) & \(91.59\pm 1.81\times 10^{-1}\) \\  & 9 & \(98.78\pm 3.28\times 10^{-3}\) & \(88.30\pm 2.28\times 10^{-1}\) & \(93.02\pm 6.09\times 10^{-1}\) & \(90.37\pm 1.91\times 10^{-1}\) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 4: Qualitative comparison of different numbers of sequence images: (**a**) original soil CT image, (**b**) ground truth, (**c**) three images in sequence, (**d**) five images in sequence, (**e**) seven images in sequence, and (**f**) nine images in sequence.  
contracted with images that have been manually adjusted. Given the variability in soil composition and the heterogeneity of pore distributions, numerous gray values within the soil CT images may confound the models' assessments.  
Figure 5: Qualitative comparison of different models, where rows represent different models and columns represent different conditions.  
Certain small pores are not well segmented and are predominantly overlooked, which can be ascribed in part to the diminished representation of pore structures in the soil images. The absent pores are denoted by circles in Figure 5. For elongated, strip-shaped pores, only the proposed method successfully connects them in a manner akin to the calibration chart, whereas alternative methods yield fragmented results. Qualitatively, the experimental outcomes suggest that BDULSTM offers superior segmentation, capturing the smallest pores and providing results that visually approximate those of the manually refined images most closely.
# Numerical Analysis on the Performance of a Radiant Cooling Panel with Serpentine-Based Design
## 3 Results and Discussions
### CFD Simulation--ANSYS Fluent
#### 3.3.3 Cooling Capacity  
The cooling capacity of the RCP for each of the flow configurations was analyzed and calculated by using Equations (13) and (14), as given in Section 2.3. The cooling capacity formula was taken from ASHRAE Transactions [29], where the apparent heat is removed from the room by convection and radiation, which is absorbed by the RCP. Therefore, the cooling capacity, the Q\({}_{\text{cool}}\) of the RCP can be determined by calculating the total heat flux absorbed through radiation and convection.  
\[\text{Q}_{\text{cool}}=\text{q}^{\prime\prime}{}_{\text{r}}+\text{q}^{\prime \prime}{}_{\text{c}} \tag{15}\]  
Based on Table 9, Design 2 had the highest cooling capacity compared to the other two designs. Design 1, which is the common serpentine flow configuration, had the lowest cooling capacity, which was 19.752 W/m\({}^{2}\) smaller than that of Design 2. Design 2 is created from a heat exchanger perspective, where counterflow is more efficient than a parallel flow. By implementing this idea into the RCP where the inlet and outlet are located on the same side of the plate, a significant improvement of its cooling performance was achieved. The cooling performance of the RCP agrees with [PERSON] et al. [1], where the counterflow design group is better than the conventional flow design group. Therefore, the arrangement of pipe configuration can have a significant impact on its cooling performance.  
Figure 13: Temperature distribution of the cooling panel for Design 3.  
In addition, the heat transfer characteristics obtained also agree with [PERSON] et al. (2019), where the heat absorption consists of 2/3 radiation and 1/3 convection of the total heat transfer.
## Materials and Methods  
A non-probability consecutive sampling procedure was used within 12 weeks, from January to March 2018, among patients attending a dental clinic in Rome (Italy) with a diagnosis of plaque-induced gingivitis, according to the American Academy of Periodontology (AAP) guidelines [2]. The sample size was estimated using a finite population correction (FPC) considering the Lazio population with a mean age assimilable to our sample and a gingivitis prevalence of 20%, according to AAP 2017 workshop data [2].  
Each patient underwent an anamnestic and clinical examination after signing an informed consent to study participation. The study was conducted in accordance with the Declaration of Helsinki of 1975 and was approved by the local Ethical Committee (prot. nr. 1216/19). The exclusion criteria were pregnancy, presence of hematologic pathologies, current anticoagulant therapy, presence of diagnosed autoimmune gum diseases and refusal of study participation.  
