esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Pbm
* The precise nature of the surrounding tissues that will determine the light penetration depth. * The tissue oxygenation status that will be enhanced by applying low fluences with longer irradiation times, along with some \"time-off\" intervals. * The new concept of \"photon fluence\", where the individual wavelength-specific photon energy (in eV) is considered. As mentioned in the Introduction [1; 2], the excessive and inappropriate use of antibiotics has resulted in the development of highly resistant microbiota. Consequently, aPDT has emerged as an alternative treatment [4]. Besides its primary antimicrobial effect, aPDT has been observed to have a secondary beneficial impact on the healing of the surrounding tissues in the form of PBM [12; 13]. The following approaches are commonly employed alongside or as alternatives to antibiotics in the management of oral health issues. Anti-inflammatory and analgesic drugs help alleviate pain and reduce inflammation in oral conditions [66; 67]. Antimicrobial mouthwashes and oral gels are used to target and control oral microbial infections [68; 69]. Ozone has antimicrobial properties and is capable of eliminating bacteria, viruses and fungi and its clinical efficacy and safety in different dental procedures are still being evaluated [70]. Acupuncture and homeopathy may be used to provide pain relief and promote overall well-being [71; 72]. Platelet-rich fibrin (PRF) and stem cells are being explored for their potential in accelerating wound healing and tissue regeneration in oral and maxillofacial surgery [73; 74]. Additionally, some recently introduced compounds, such as probiotics [75], lysates [76] and postbiotics [77], have been demonstrated as having a significant influence on an oral environment, leading to modified clinical and microbiological parameters in patients. Since this is a laboratory-based experimental study, and the laboratory conditions are totally different than in the oral environment, caution should be exercised before extrapolating results to in vivo patient therapy. As previously mentioned, in natural clinical conditions, several factors can significantly influence the treatment outcome, such as the presence of competitive absorbers of each photosensizer, the optical properties of the surrounding tissues (such as the gingiva, bone and/or dentin) and the availability of oxygen in the treatment environment. As such, the present findings of this study cannot be reliably replicated in clinical conditions or used to interpret therapy outcomes [78]. Consequently, the energy densities calculated and proposed in this study are only theoretical. The treatment outcome, when applying these fluences, should be further investigated through well-designed in vivo studies, comparing against the treatment alternatives noted above and conventional methods. This research will aim to explore the efficacy of the examined photosensitizers activated by these theoretical fluences in combining the beneficial effects of photobiomodulation (PBM) in the surrounding tissues when antimicrobial photodynamic therapy (aPDT) is performed at a specific target.
## 1 Introduction
Thus, low-complexity, efficient detection methods are required. Recently, iterative solvers, which avoid the need for direct matrix inversion, have been extensively studied as efficient approaches for approximating MMSE solutions in massive MIMO systems [13]. These methods offer substantial reductions in computational complexity while maintaining performance levels close to that of the linear MMSE detector [14], making them more suitable for real-time processing, where direct solvers might fail due to their higher memory and computational demands. The concept behind the complexity reduction in iterative detectors leverages the observation that the channel matrix within the massive MIMO system is column asymptotically orthogonal and column full rank. In [15], a well-known approach based on the Neumann series expansion was employed to approximate matrix inversion. However, its complexity is cubic and can exceed that of the MMSE approach when the expansion order is greater than three. The complexity of signal detection was reduced in [16] with the help of the Richardson iteration method, which is a linear detection scheme capable of estimating the transmitted vector without computing the complex matrix inversion. However, its reliance on the relaxation parameter significantly influences the convergence of the method. In [17], a reduced-complexity second-order Richardson method was developed to accelerate the convergence of the conventional Richardson detector. Another detector based on the conjugate gradient (CG) method was proposed in [18]. It obtained a low error rate but faced challenges related to correlation issues and limited parallelism. The Gauss-Seidel method and its improvements were presented in [19; 20], achieving high performance in massive MIMO systems. Although GS-based detectors can achieve better performance, the inherent sequential iteration structure poses challenges for parallel implementation. The successive over-relaxation (SOR) detector [21] achieved improved convergence by employing a suitable matrix-splitting technique and a relaxation parameter. The authors of [22] pointed out that the detection algorithm based on accelerated over-relaxation (AOR) iteration can provide an enhanced convergence rate compared to the SOR scheme. In addition, various enhanced versions of basic iterative methods or combinations of different detection algorithms, such as the improved Neumann series [23; 24], joint steepest descent and non-stationary Richardson iteration [25], recursive CG [26], steepest descent and Barzilai-Borwein approach [27], and extrapolation principle-based approach [28], have been applied to further improve detection capability. Nevertheless, their complexity increases with the order of iterations.
