esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 XR Technologies and Inclusive Education  
### How Can XR Technologies Increase Inclusion?  
Extended reality (XR) technologies offer innovative solutions to the most important challenges faced in inclusive education. By creating immersive and interactive learning environments, XR technologies can address the diverse needs of all students, making education more accessible and engaging.  
XR technologies can create tailored learning experiences that adapt to individual students' needs. For example, VR can simulate real-world scenarios that help students with disabilities practice life skills in a controlled environment. AR can provide real-time translations for non-native speakers, while MR can offer interactive lessons that adjust to different learning styles and paces. Additionally, ontology-based frameworks can further enhance these adaptive capabilities by providing structured knowledge representation. Ontologies enable the mapping of learner profiles to specific educational content, ensuring that the learning experience aligns closely with individual needs and goals. Studies such as [PERSON] [26] have explored how ontology-driven approaches can support adaptive learning and personalized educational environments.  
Also, the immersive nature of XR can significantly boost student engagement and motivation. Students are more likely to participate actively and retain information when they can interact with 3D models, virtual simulations, and augmented content. This is particularly beneficial for students with attention deficits or those who struggle with traditional learning methods.  
XR technologies can make education more accessible for students with physical, sensory, or cognitive disabilities. For instance, VR can provide virtual field trips for students who cannot physically visit certain locations. AR can overlay digital information onto real-world objects, aiding students with visual impairments. MR can create interactive environments that facilitate hands-on learning for students with various disabilities.
###### Abstract
This paper explores statistical techniques using n-gram statistics, word frequency, and the TF-IDF measure because these techniques require no domain knowledge. The paper also explores a rule-based approach adopted from cognitive network science [19], which represents domain knowledge as a network of conceptual associations and enables greater contextual information. * The purpose of sentiment analysis is to determine the degree of positivity, negativity, or neutrality of transcripts that provide insight on analyst opinions towards insurers. This work uses the financial lexicon provided by [PERSON] [PERSON] [28] (referred to as the \"[PERSON] lexicon\") to identify sentiment. This lexicon was based on [PERSON]'s approach [18], i.e., attribute a sentiment score to each word in a text and then compute an overall sentiment score for the whole text based on single scores and simple grammatical rules. The [PERSON] lexicon was specifically developed for data mining financial articles to address the fact that almost 3 out of 4 negative words in common language are typically not rated negative in a financial context (see [28]). [PERSON] and colleagues [29] have shown that the [PERSON] lexicon outmatched [PERSON] and other approaches in predicting the sentiment of financial texts. Importantly, the [PERSON] lexicon provides predictions of the polarity of the content (i.e., whether it contains mainly positive or negative sentiments) and a subjectivity score that assesses the extent to which the text data is based on facts or opinions. * LDA is a family of unsupervised learning algorithms that identify hidden relationships between words used across several documents. No prior knowledge of topics or themes is usually required and LDA can infer collections of words consistently cooperearing together across documents, i.e., topics [4]. This paper examines whether LDA can identify similar firms based on the identified topics.
## 3 The System Performance
### Maximize Capacity  
In this section, we will find the PS factor to maximize capacity. The overall capacity from source to destination can be given by  
\[C_{s,d}=\frac{1}{2}\log_{2}(1+SNR)=\frac{1}{2}\log_{2}\left[1+\frac{\eta\rho( 1-\rho)\psi|h_{sr}|^{2}|h_{rd}|^{2}}{\eta\rho|h_{rd}|^{2}d_{sr}^{m}+(1-\rho)d_ {i}^{m}}\right]. \tag{18}\]Therefore, to maximize capacity, from Equation (18), we can see that \(\max(C_{s,d})\Leftrightarrow\max(SNR)\).  