All patients' full mouth plaque score (FMPS) and gingival index (GI) [13] were measured and recorded at the first visit (t0). FMPS was performed by applying a fluorescein plaque detector and using VALO LED curing light, according to the Dental Biofilm Detection Topographic Technique (D-Biotech) protocol, in order to achieve a more accurate and ergonomic deplaquing. Patients were randomly divided into a study group and a control group; both received full mouth scaling and were educated as concerns a home oral hygiene method based on the Tailored Brushing Method (TBM) [14]. The study group received indication to brush their teeth once a day without time specification using a full brush (1 cm, approx. 0.5 g) dental gel with chlorhexidine digluconate (0.2%) (Cervitec Gel, Ivoclar Vivadent, Schaan-Liechtstein), and to avoid drinking and eating for at least 30 minutes after the application, while the control group were provided with a chlorhexidine-free gel (placebo). Both gels contained sodium fluoride (0.2%). The two gels were identical in taste, color and texture. Neither the researcher nor the patients were aware of the group allocation.  
Every patient was asked to not use any kind of other toothpaste or mouthrinse and was told to report any adverse effect.  
FMPS and GI were measured after 1 week (t1), 2 weeks (t2) and 3 weeks (t3) by the same operator (GMN).
# Influence of Posture Change on Train Running Safety under Crosswind
## 2 Numerical Methodology  
### Model  
Figure 1 shows the train employed in the present study, which is the 1:1 scale Chinese CRH3 high-speed train. To display the difference in the aerodynamic performance of different cars, a leading car, a middle car, and a trailing car were included in the 3-car train; their lengths are 25.78 m, 25.33 m, and 25.78 m, respectively, which makes the entire train 76.9 m long. The height (H) and width of the train are 3.89 m (H) and 3.26 m, respectively. Some geometric details on the train, such as doors, windows, handles, and so on, were ignored to increase the efficiency of grid generation and numerical simulation, while those geometric features that could theoretically affect the train's dynamic response, such as bogies and inter-carriage gaps, were retained. To replicate the ground clearance provided by the railway track, the train's lowest end (the lower vertex of the bogie wheelset) was given a 0.2 m distance from the ground.  
Based on previous research, different running posture changes were found when the leading car, the middle car, and the trailing car passed through the crosswind area [26].  
According to their actual change trends through the wind zone, the posture changes in different degrees of freedom, as in Table 1, are applied to different cars. Note that these attitude changes cannot be accurately assigned due to differences in train geometry, dynamic models, and yaw angles. However, for a more convenient comparison, the rotation angle of each vehicle is increased as a fixed attitude, which is in contrast to the working condition that does not consider the posture change.
# Additive Orthant Loss for Deep Face Recognition
## 1 Introduction
Based on the above properties, we can see that it is necessary to increase the feature magnitude to find an optimal one as well as to improve cosine similarity. In this paper, we propose an additive orthant loss function, termed the Orthant loss, to improve the inter-class separability and the intra-class compactness. The Orthant loss improves the inter-class separability by increasing the distance between features and the origin using the rescaled softplus function and an additive margin. Additionally, the 1-bit quantization and element-wise operation of the Orthant loss simultaneously improve the intra-class compactness of each feature space by attempting to make the features of the same identity have the same signs. In this paper, the proposed loss function is termed as the Orthant loss, since we try to map features of the same identity to an orthant where the center of the corresponding class is located. The Orthant loss has two major differences from the Center loss [25]. First, the Center loss requires an additional fully connected layer to learn the center location of each class, whereas the Orthant loss does not require the layer. In addition, the Center loss learns the distributions of all features to gather at the center of each class without considering the feature magnitudes, while the Orthant loss learns the distributions to efficiently utilize the feature spaces by considering the feature magnitudes. Since our new loss function has no network modification, it is compatible with many of the latest softmax-based loss functions. Thus, we construct a final loss function by combining one of the lastest softmax-based loss functions [33; 35] with the Orthant loss. The experimental results show that a new loss function combining the ArcFace and the Orthant loss together, termed the ArcOrthFace, has the better accuracy compared to softmax-based loss functions, e.g., Center loss [25], SphereFace [28], ArcFace [33] and MagFace [35], respectively, on CFP-FP [40], AgeDB-30 [41] and MegaFace [42] testing datasets. Additionally, we confirm that the Orthant loss combined with the softmax-based functions [22; 33; 35] improves the recognition accuracy in most benchmarks. In conclusion, the Orthant loss, combined with the latest softmax-based functions, can improve the accuracy of deep face recognition by enhancing the inter-class separability and the intra-class compactness of feature spaces.