## 3 Simulation Results and Discussion  
In this segment, the optimization, development, and model outcomes of all of the designs are discussed in detail. Section 3.1 focuses on the design and optimization of a LP DRA at its full potential return loss bandwidth along with a single conformal copper strip. Likewise, out of shifting the single copper feed to a novel conformal-metal-strip-shaped feed with optimized parameters excites the orthogonal waves for generating circular polarization. In Section 3.2, to provide improved isolation, we discuss how a DGPS with optimized parameters was introduced at an optimized location, i.e., between the two radiators.
# Fabrication of a Simultaneous Highly Transparent and Highly Hydrophobic Fibrous Films
## 1 Introduction  
Recently, building integrated photovoltaics (BIPV) have received great attention as a renewable and non-polluting energy, and together with energy efficiency, zero energy and zero emission buildings are ever increasing. To become a zero energy or zero emission building, solar cells are integrated within the climate envelopes of buildings and utilizing solar radiation to produce electricity. BIPV offer an economical and technical solution to integrate solar cells harvesting solar radiation to produce electricity within the climate envelopes of buildings. Self-cleaning property receives attention in applications such as building walls. The most commonly used concepts for self-cleaning surfaces are hydrophobicity. The hydrophobicity for self-cleaning comes from the assumption that rolling water droplets down the hydrophobic surface will pick up dirt and become washed away. One of the tools to realize self-cleaning includes textiles and fabrics. Electrospinning is an effective process for fabrication of high surface-to-volume ratio and the generation of rough surface. Several researches have been able to produce electrospun surfaces with hydrophobicity. Such property comes about from a combination of surface texture and material used [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. The rough surfaces composed of electrospun textiles with contact angle greater than 90 degdefines as hydrophobic surface [13]. Additionally, high-hydrophobic surfaces with high contact angles (>120\({}^{\circ}\)) exhibited self-cleaning due to their non-adhesive and non-wetting features [14; 15; 16]. Recently, many researches on highly hydrophobic surfaces with high water contact angle (WCA) of the electrospun fibers have been published [17; 18; 19]. The various application of the electrospun fibers such as transparent conducting film, stretchable electrode, and gas sensing material have been demonstrated [20; 21; 22]. In this study, we examined the influences of electrospun fiber alignment on the optical transmittance and water contact angle to obtain transparent and self-cleaning solar cells (TSC). The uniaxially oriented electrospun polystyrene (PS) fiber scaffolds were produced by employing our two dimensionally aligning jigs as the collector in electrospinning [21]. The transmission characteristics of the randomly distributed fibers and the aligned fibers were measured using UV-Visible transmission spectroscopy. Surface roughness of the randomly distributed fibers and the aligned fibers were analyzed based on atomic force microscope (AFM) images. The distributions in the fiber diameter of the randomly distributed fibers and the aligned fibers were investigated with scanning electron microscopy (SEM) micrographs.