From Equation (8), \(SNR\) is a concave function of \(\rho\), which, maximizing the \(SNR\), can be given as follows:  
\[\rho^{*}=\frac{1}{1+|h_{rd}|\sqrt{\frac{h_{rd}^{max}}{d_{i}^{max}}}}. \tag{19}\]  
**Theorem 2** (EC--Closed Form).: _The closed-form expression of the EC in the maximize mode of the system model can be expressed as_  
\[C_{AF}=\frac{1}{\ln 2}\sum_{v=1}^{N_{p}}\omega_{v}\frac{1-F_{SNR_{\max}}(x_{v})}{ 1+x_{v}}. \tag{20}\]  
Proof of Theorem 2.: See Appendix B.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 4 Discussion
### Comparison of PA Imaging with Other Imaging Modalities
#### 4.1.3 PA Imaging Versus Histology  
[PERSON] et al. [35], [PERSON] et al. [110], and [PERSON] et al. [113] validated their findings from the in vivo PA images with the corresponding histopathology images. [PERSON] et al. [35] found that the average size deviation of the lesions detected in PA imaging with respect to the corresponding lesions detected in histopathology was 28%. For six cases out of 29, they [25] compared PA images with the corresponding histopathology images. They observed a good correlation between PA and the corresponding histology images in terms of lesion shape and size. Out of the six cases detected by [PERSON] et al. [110] using in vivo PA imaging, for five cases the results were consistent with that of the final pathology. From these studies, it was observed that in addition to lesion size, lesion shape and appearance of PA images were also correlated with histology. [PERSON] et al. [113] reported typical PA-US and histopathological differences between triple-negative breast cancers (TNBCs) and luminal-A (LUMAs) molecular subtypes. In case of TNBCs, they observed rich internal findings and relative lack of external peripheral zone findings in PA-US feature scores. LUMAs showed more prominent external peripheral zone radiating vessels but reduced PA-US internal feature scores. Among PA-US features, they found significant correlations between PA-US internal feature scores and the secondary histopathological indicators such as continuous number of mitoses, estrogen receptor (ER) status and Ki-67 percentages.
## 2 Multi-Area Interconnected Power System with the HESS  
Interconnected power system is usually composed of several control areas. Each area is connected to another by a tie-line for power exchange under normal operating conditions to overcome the mismatch between the generation and the demand [17]. Under any load change disturbance, there are two control aims in the operation of interconnected power system model:  
* One objective is to make sure that the deviation of load frequency is zero or fluctuate in a certain really small range near zero.
* Another objective is to ensure that the switching power of the tie line returns to the set value.  
Taking the four-area interconnected power system as an example, a new topology diagram of interconnected power system with the HESS is presented in this paper which is shown in Figure 1[6]. The regional power system consists of three parts: generation system, user load and the HESS. In Figure 1, \"G\" represents the generation system, \"L\" indicates the user load and the \"H\" denotes the hybrid energy storage system (HESS).
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Limitations of the Proposed Framework  
While the proposed framework demonstrates strong performance across various benchmarks, it is important to acknowledge its limitations.  
The Offline Keypoint-Map Tracking method is explicitly designed for scenarios where the camera's trajectory is known beforehand. Its primary weakness is its high sensitivity to deviations from the predefined path. The online tracking phase relies on retrieving keyframes from the map based on the camera's predicted proximity to the precomputed path. If the camera deviates significantly from the expected trajectory, the retrieved 3D points will not align well with the live view, resulting in matching failures and a loss of tracking. This makes the offline approach unsuitable for exploratory navigation or situations where the path cannot be guaranteed.  
A core characteristic of the framework is its reliance on a static, pre-built LiDAR map. This presents a limitation in dynamic real-world environments. In its current form, the system does not explicitly model or account for dynamic objects. Consequently, significant changes in the scene can introduce discrepancies between the static rendered view and the live camera feed. This may affect the reliability of keypoint matching, especially if the dynamic elements occlude a large portion of the static background. While the RANSAC-based pose estimation and the IMU data provide inherent robustness to a certain number of outlier matches, widespread or drastic changes to the environment could degrade tracking accuracy or lead to temporary failures. Addressing this would require online map updating capabilities, as noted in our future work. Similarly, the system is susceptible to unexpected occlusions not present in the original map, such as a person walking directly in front of the camera. Such events can temporarily reduce the number of visible, valid keypoints, and if the occlusion is substantial, it could compromise the tracker's stability until a clear view is re-established.  
Both of our methods use IMU data to predict the camera's pose, which serves as a strong prior for the vision-based matching step. While this improves efficiency and robustness in most cases, the system can fail if there is abrupt, unexpected motion that is not well predicted by the IMU. A poor initial pose guess can prevent the system from finding enough correct 2D-3D matches, leading to tracking failure.
