esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work compares several epoxide-containing solid polymer electrolytes from four main categories: polyether, silicone-containing, amine-containing and plasticizer-containing (Table 1). General trends in ionic conductivity and electrochemical stability suggest that solid electrolytes that contain only hydrocarbons tend to have relatively low ionic conductivity and electrochemical stability relative to Li\({}^{+}\)/Li. The addition of other functional groups such as silanes, siloxanes or amines seems to increase both ionic conductivity and electrochemical stability by improving crosslinking in the resultant films and/or by decreasing film crystallinity and/or by decreasing the affinity of the lithium ions for the polymer phase. However, the intervention that was shown to have the greatest impact on ionic conductivity was the addition of plasticizers which improve ionic conductivity by solvating lithium cations and by increasing the mobility of the polymer chains. However, the impact of plasticizer inclusion on the electrochemical stability of epoxide-based polymer electrolytes was not extensively discussed in literature. The relatively rigid framework of the epoxide-based polymer electrolyte provides physical support allowing plasticizers to be used while avoiding leaking which can occur with typical solvent based electrolytes. Despite the significant increase in ionic conductivity that is provided by the inclusion of plasticizers, and based on the reported properties, the overall best electrolyte that was presented in this review was determined to be the POSS-PEO-co-PEO electrolyte with LiTFSI as it had a relatively low T\({}_{\text{g}}\) (\(-43\)\({}^{\circ}\)C), a relatively high ionic conductivity at room temperature (\(1.1\times 10^{-4}\) S/cm), a high transport number (0.62) and a high electrochemical stability and was cycled in full cells with both LFP and NMC.  
**Author Contributions:** Conceptualization, G.F., D.L. and A.P.; writing--original draft preparation, G.F.; writing--review and editing, G.F., N.V., D.L., A.P. and M.D.; supervision, A.P. and M.D.; project administration, M.D. and D.A.-P.; funding acquisition, M.D. and D.A.-P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Authors would like to acknowledge funding from the National Sciences and Engineering Research Council of Canada (NSERC) and Total Canada Inc. (NSERC Collaborative Research and Development; RDCP) 528052-18).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Experts' Comments  
Using the camera's intrinsic and extrinsic parameters calculated by the calibration method in this paper, we performed 3D scene recovery in two ways: 3D mesh and MPI. Our main contributions are the pipeline and calibration system (the first and second item in Section 1.3) that enable 3D rendering (the third item in Section 1.3) in the medical imaging context. As such, we consider that asking medical doctors to provide their comments is the best way to investigate how well and in what way the generated 3D rendering performs in medical usage. We asked two medical doctors from Keio University School of Medicine to observe and provide comments on each rendering result displayed on the spatial reality display (SRD) from Sony Corporation [36], an outostereoscopic display that reproduces 3D spatial images using real-time gaze recognition and dedicated image generation algorithms. One of the medical doctors is a Prof. Dr. of the Department of Plastic and Reconstructive Surgery and the other is a Dr. of the Department of Anatomy.  
Figure 8 shows the 3D mesh models and MPIs on SRD shown to the doctors. The following summarizes the positives (P) and negatives (N) from the medical doctors.  
Figure 7: Occlusion-free rendering using two techniques. These frames contain a viewpoint where the operation field is occluded by the surgeon’s head (first row). Our free-viewpoint rendering techniques (**a**) by changing the viewpoint in 3D or (**b**) by disocclusion rendering (Equation (2)) can reveal the occluded area.  
3D mesh.
* (P1) Very close impression to what we see in actual surgeries regarding appearance and shape;
* (N1) The blind spots on the sides are sharply vertical, unlike the actual shape;
* (N2) The scale should be adjusted.  
MPI.
* (P1) Feeling of being there, however, worse than 3D mesh;
* (P2) Interesting to see \"images\" in 3D;
* (N1) Blurry and unclear;
* (N2) Only rough shapes can be grasped;
* (N3) Planes are obvious from a steep angle (i.e., stack-of-cards artifacts);
* (N4) The scale should be adjusted.  
Although MPI enables new disocclusion rendering, as demonstrated in Section 3.3, there are clear limitations. MPI has more disadvantages and one of the main reasons would be wider baselines between the cameras in McSL. One medical doctor commented that \"it is difficult to list advantages from a clinical point of view\" due to the limitations. Nonetheless, we should note that MPI can be extended to videos rather more straightforwardly than  
Figure 8: 3D mesh and MPI on Sony SRD. We showed polysyndactyly, cleft lip, and accessory auricle surgery images to medical doctors to review their comments.  
the 3D mesh [18]. As a common issue, the medical doctors must have adjusted the scale manually for individual scenes, given that the scene scale is unknown and thus different in each scene.