## 4 Applications
### Impeller
However, the cost of computation is significantly reduced when using PM because it only needs to solve the equilibrium equations along the expansion routes. (3) According to Figure 9, increasing the uncertainty of interval parameters leads to the expansion of the response interval. This indicates that the extreme conditions of the structure can only be predicted when uncertainty is taken into account. Design solutions obtained by considering uncertainty are more secure and reliable. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Uncertainty Level**} & \multirow{3}{*}{**Bounds**} & \multicolumn{1}{c}{**SM**} & \multicolumn{1}{c}{**STM**} & \multicolumn{2}{c}{**SDAM**} & \multicolumn{2}{c}{**PM (\(\boldsymbol{\varepsilon}\) = 1\%)**} \\ \cline{3-8}  & & **Value** & **Value** & & & **Value** & & **Value** \\  & & **(mm)** & **(mm)** & **Error** & **(mm)** & **Error** & **(mm)** & **Error** \\ \hline \multirow{3}{*}{10\%} & LB & 5.459 & 5.106 & \(-6.47\%\) & 5.098 & \(-6.60\%\) & 5.454 & \(-0.09\%\) \\  & UB & 12.187 & \(11.701\) & \(-3.99\%\) & 11.710 & \(-3.91\%\) & 12.182 & \(-0.04\%\) \\  & LB & 3.513 & 2.301 & \(-34.50\%\) & 2.247 & \(-36.04\%\) & 3.508 & \(-0.14\%\) \\  & UB & 17.803 & 15.492 & \(-12.98\%\) & 15.575 & \(-12.52\%\) & 17.798 & \(-0.03\%\) \\  & LB & 2.172 & \(-0.174\) & \(-108.02\%\) & \(-0.343\) & \(-115.82\%\) & 2.169 & \(-0.12\%\) \\  & UB & 25.873 & 19.613 & \(-24.20\%\) & 19.931 & \(-22.97\%\) & 25.869 & \(-0.02\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Bounds and errors of \(u_{1}\) for the three cases. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Uncertainty Level Bounds**} & \multicolumn{1}{c}{**SM**} & \multicolumn{1}{c}{**STM**} & \multicolumn{2}{c}{**SDAM**} & \multicolumn{2}{c}{**PM (\(\boldsymbol{\varepsilon}\) = 1\%)**} \\ \cline{3-8}  & & **Value** & **Value** & & **Value** & & **Value** \\  & & **(\(\boldsymbol{\times 10^{-2}}\) Rad)** & **(\(\boldsymbol{\times 10^{-2}}\) Rad)** & **Error** & & **(\(\boldsymbol{\times 10^{-2}}\) Rad)** & **Error** \\ \hline \multirow{3}{*}{10\%} & LB & 1.333 & 1.236 & \(-7.28\%\) & 1.243 & \(-6.72\%\) & 1.332 & \(-0.09\%\) \\  & UB & 2.985 & 2.856 & \(-4.33\%\) & 2.867 & \(-3.94\%\) & 2.984 & \(-0.04\%\) \\  & LB & 0.855 & 0.516 & \(-39.69\%\) & 0.540 & \(-36.86\%\) & 0.854 & \(-0.14\%\) \\  & UB & 4.362 & 3.756 & \(-13.91\%\) & 3.814 & \(-12.58\%\) & 4.361 & \(-0.03\%\) \\  & LB & 0.527 & \(-0.144\) & \(-127.33\%\) & \(-0.100\) & \(-119.06\%\) & 0.526 & \(-0.12\%\) \\  & UB & 6.349 & 4.716 & \(-25.73\%\) & 4.883 & \(-23.10\%\) & 6.349 & \(-0.01\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Bounds and errors of \(\theta_{z,2}\) for the three cases.
## 5 Conclusions
### Practical Applications  
The findings of this study can be directly applied to training programs to enhance performance and efficiency in race walking. Coaches and athletes can incorporate the following strategies:  
1. Optimization of Walking Technique: Regular motion analysis sessions using tools like the Vicon system can help identify technical errors such as excessive center of mass oscillations and limb asymmetry. Coaches should focus on correcting these errors with targeted exercises that improve stability and symmetry.