Further, all the letters from Daria are in \"DysARthrIA\" and in the same order. This choice was informed by emerging research indicating the potential of nonverbal vocalizations in enhancing interaction for individuals who have speech impairments [15; 27]. Daria is a custom-developed system that allows for interaction with SVAs by using nonverbal voice cues, offering a more straightforward, shorter, and less fatiguing alternative to traditional speech commands. For example, users can simply make the sound /a/ (\"aaa\") to turn on lights, which is significantly simpler than uttering complex sentences such as \"[PERSON], turn on the lights\". Daria is programmed using five distinct nonverbal voice cues, each mapped to a specific action. This mapping includes /a/ for lights, /i/ for news, /p/ to initiate a call, humming for music, and /u/ for weather updates. This design ensures ease of control and enhanced accessibility, particularly for users who have severe dysarthria, allowing them to perform a variety of tasks using minimal effort. Prior studies have been conducted on the design of Daria [15; 27], underscoring its primary goal of empowering individuals who have dysarthria. The system's design involved collaboration with individuals diagnosed with dysarthria, ensuring that Daria is sensitively and effectively attuned to their specific communication challenges and preferences. 3. Eye gaze control. This method employs eye gaze control by which users control a tablet connected to the SVA using only their eyes. These methods were chosen for their potential to accommodate the varying communication capabilities of individuals who have dysarthria, ensuring a broad and inclusive approach to interaction with SVAs. Understanding the strengths and limitations of each method enables us to enhance and optimize these technologies. Usability measures for each modality were assessed according to the International Organization for Standardization (ISO), which defines usability as the \"extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use\" [28]. Furthermore, the workload required for each interaction was also evaluated. Finally, this study aimed to evaluate and compare the usability of voice commands, nonverbal voice cues, and eye gaze interactions for individuals who have dysarthria. By doing so, it sought to offer comprehensive insights into the most effective and user-friendly methods of interaction for this population, thus contributing to the development of more accessible and efficient communication technologies.
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
## 3 Results and Discussion
### Aggregate Effect on Compressive Strength
These properties may indirectly affect compressive strength because they affect the water content, which then affects the workability of the concrete. In contrast, these properties may also directly affect the compressive strength of concrete because they affect the adhesion between the aggregate and the cement paste, stress concentrations during loading, and microcrack patterns [10]. Thus, in this study, the magnitudes of the effect factors of each aggregate type on the compressive strength of concrete were calculated by assuming six cases: Case 1: \(0<c<b<a\); Case 2: \(0<b<c<a\); Case 3: \(0<c<a<b\); Case 4: \(0<a<c<b\); Case 5: \(0<b<a<c\); and Case 6: \(0<a<b<c\). For each of the cases used to assess the effect factors of each aggregate type, the largest factor value was set to 1, whereas the second largest factor value was between 0.01 and 0.99. These values were substituted into Equation (1) to calculate the ranges of the factors that met each assumption. As a result of analyses, we found that only Cases 1 and 6 could meet the assumption when the appropriate values were substituted into Equation (1).
## 3 Distributed Clustering Techniques
### Cluster Formation Techniques
For example, the sensor network shown in Figure 2 has been divided into five clusters: each managed by one Sink Node (represented by solid triangles in the figure). In the upper figure, the Anchor Nodes have been assigned to clusters that correspond to Voronoi cells: to identify the clusters, a different shape is used to represent Anchor Nodes within the cluster (_i.e._, rectangles, crosses, diamonds) and the Voronoi boundaries have been identified by lines. When an obstacle is introduced in the lower figure (the solid horizontal line), the clusters reconfigure in response to the resulting signal blockage. As can be seen in Figure 2, the clusters still conform to a Voronoi pattern wherever possible, but no longer include anchors that are blocked by the obstacle (these anchors are assigned to adjacent clusters). The third technique shown in Table 2 is intended to approximate the _ad hoc_ clustering techniques described in the previous section. For this technique, Sink Nodes again serve the role of cluster heads, but Anchor Nodes are temporary members of clusters that are formed for each Mobile Node tracking task. In order to determine which Sink Node is to serve as cluster head for a given tracking task, Sink Nodes initiate Vickrey auctions amongst themselves. The winning Sink Node then manages the tracking task by recruiting Anchor Nodes into an _ad hoc_ cluster. This cluster remains active until either the Sink Node does not have enough resources (e.g., wireless nodes) to track the event (_i.e._, in case the event is mobile and goes out of the detection range of the _ad hoc_ cluster), or the Sink Node decides to no longer track the event. At this point all the participating wireless nodes are dismissed and they will no longer have any commitment to the _ad hoc_ cluster (_i.e._, the transient organization).