## 4 Construction of the Dynamic and Static Abutment Pressure Calculation Model
### Dynamic Abutment Pressure Calculation Model  
Under the action of abutment pressure, the coal body on both sides or in front of the goat tends to move to the stope and forms the stress limit equilibrium zone. According to the characteristics of coal wall stress transfer to the deep coal body after yield, a dynamic abutment pressure calculation model is established by using the limit equilibrium method, as shown in Figure 10, where \(p_{0}\) is the coal wall support resistance (MPa), \(x\) is the distance (m) between the investigation point and the coal wall, \(x_{t}\) is the distance (m) between the influence of abutment pressure, and \(x_{0}\) is the distance (m) between the peak position and the coal wall. \(N_{0}\) is the vertical supporting force of the coal wall (MPa), \(\gamma\) is the average bulk density of the overlying rock (MN\(\bullet\)m\({}^{3}\)), \(H\) is the buried depth of the stope (m), \(k\) is the stress concentration factor, and \(\sigma_{y}\) is the vertical stress in the coal body (MPa). The influence range of dynamic abutment pressure can be divided into three parts: the 0-\(x_{0}\) part is the plastic region, and the abutment pressure presents exponential function distribution. The part \(x_{0}\)-\(x_{t}\) is the elastic active region, and the abutment pressure is negative exponential function distribution. The part \(x_{t}\)-\(\infty\) is the stress region of the original rock, and the abutment pressure presents constant function distribution.  
Based on the above analysis and considering the boundary conditions, the calculation model of abutment pressure distribution can be obtained as follows:  
\[\sigma_{y}=N_{0}\mathrm{e}^{\frac{2\gamma x}{M}\left(\frac{1+\sin\vartheta}{ \Gamma-\sin\vartheta}\right)}, \tag{5}\]  
where, \(\sigma_{y}\) is the vertical stress of the coal body (MPa), \(M\) is the thickness of the coal seam (m), \(f\) is the friction factor of the layer.  
Assume that 0 is the lateral pressure coefficient, \(\sigma_{x}=\lambda\sigma_{y}\) in the elastic region, then  
\[\sigma_{y}=k\gamma H\mathrm{e}^{-\left[\frac{2\gamma L}{M}\left(x-x_{0}\right) \right]}, \tag{6}\]  
The influence distance of abutment pressure is as follows:  
\[x_{t}=x_{0}+\frac{M}{2f\lambda}\ln k, \tag{7}\]
## 4 Experimental Results
### Mission Allocation  
Mission allocation refers to the outcomes produced by optimization algorithms in response to user-requested mission assignments. In Figure 12, the mission allocation results are depicted over a single day for all test cases, spanning from 100 to 1000 targets. The scenarios involve different numbers of satellite constellations (8 to 40) and apply two algorithms, MDP and greedy. The upper graph illustrates the number of observed targets, representing the sum of instances where the binary decision variable \(x_{ijk}\) is equal to 1. In simpler terms, it reflects the total count of occasions when targets are successfully observed, excluding the profit (\(p_{i}\)) gained from observing a target in the objective function. Meanwhile, the lower plot corresponds to Equation (3), revealing the objective function value of total profit derived from target observation. This calculation includes the profit associated with each target. In essence, the lower plot provides a comprehensive overview of the objective function value, taking into account the profits obtained through capturing individual targets.  
Figure 11: Simulation workflow.  
Figure 12: Mission allocation results under all test cases.  
Figure 12 illustrates an increasing trend in mission allocation results with a growing number of targets and satellites. Notably, the MDP algorithm consistently outperforms the greedy algorithm. As the number of targets increases, the impact of an increased number of satellites on mission allocation results becomes more evident, along with the growing disparity between the MDP and greedy algorithms. Moreover, when maintaining the same number of satellites, the mission allocation results tend to converge even as the number of targets increases, suggesting a threshold beyond which adding more targets has a limited effect on the number of successfully allocated missions. For example, with eight satellites, the number of missions stabilizes at approximately 180, even as the number of targets increases to 1000. This phenomenon enables mission planners to determine the optimal number of satellites required based on the number of targets.
## 3 Results
### The AR Application  
The developed Augmented Reality application is designed specifically for use on the holographic table at Labor.A, Politecnico of Milan (Figure 4). The holographic table is an AR device based on the principle of stereoscopy, which utilizes multiple projections of 2D images and lightweight glasses to create visual 3D artifacts. Unity has been selected as the primary workspace for this application due to its versatility and ability to seamlessly communicate with the holographic table and other software components involved in the survey scan workflows.  