2. Strengthening Key Muscle Groups: Incorporating exercises to strengthen hip sensors, knee flexors, and muscles controlling the support phase, such as plantar and dorsal flexors, should be a key component of training. These exercises will improve muscle coordination and enhance energy utilization during walking.
3. Individualized Training Plans: Training programs should be personalized based on the athlete's specific biomechanical and physiological needs, including regular testing to monitor progress and adjust the training regimen accordingly.
4. Monitoring Fatigue and Recovery: Coaches should carefully monitor the intensity and volume of training, particularly during high-intensity sessions, to prevent excessive muscle fatigue. Proper rest periods and recovery protocols can help maintain optimal walking technique.
5. Use of Technology in Training: Utilizing advanced tools such as 3D motion analysis, EMG monitors, and portable ergospirometers allows for precise assessment of performance and technique, enabling more tailored adjustments to training programs to improve energy efficiency.  
Conceptualization, W.C., A.T.K. and W.M.; methodology, W.C., A.T.K. and L.R.; software, W.C., A.T.K. and W.M.; validation, W.C., A.T.K. and L.R.; formal analysis, W.C., A.T.K. and W.M.; investigation, W.C., A.T.K. and W.M; resources, W.C., A.T.K. and T.A.; data curation, W.C. and A.T.K.; writing--original draft preparation, W.C., A.T.K., L.R. and T.A.; writing--review and editing, W.C., A.T.K., W.M., T.A. and L.R.; visualization, W.C. and A.T.K.; supervision, W.C., A.T.K. and T.A.; project administration, W.C. and A.T.K.; funding acquisition, W.C. and A.T.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science Centre grant number NN404202837 \"Searching for individual technique patterns in sports walking based on three-dimensional movement analysis and the physiological cost of exercise of varying intensity\".  
**Institutional Review Board Statement:** The studies involving human participants were reviewed and approved by the Bioethical Committee at the Regional Medical Chamber in Krakow (No. 287/KBL/OIL/2020). The patients/participants provided their written informed consent to participate in this study.  
**Informed Consent Statement:** Informed consent was obtained from all study participants.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Preliminaries
From this and the fact that \(S(i,s)\) is a minimum subset of \(W(i,s)\) dominating every interior vertex of \(G[W(i,s)]\), \(|S(i,s)|\leq|D(i,s)|\). From this, we have  
\[|S_{s}|\leq\sum_{i=0}^{r}|S(i,s)|\leq\sum_{i=0}^{r}|D(i,s)| \tag{4}\]  
Since the vertices of \(V_{i\times k+s-1}\) and \(V_{i\times k+s}\) appear in subgraphs \(G[W(i,s)]\) and \(G[W(i+1,s)]\), \(0\leq i<r\),  
\[\sum_{i=0}^{r}|D(i,s)|\leq|D|+\sum_{i=0}^{r-1}|D\cap V_{i\times k+s-1}|+|D\cap V _{i\times k+s}| \tag{5}\]  
Notice that  
\[\min_{s=2}^{k+1}\{\sum_{i=0}^{r-1}|D\cap V_{i\times k+s-1}|+|D\cap V_{i\times k +s}|\}\leq\frac{2|D|}{k} \tag{6}\]  
Let \(S\) be an \(S_{s}\) with a minimum cardinality. Then from Inequalities (4), (5), and (6), we have \(|S|\leq|D|+\frac{2|D|}{k}\). that is, the solution produced by the modified algorithm has the approximation ratio \((1+2/k)\) for the planar dominating set problem. Given a planar graph \(G\), a planar embedding of \(G\) can be computed in linear time [23]. It is obvious that Step 1 and Step 3 can be computed in linear time. Step 2 (a) and (c) can be computed in \(O(kn)\) time. Recall that FT Algorithm (by [PERSON] and [PERSON] [14]) is the most efficient known exact algorithm for the planar dominaing