## 2 Proposed Antenna Structure
### Antenna Design and Configuration
#4, the back side of the structure was added with a quadrilateral shape, as shown in Figure 2, to enhance the 3 dB AR band. A quadrilateral-shaped parasitic strip was utilized to balance the electric-field magnitudes of both vertical and horizontal components to make them achieve the same value with a 90\({}^{\circ}\) phase difference between the two sides. Figure 3: S\({}_{11}\) outcomes of the designed four antennas. Figure 2: The antenna design improvement procedures. them for the CP generation. By adding the parasitic strip, the path of the electric current was increased in the antenna that shifted the frequency band. Furthermore, the CP mode was generated at a lower frequency band from 4.48 to 6.11 GHz, as shown in Figure 4.  
The CP generation behavior could be understood by displaying the antenna distribution current at different orthogonal phases at 5.8 GHz, as presented in Figure 5. It is shown that at 0\({}^{\circ}\) the current was generated along the positive Y-direction. At the 90\({}^{\circ}\) phase, it was radiated along the negative X-direction, while at 180\({}^{\circ}\) and 270\({}^{\circ}\), the currents were generated in the negative Y- and positive X-directions, respectively. Moreover, we concluded that the current rotated in a counter-clockwise direction.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Algorithms
#### Joss  
The JOSS [107] (joint sparse spectrum) algorithm is based on the TROIKA algorithm. The key difference, as shown in the Figure 18 is that it employs joint sparse signal recovery to estimate the spectra of multiple measurement vectors comprising of several PPG signals in addition to simultaneously recorded acceleration signals.  
1. Joint sparse signal recovery: Using the MMV model (multiple measurement vector), the spectra of the PPG signal and acceleration signals are jointly estimated. The MMV model provides unique solutions and better error performance compared to the SMV model (single measurement vector).
2. Spectral subtraction: A simple spectral subtraction method is applied to remove the spectral peaks of MA from the PPG spectrum. The maximum values of spectral coefficients in the acceleration spectra are subtracted from the corresponding values  
Figure 17: The TROIKA framework for HR estimation: The three main steps are signal decomposition, sparse signal reconstruction and spectral peak tracking.  
in the PPG spectrum. Thresholding is then performed to zero out coefficients below a certain level.
3. Spectral peak tracking: Compared to the TROIKA algorithm, a new phase of peak discovery is introduced. After the initialization of the peak, it is refined and validated based on previous estimates and the expected range of heart rate changes.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 5 Discussion
### Effect of Haptic Feedback  
In Study 2, haptic feedback enhanced the reaction time when switching from the Primary Task to the Secondary Task. Haptic feedback also improved the user experience and reduced the workload (as summarized in Table 3). Users reported that haptic feedback was more salient and easier to perceive than visual feedback, prompting immediate reactions when provided. The efficacy of haptic feedback can likely be ascribed to the considerable cognitive load imposed by the visual search in the Primary Task, which could potentially divert users' attention away from the visual guidance arrow. Given that haptic feedback introduces a distinctly different sensory modality for direction reminders in the Secondary Task, users can respond more promptly to the haptic guidance.  
Thus, haptic feedback could prove to be an invaluable interaction design resource in situations where users are extensively engaged in visually demanding tasks. Such scenarios often require users to concentrate their limited visual attention on a primary task in a specific direction, raising the possibility of them overlooking critical visual information from other directions. Instances of these scenarios include situations where individuals are walking, driving, cycling, or working in the field, with their visual attention primarily directed forward. In these circumstances, haptic guidance techniques could elicit quick responses, and thereby enhance safety.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Static Arrow** & **Dynamic Arrow** & **Improvement (\%)** \\ \hline Reaction Time & 5.87 s & 4.66 s & 20.61\% \\ Selection Time & 3.67 s & 3.32 s & 9.54\% \\ Head Rotation Angle & 152.26\({}^{\circ}\) & 141.06\({}^{\circ}\) & 7.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Static and Dynamic Arrows for Secondary Task in Study 1.  