The choice of Unity as the main development environment is driven by its compatibility with the holographic table and its capability to facilitate smooth communication and integration with the AR device. Unity's flexible and powerful features enable the creation of interactive and immersive AR experiences while also accommodating the integration of various survey scan workflows.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 4 Numerical Simulation
### Comparison of Recognition Performance  
In this section, we compared the recognition performance of the proposed algorithm with that of algorithms in [15; 16], which also studied polar code parameter recognition under the AWGN channel in recent years. All the algorithms are executed with structured and unstructured polar codes with a code length of 256 and a code rate of 1/3 and 2000 codewords, and the results are shown as follows. The curves of the algorithm proposed in this study are marked as DS, and those of the algorithms advanced in [15; 16] are marked as [15; 16] in Figures 11-13.  
Figure 10: Performance under different code lengths.  
As shown in the figures, when the parameters of structured polar codes are recognized, the recognition performance of the algorithm proposed in this paper is better than that of the algorithm advanced in [15] and worse than that in [16]. This is because the algorithm advanced in [15] identifies the information bit positions one by one, which makes it insensitive to the coding structure, and it suffers from errors involving correct information bit numbers but incorrect bit positions. The algorithm proposed in this paper directly uses the GA construction method to determine the number and distribution of the information-bit index, which means that when the code length and rate are correctly identified, the information-bit index must be correct. This helps avoid the recognition error with a correct information-bit number with an incorrect index, while at the same time avoiding calculating the judgment statistic row by row and reducing the number of calculations. The computation comparison is analyzed in detail in the following subsection, and the introduction of likelihood difference in [16] did boost performance.  
Figure 11: Comparison of recognition performance under different code lengths [15,16]. (**a**) The curves under structured polar codes. (**b**) The curves under unstructured polar codes.  
Figure 12: Influence of code length and code rate on complexity [15,16].  
However, the algorithms advanced in [15; 16] are invalid when recognizing the parameters of structured polar codes. This is because the recognition criteria of algorithms advanced in [15; 16] are only designed for structured polar codes, so they failed in terms of unstructured polar code recognition.
## 4 Power Flow Analysis
### Input Power Calculation  
The next step is to obtain the power delivered by the voltages \(\rm V_{1}\) and \(\rm V_{2}\) to the HB-LED load. It is important to point out that this power calculation is without taking the proposed TIBuck DC/DC converter into account, so \(\rm V_{1}\) and \(\rm V_{2}\) will be considered as individual voltages sources, as depicted in Figure 3. Later in this study, the generation of \(\rm V_{2}\) from \(\rm V_{1}\) and its respective power conversion will be considered. The power of each input voltage can be easily calculated from Figure 2.  
In terms of average values, the average driven by the inductors of the high-order output filter of the TIBuck DC/DC converter is the same as the average current through the load Io. With \(\rm D_{ti}\) being its duty cycle, the average currents of the input voltages \(\rm I_{v1}\) and \(\rm Iv2\) can be easily calculated, as follows:  
\[I_{v1}=D_{ti}\cdot I_{o}, \tag{24}\]  
\[I_{v2}=(1-D_{ti})\cdot I_{o}. \tag{25}\]  
Hence, the power of each input voltage \(\rm P_{v1}\) and \(\rm P_{v2}\) can be obtained from Equations (24) and (25), as follows:  
\[P_{v1}=V_{1}\cdot D_{ti}\cdot I_{o}, \tag{26}\]  
\[P_{v2}=V_{2}\cdot(1-D_{ti})\cdot I_{o}. \tag{27}\]
## 4 Experimental Results  
### Subjects and Their Pure Tone Audiograms  
The system depicted in Figure 2 was realized in a prototype hearing aids software in a computer and used for the following experimental tests. Six hearing-impaired patients with V-shaped audiograms were tested in the experiments. The general information of the patients is shown in Table 1. All of the patients had the experience of wearing hearing aids for more than three months. All of the hearing aids had the functions of loudness compensation and wide dynamic range compression. There was not any frequency-shifting processing in their hearing aids. Pure tone hearing thresholds of the patients without wearing hearing aids were measured and the results of audiograms are shown in Figure 4 for both left and right ears.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subject** & **Age** & **Gender** & \begin{tabular}{c} **Cause of** \\ **Deafness** \\ \end{tabular} & **Duration** & \begin{tabular}{c} **Deafness** \\ **Degree** \\ \end{tabular} & \
\begin{tabular}{c} **Wearing** \\ **Experience** \\ \end{tabular} &
\begin{tabular}{c} **Wearing Hours** \\ **Everyday** \\ \end{tabular} \\ \hline S1 & 42 & Male & Noise-induced & 2 years & Moderate & 12 months & \(\geq\)8 h \\ S2 & 25 & Male & Hereditary & 1 year & Moderate & 3 months & 2\(\sim\)8 h \\ S3 & 47 & Male & Noise-induced & 4 years & Moderate & 12 months & \(\geq\)8 h \\ S4 & 60 & Female & Mixed & 5 years & Severe & 28 months & \(\geq\)8 h \\ S5 & 38 & Female & Noise-induced & 4 years & Severe & 20 months & \(\geq\)8 h \\ S6 & 40 & Female & Noise-induced & 4 years & Moderate & 24 months & 4\(\sim\)8 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: General information of the subjects.  