set problem. We use FT Algorithm for Step 2 (b). Given a graph \(G\) and a branch-decomposition of \(G\) with width \(\beta\), FT Algorithm finds an optimal solution for the dominating set problem in \(O(2^{(3\log_{4}3)\beta}n)\) time. Each subgraph \(G[W(i,s)]\) is \((k+2)\)-outer planar and has branchwidthat most \(2(k+2)\). A branch-decomposition of \(G[W(i,s)]\) with width at most \(2(k+2)\) can be computed in linear time [24]. Each vertex of \(G\) appears in at most two subgraphs for a specific value \(s\). Therefore, Step 2 (b) takes  
\[\sum_{s=2}^{k+1}\sum_{i=0}^{r}O(2^{(6\log_{4}3)(k+2)}|W(i,s)|)=\sum_{s=2}^{k+1} O(2^{(6\log_{4}3)(k+2)}n)=O(2^{(6\log_{4}3)(k+2)}kn)\]  
time which is the dominating part of the modified application's running time. []  
Notice that if \(G\) is decomposed into \((2k+2)\)-outer planar subgraphs, the modified PTAS achieves the approximation ratio \((1+1/k)\) and has running time \(O(2^{(12\log_{4}3)(k+1)}kn)\). We conclude this section by comparing the running time of the modified PTAS with that of the application of [PERSON]'s framework in Section 3.1 for the planar dominating set problem. We assume that the most efficient FT Algorithm is used in Step 2 (b) of both algorithms. Assume that \(G\) is decomposed into \((k+1)\)-outer planar subgraphs in [PERSON]'s framework in Section 3.1. Then a branch-decomposition of \(G[U(i,s)]\) with width at most \(2(k+1)\) can be computed in linear time and Step 2 (b) takes  
\[\sum_{s=2}^{k+1}\sum_{i=0}^{r}O(2^{(6\log_{4}3)(k+1)}|U(i,s)|)=O(2^{(6\log_{4} 3)(k+1)}kn)\]  
time which is the dominating part of the framework's running time. The constant in the exponent of the running time of the modified PTAS for the approximation ratio \((1+1/k)\) is as twice as that of [PERSON]'s framework in Section 3.1.
###### Abstract  
With the development of communications technology, surface acoustic wave (SAW) and bulk acoustic wave (BAW) devices have become hotspots of the competitive research in the frequency band above GHz. It imposes higher requirements on the operating frequency, temperature coefficient of frequency (_TCF_), and electromechanical coupling coefficient (\(k^{2}\)) of SAW devices. In this work, we reported on a novel ZnO/SiO\({}_{2}\)/diamond-layered resonator structure and systematically investigated its propagation characteristics by using finite element methods. A comparative study and analysis of \(k^{2}\) and acoustic velocity (\(v_{\rm p}\)) for both the excited Rayleigh mode and the Sezawa mode were conducted. By selecting the appropriate ZnO piezoelectric film, SiO\({}_{2}\), and electrode thickness, the Sezawa mode was chosen as the main mode, effectively improving both \(k^{2}\) and \(v_{\rm p}\). It was observed that the \(k^{2}\) of the Sezawa mode is 7.5 times that of the excited Rayleigh mode and nearly 5 times that of piezoelectric single-crystal ZnO; \(v_{\rm p}\) is 1.7 times that of the excited Rayleigh mode and nearly 1.5 times that of piezoelectric single-crystal ZnO. Furthermore, the proposed multilayer structure achieves a _TCF_ close to 0 while maintaining a substantial \(k^{2}\). In practical applications, increasing the thickness of SiO\({}_{2}\) can compensate for the device's _TCF_ reduction caused by the interdigital transducer (IDT). Finally, this study explored the impact of increasing the aperture width and IDT pairs on the performance of the single-port resonator, revealing the changing patterns of quality factor (Q) values. The results reported here show that the structure has great promise for the fabrication of high-frequency and low-_TCF_ SAW devices.  