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 4. Results
### Modal Analysis of Bridge  
The bridge is analyzed in its original state. Figure 8 illustrates the fundamental mode shapes in the actual condition. Table 4 gives the fundamental modal periods for all levels of flood probabilities of exceedance. The permanent masses of self-weight of structural elements, wearing surfaces, sidewalks, and barriers are considered. The bridge is assessed with normal traffic, and the quasi-permanent values of variable actions are taken to be equal to zero.  
Figure 7: Pounding location at the abutment under seismic actions (schematic model referring to the original drawings used in the FE model).  
Figure 8 shows that the bridge is dominated by a fundamental mode of vibration in the longitudinal direction due to the smaller stiffness of bent columns in this axis.  
As the local scour depths increase, the modal periods also exhibit a corresponding slight increase. The pile foundations of the bridge have larger stiffness values than the bent columns, and the vibration modes show low sensitivity to increasing the local scour depths.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 2 Materials and Methods
### Dataset
#### 2.4.2 The Photometric Acquisition Setup  
We set up a real photometric stereo experiment platform to collect the images needed for training, as shown in Figure 3a. The camera and the circular light frame were fixed by a frame including clamping devices to ensure that the light conditions of each acquisition were determined and consistent, and the light frame was fixed with the camera (Automation Technology GmbH, Bad Oldesloe, Germany) at its center. The arrangement of the circular light frame is shown in Figure 3b. We designed our light sources as 20 white LED bulbs of the same size (60 degrees) as the scattering angle and fixed them on a circular ring. The angle interval between each adjacent white LED bulb was 18 degrees.
# Development of Post Processing for Wave Propagation Problem: Response Filtering Method
## 3 Response Filtering Method for Improved Transient Stress
### Response Filtering Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Central-difference method & & & & & & & & \\ Filtering ratio (\%) & w/o RFM & 0.5 & 1 & 1.5 & 2 & 2.5 & 3 & 3.5 \\ Error indicator \(\xi(\times 10^{-3})\) & 1.6710 & 1.6400 & 1.5039 & 1.3723 & 1.0918 & 1.1012 & 0.9666 & 0.9809 \\ Number of over (under) shoots & 73 & 73 & 73 & 47 & 19 & 15 & 23 & 23 \\ \hline [PERSON]’s method & & & & & & & & \\ Filtering ratio (\%) & w/o RFM & 0.5 & 1 & 1.5 & 2 & 2.5 & 3 & 3.5 \\ Error indicator \(\xi(\times 10^{-3})\) & 1.6025 & 1.5811 & 1.4269 & 1.3339 & 1.1746 & 0.9752 & 0.9827 & 0.9688 \\ Number of over (under) shoots & 71 & 71 & 71 & 51 & 21 & 13 & 21 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy improvement of the RFM with different numerical integration method (at 1\({}^{\text{st}}\) element, \(\Delta t=4.0\times 10^{-7}\)s)Because the proposed method yields an artificially generated piecewise constant solution, it has to be validated not only in time but also in space. Therefore, the benchmark problem in Figure 2 was conducted once more with different space condition. Both exact and numerical stress responses at \(x=0.25\) in (1 st element) were calculated by the same procedure as the previous example. Lots of spurious oscillation was found around at second arrival time in the finite element method (\(t=\frac{20+19.75}{2.012\times 10^{5}}\)) in Figure 5. The detail performance of RFM with different filtering ratio, \(FR\), is written in Table 2. Similar to the previous example, the \(FR\) (filtering ratio) value around 2% shows the improvement in time and space. Figure 4: Comparison between the exact (analytical) solution and the filtered numerical solution (Left: central-difference method, Right: [PERSON]’s method). The responses are shown (**a**) without RFM, (**b**) with \(FR=0.5\%\), (**c**) with \(FR=1\%\), and (**d**) with \(FR=2\%\).