In our experiment, haptic feedback served as guidance for users to effectively transition from the Primary Task to the Secondary Task, improving user reaction time without compromising the performance of the Primary Task. This discovery supports the concept that in dual-task settings, tasks requiring different sensory modalities could potentially outperform tasks that employ the same sensory modality [19]. Our findings align with those of [PERSON] et al. [22], which demonstrated that haptic feedback in a secondary task, such as slide controlling, improved overall performance. Furthermore, although visual cues could potentially diminish performance in easy visual target acquisition tasks [65], our results suggest that haptic feedback could serve as an effective alternative in such cases.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 1 Introduction
First, we enhanced the standard PSO algorithm by grouping particles based on their exploration and exploitation functions and applying dynamic inertia weight mechanisms, with the grouping ratio adjusted in real-time during iterations. The performance of the IPSO algorithm was validated using the CEC2022 benchmark functions, demonstrating improved convergence accuracy and stability compared to conventional PSO algorithms. Next, ten high-impact recorders were used in impact experiments, and the experimental data were split and expanded to create training and test datasets. During the training process, the IPSO algorithm was used to optimize the SVM penalty and kernel parameters. Finally, the model's accuracy was validated using the test dataset.
## 3 Finite Element (FE) Investigation
### Mesh Sensitivity Analysis  
It is well known that FE analysis involving fracture behavior is strongly dependent on the mesh size due to the strain localization during the material softening. The mesh sensitivity analysis is carried out with three mesh sizes of 0.35 mm, 0.50 mm, and 0.75 mm in a region where fracture would initiate and propagate. Figure 9 shows the load-displacement curves obtained from FE analysis with different mesh sizes. It was found that there is a noticeable effect of mesh size on the fracture response. The 0.35-mm fine mesh exhibited a  
Figure 8: Locus of the calibrated fracture initiation.  
Figure 7: Relationships between fracture parameters: (**a**) Uniaxial tension specimen; (**b**) In-plane shear specimen.  
sudden drop of load capacity at smaller fracture displacement than the comparable one of the 0.75-mm coarse mesh. In this sense, the smaller size of the mesh is associated with the fracture initiation at an earlier stage. In any case, the 0.50-mm medium mesh, which shows a good agreement with the experimental load-displacement curve, was selected for further fracture simulations. The use of this element size can be justified from the outcome of [PERSON] et al.'s study [27] indicating that the mesh size of 0.5 mm could not affect the accuracy of fracture simulation.
## 5 Algorithm Design
### Joint Offloading Decision and Resource Allocation  
The joint offloading decision and resource allocation scheme based on potential game, Lagrange multiplier method, and satellite-terrestrial cooperation (JODRA-PGLMC) is proposed in this paper. \(t\) is the iteration number, \(t^{\max}\) is the maximum iteration number. \(y(t)\) denotes UEs' task offloading decisions in the \((t-1)\)th iteration. All UEs' task offloading decisions are initialized to local computing. During each iteration, each UE tries to change its task offloading decision while the task offloading decisions of other UEs remain unchanged. If the UE gets more payoff, its task offloading decision will be changed, otherwise, its task offloading decision will be unchanged. The payoff is bounded due to the limited strategy space, when no UE can gain more payoff by changing its task offloading decision, the algorithm will converge and obtain the local or global optimal solution of \(\mathcal{P}\)[34]. In this case, the game reaches the NE. \(y(t)\) is equivalent to \(y^{*}=\left(y_{1}^{*},y_{2}^{*},y_{3}^{*},\ldots,y_{N}^{*}\right)\), which is the optimal task offloading decisions of UEs, i.e., \(x_{i0}^{*}\), \(x_{iS}^{*}\), \(x_{iB}^{*}\), corresponding \(f_{iS}^{*}\) and \(f_{iB}^{*}\) are the optimal computing resource allocation. Thus, the minimum \(U\) can be obtained. The complexity of the algorithm is \(\mathcal{O}(t^{\max}N)\). The detailed steps of JODRA-PGLMC is expressed in Algorithm 1.  
```
1:Initialize \(y(1)=\{y_{i}=0,\forall i\in\mathcal{N}\}\). Set \(t=1\). Set \(t^{\max}\).
2:while\(1\leq t\leq t^{\max}\)do
3:\(t=t+1\).
4:for each UE \(i\in\mathcal{N}\)do
5: Calculate \(V_{i}(y_{i}(t-1),y_{-i}(t-1))\).