surface acoustic wave; ZnO piezoelectric film; finite element method; Sezawa wave; Rayleigh wave +
Footnote †: journal: Applied Sciences
## 2 Antenna Design and Performance
### Dual-Polarization P-Band Tx/Rx Dipole Antenna Design and Performance  
We assembled two optimized dual-polarization half bow-tie dipole antennas in an orthogonal manner to construct a Tx/Rx structure, as shown in Figure 12a. Ports 1 and 2 are included in the Tx part, and the other ports are included in the Rx part. Therefore, the designed Tx/Rx antenna can transmit and receive dual-polarized signals in the P-band. The simulated \(-10\) dB reflection coefficient bands cover from 433.81 to 796.3 MHz. However, the simulated isolation (\(\mid\)S\({}_{\text{MN}}\mid\) ) performances between the Tx and Rx ports are lower than 19.2 dB over the target band. Since all antennas are integrated in a limited space, the distance between the antennas is not sufficient to allow for high isolation characteristics. Figure 12c shows the simulated radiation performance at 580 MHz when Port 1 is excited. Since the designed Tx/Rx antenna has a symmetrical structure, the radiation characteristics of all antennas are identical. Therefore, only the simulated result for the case of Port 1 excitation is shown in this paper. At 580 MHz, the simulated radiation gain is 2.99 dBi at \(\theta=0^{\circ}\) and FBR is 1.65 dB. The isolation characteristics can be improved by inserting an absorber, and this procedure will be discussed in the next section.
## Appendix A  
### Definition of Graph Determinacy Entropy  
The structural determinacy of graphs can be quantified by the binary entropy of the adjacency matrix, defined as  
\[H(A)=-\frac{1}{N^{2}}\sum_{i,j}[a_{ij}\log a_{ij}+(1-a_{ij})\log(1-a_{ij})], \tag{1}\]  
where binary entropy \(H(A)\) quantifies the structural determinacy of the graph through the adjacency matrix \(A=[a_{ij}]\), where \(a_{ij}\in\{0,1\}\) indicates the presence of the edges between nodes \(i\) and \(j\), \(N=|\mathcal{V}|\) represents the total number of nodes, \(a_{ij}\log a_{ij}\) captures the existence entropy of the edge (with \(0\log 0\equiv 0\)), and \((1-a_{ij})\log(1-a_{ij})\) accounts for the uncertainty of the absence of the edge under identical zero convention.Crucially, lower entropy values indicate higher structural determinacy, as \(H(A)\to 0\) corresponds to maximally certain connectivity patterns.  
For the predefined deep sequence correlation graph (based on cosine similarity), setting the threshold \(\theta=0.7\) yields an entropy value of \(H(A_{d}^{\text{cos}})=0.31\), indicating significant structural uncertainty. Further sensitivity analysis reveals that when \(\theta\) increases from 0.6 to 0.8, the entropy fluctuation \(\Delta H=+0.13\) demonstrates the inherent threshold dependence of conventional methods. In contrast, the adaptively constructed deep sequence graph \(A_{d}^{\text{adp}}\) maintains a consistently low entropy \(\left(H(A_{d}^{\text{adp}})=0.12\right)\) with negligible variation (\(\Delta H=0.01\)) under threshold changes. This comparison clearly shows that the adaptive mechanism effectively mitigates threshold sensitivity through dynamic edge weight learning, resulting in substantially improved graph robustness.  
For the predefined feature correlation graph (based on covariance signs), the entropy \(H(A_{f}^{\text{cov}})=0.28\), while slightly better than that of the predefined deep sequence graph, remains constrained by statistical assumptions. However,the adaptive feature graph achieves a significantly lower entropy of \(H(A_{f}^{\text{adp}})=0.15\). This highlights that feature correlation modeling requires overcoming both threshold dependency and statistical bias challenges, which the adaptive mechanism addresses through implicit encoding of non-Euclidean relationships via attention weights.