Figure 3: Frequency interpolation and sampling.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 4 Application of Digital Twins to PHM
### Prospects for Digital-Twin-Driven PHM  
In industrial production, equipment failure and maintenance are common problems that not only cause wasted production line downtime but also increase maintenance costs, thus affecting the productivity and profitability of an organization. Digital twin technology can predict potential equipment failures and suggest repairs in advance through real-time monitoring and analysis of digital models of actual physical systems. This predictive maintenance helps to reduce equipment downtime and repair costs, improve equipment reliability and stability, and thus increase productivity and product quality. Digital twins have continued to gain popularity in the aerospace, automotive manufacturing, construction, and energy industries.  
With the development of the industrial Internet, the prospects for applying digital twin technology will become even broader. The industrial Internet provides a broader space for developing digital twin technology. Production data can be collected in real time through sensors and intelligent devices, providing more reliable data support for establishing and optimizing digital twin models. At the same time, the continuous progress of artificial intelligence and extensive data analysis technology also provides more possibilities for theapplication of digital twin technology, which can realize the intelligent management and optimization of production processes.  
However, the application of digital twin technology also faces some challenges. First, establishing digital twin models requires a lot of real-time data support. The data's quality and accuracy are crucial for the model's establishment and predictive maintenance. Second, the application of digital twin technology requires enterprises to have specific technical and talent reserves, including the ability to understand and apply digital twin technology and an in-depth understanding of the production process.  
The application of digital twin technology in the industrial field is promising and can provide enterprises with more reliable and efficient production management and maintenance solutions. With the continuous development of the industrial Internet and artificial intelligence technology, the application of digital twin technology will also usher in more innovations and breakthroughs, bringing more opportunities and challenges to industrial production. In the future, digital twin technology is expected to become an essential support for industrial intelligence and digital transformation, promote changes in industrial production modes and management, and realize intelligence and efficiency within industrial production and system fault diagnosis and maintenance.  
Figure 6: Digital twin maturity.
## 3 Metaverse Classroom: A Layered Architectural View
### Personalization and Accessibility Layer  
#### 3.3.1 AI-Driven Personalization  
AI-driven personalization in a Metaverse classroom offers a customized learning experience by adapting content, pace, and instruction style to each student's needs [130]. Using intelligent algorithms, AI monitors student progress and adjusts lessons in real time. For example, if a student struggles with a math concept, the AI can provide additional practice or simplify the material [7]. Conversely, AI can introduce more advanced challenges for students excelling in a subject to maintain engagement and prevent boredom [71]. This ensures students progress comfortably, feeling both challenged and supported. One key benefit of AI-driven personalization is real-time feedback, which helps students immediately recognize and correct mistakes [131]. For instance, in a virtual chemistry lab, students might receive instant feedback on procedural errors, guiding them toward the correct method [132]. This immediate response reduces uncertainty about performance, boosting clarity and confidence [133]. Real-time feedback fosters a proactive learning mindset, where students view challenges as growth opportunities rather than obstacles [83].  
AI analytics also give educators valuable insights into student performance and engagement [18]. AI can track patterns, such as subjects where students struggle, time spent on tasks, and preferred learning methods [44]. For example, if AI detects that a student learns better with visual aids, teachers can adjust their approach to include more visuals [134]. His targeted strategy empowers teachers to offer personalized support, creating an environment where individualized learning is the standard [135]. Adaptive learning paths are another powerful feature of AI-driven personalization. These paths evolve based on a student's progress, preferences, and engagement levels [136]. For example, in a Metaverse history class, AI might adjust lessons to suit a student's preference for narratives,timelines, or simulations [52]. The AI recalibrates their learning paths as students advance, introducing new resources and challenges to match their development. This dynamic approach gives students a sense of ownership over their learning journey [137]. Finally, AI-driven personalization enhances student motivation by creating a sense of personal relevance and achievement [138]. AI tracks progress and provides tailored encouragement, recognizing accomplishments and suggesting next steps to keep students engaged [20]. For instance, after completing a challenging module, a student might receive a congratulatory message or a suggestion for further exploration [139]. This personalized feedback reinforces a sense of achievement and forward momentum, making students feel valued and supported [140]. By transforming the Metaverse classroom into a responsive, adaptive ecosystem, AI-driven personalization enriches the learning experience to meet each learner's needs and potential [136].