6: Obtain \(y_{i}^{\prime}\) and \(y_{i^{\prime\prime}}^{\prime},y_{i^{\prime}}^{\prime}y_{i}^{\prime\prime}\in \mathcal{Y}_{i}/\{y_{i}(t-1)\}\), \(y_{i}^{\prime}\
eq y_{i}^{\prime\prime}\).
7:if\(y_{i}^{\prime}=0\) or \(y_{i^{\prime}}^{\prime}=0\)then
8: calculate \(V_{i}(y_{i^{\prime}},y_{-i}(t-1))\) or \(V_{i}(y^{\prime\prime},y_{-i}(t-1))\).
9:else if\(y_{i}^{\prime}=1\) or \(y_{i}^{\prime\prime}=1\)then
10: obtain \(f_{iS}^{*}\) by (15), so as to calculate \(V_{i}(y_{i^{\prime}}^{\prime},y_{-i}(t-1))\) or \(V_{i}(y^{\prime\prime},y_{-i}(t-1))\).
11:else if\(y_{i}^{\prime}=2\) or \(y_{i}^{\prime\prime}=2\)then
12: obtain \(f_{iB}^{*}\) by (22), so as to calculate \(V_{i}(y_{i^{\prime}}^{\prime},y_{-i}(t-1))\) or \(V_{i}(y^{\prime\prime},y_{-i}(t-1))\).
13:endif
14: Compare \(V_{i}(y_{i}(t-1),y_{-i}(t-1))\), \(V_{i}(y_{i^{\prime}}^{\prime},y_{-i}(t-1))\) with \(V_{i}(y^{\prime\prime},y_{-i}(t-1))\), update \(y_{i}(t-1)\) to \(y_{i}(t)\) using the task offload decision corresponding to the maximum among them.
15:endfor
16:if\(y(t)=y(t-1)\)then
17: break.
18:endif
19:endwhile
20:return\(y(t)\).
```  
**Algorithm 1** JODRA-PGLMC
## 1 Introduction
### The Sentinel-2 Spectral Bands  
Table 1 gives the name and the central wavelength for each band acquired by Sentinel-2. There are four bands at 10 m resolution: the three usual visible bands (B2-B4) and a wide NIR band (B8). The 20 m resolution bands are three narrow bands in the red edge (B5-B7), one narrow NIR (B8A) and two SWIR bands (B11, B12). Finally, the 60 m resolution bands are aimed at radiometric corrections (B1 for aerosol content estimation, B9 for water vapor and B10 for cirrus detection). Figure 1 illustrates the relative spectral responses of the 10 m, 20 m and 60 m resolution bands.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Band** & **Central Wavelength (nm)** & **Spatial Resolution (m)** \\ \hline
1—Coastal aerosol & 442.7 & 60 \\
2—Blue & 492.4 & 10 \\
3—Green & 559.8 & 10 \\
4—Red & 664.6 & 10 \\
5—Vegetation red edge & 704.1 & 20 \\
6—Vegetation red edge & 740.5 & 20 \\
7—Vegetation red edge & 782.8 & 20 \\
8—NIR & 832.8 & 10 \\
8—Narrow NIR & 864.7 & 20 \\
9–Water vapour & 945.1 & 60 \\
10—SWIR—Cirrus & 1373.5 & 60 \\
11—SWIR & 1613.7 & 20 \\
12—SWIR & 2202.4 & 20 \\ \hline \end{tabular}
\end{table}
Table 1: Name and central wavelength of the Sentinel-2 spectral bands [1].  
Figure 1: Sentinel-2A relative spectral responses from [[https://sentinels.copernicus.eu/documents/247904/685211/S2-SRF_COPE-GSEG-EOPG-TN-15-0007_3.0.xlsx](https://sentinels.copernicus.eu/documents/247904/685211/S2-SRF_COPE-GSEG-EOPG-TN-15-0007_3.0.xlsx)]([https://sentinels.copernicus.eu/documents/247904/685211/S2-SRF_COPE-GSEG-EOPG-TN-15-0007_3.0.xlsx](https://sentinels.copernicus.eu/documents/247904/685211/S2-SRF_COPE-GSEG-EOPG-TN-15-0007_3.0.xlsx)), accessed on 10 May 2022.