esa-sceva/satcom-chunk-collection huggingface dataset:
applied sciences  
Article Technical Feasibility and Histological Analysis of Balloon-Expandable Metallic Stent Placement in a Porcine Eustachian Tube  
[PERSON] 1,1,  
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[PERSON] 2,  
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[PERSON] 4,  
[PERSON] 2,*,1  
and [PERSON] 2,*,1  
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote 2: email: [EMAIL_ADDRESS]  
Footnote 3: email: [EMAIL_ADDRESS]  
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Footnote 6: email: kk@  
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mcc.se.uk  
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Until recently, otolaryngologists' understanding of ET dysfunction (ETD) was limited and few treatment options were available [4; 5; 6; 7; 8]. Currently, the most common surgical approach is myringotomy with or without the insertion of a ventilation tube, with a reported success rate of 79% after a 4-month follow-up period [6]. However, this is only a temporary management solution and it does not address the ET directly. Other conservative management solutions for ETD also include the Valsalslva maneuver for pressure equalization and use of nasal steroids and decongestants [9]. With the introduction of balloon Eustachian tuboplasty (BET), however, surgical management of ETD is now possible [10]. The proposed mechanisms underlying the effects of BET include microtears in the cartilaginous part of the ET [10], decreased mucosal inflammation, and reduced biofilm infection load [11]. Since 2010, studies have reported the use of BET to treat ETD, with success rates ranging between 36% and 80% [8; 12; 13; 14]. Although these prior studies reported that BET is superior to conventional medical management, some patients with ETD do not respond to this dilation treatment.  
The possible options for the further management of patients with BET failure include a repeat BET procedure or the insertion of an ET stent [15]. However, stents for ETD have not been clinically applied. Thus, animal studies are needed to develop a stent option for ETD and to compare histologic changes in the ET after a repeat BET or insertion of an ET stent. The current study established a porcine model for ET stenting and validated the feasibility of inserting commercially available stents of two different diameters using combined endoscopic and fluoroscopic guidance. This study aimed to investigate the technical feasibility of stent placement and histological changes after stent placement in a porcine ET and evaluate the optimal size and luminal configuration of ET stents for the porcine model.
## 4 Results  
### Multi-Fidelity Framework  
The results for E1 are presented in Table 6 and Figure 10. These results show that the low-fidelity model performs well on the low-fidelity test data (E1-1), achieving an \(R2\) of 0.995 and \(RMSE\) of 0.044 m. This indicates that the trained MLP model can accurately capture the mapping relationship between the perlite settlement and temperature field. The \(R2\), which is close to 1, and the low \(RMSE\) essentially validate the efficacy of the proposed data-driven approach. As shown in Figure 10a, the model provides consistent predictions across various low-fidelity data points, closely matching the ground truth and proving the practicality of this framework in such complex engineering tasks. However, the performance of the low-fidelity model drops significantly when it is applied to high-fidelity data (E1-2). In this case, the \(R2\) decreases to 0.410 and the \(RMSE\) rises to 0.425 m, showing that the model that was trained solely on the low-fidelity data fails to generalise to high-fidelity data, which is expected due to the systematic bias between the low- and high-fidelity data sets. Essentially, this reflects the limitation of data-driven approaches--heavy data dependency and poor generalisability from one feature distribution to another. Although the model performs well on low-fidelity data, it cannot accurately adapt to real-world conditions, limiting its usefulness for real-world applications. Figure 10b highlights these discrepancies, showing that the low-fidelity model struggles with perlite settlement under high-fidelity conditions, indicating that the low-fidelity model cannot satisfyingly serve real-world engineering on its own. This result emphasises the importance of adopting a multi-fidelity machine learning framework.  
Comparing the results of E1-2 and E1-3, an improvement was seen when using the high-fidelity model trained solely on scarce high-fidelity training data. The \(R2\) increases to 0.790, and the \(RMSE\) decreases to 0.253 m. However, the limited availability of high-fidelity data restricts the performance, resulting in noticeable errors, as shown in Figure 10c. The multi-fidelity framework achieves the best results (E1-4), with an \(R2\) of 0.980 and an \(RMSE\) of 0.078 m on the high-fidelity test data, demonstrating the advantages of the proposed multi-fidelity machine learning framework. By combining both low- and high-fidelity data, the multi-fidelity model leverages the strengths of both data, improving the prediction accuracy of the model. As shown in Figure 10d, even with limited high-fidelity training data, the model achieves reliable predictions for perlite settlement. This hierarchical framework effectively integrates the mapping relationships embedded in both low- and high-fidelity data, compensating for their respective limitations and resulting in a more accurate and reliable model.
###### Abstract  
In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative \"\(3+1\)\" dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies.  
inter-satellite links; topology control; dynamic links; maximum weight-matching problem Article
## 5 Conclusions  
In this paper, we have addressed the computational challenges involved in signal detection for massive MIMO systems. We introduced two iterative detection algorithms: SAOR and CGA-SAOR. The SAOR-based detector significantly reduces computational complexity by circumventing the exact matrix inversion required by the conventional MMSE detector. The CGA-SAOR algorithm further enhances the convergence rate and detection performance by integrating the CG acceleration procedure at virtually the same complexity. Our computational complexity analysis demonstrates that both methods substantially reduce the computational burden compared to the traditional MMSE detection method. Additionally, the numerical results confirm the effectiveness of the proposed approaches, which show superior performance over existing iterative detectors, particularly in scenarios with a large number of antennas. Thus, the proposed methods achieve a good balance between computational complexity and error rate performance. Future work may explore integrating these methods with advanced machine learning techniques to further enhance their adaptability and performance.  
Conceptualization, I.A.K.; Methodology, I.A.K.; Software, M.A.; Validation, P.V. and L.W.; Formal analysis, I.A.K. and M.A.; Investigation, I.A.K., M.N.I. and S.C.; Resources, L.W.; Data curation, M.A. and S.C.; Writing--original draft, I.A.K.; Writing--review & editing, M.N.I., S.C., P.V. and L.W.; Supervision, L.W.; Funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Second Century (C2F) fund at Chulalongkorn University.  
Data Availability StatementData are available upon reasonable request from the corresponding author.  
The authors declare no conflicts of interest.  
Figure 7: Complexity comparison.
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 4 Stray Light Suppression and Simulation Analysis
### Simulation Results Analysis  
The ray tracing is based on the principles of geometric optics and optical laws, simulating the propagation of light rays within optical systems to achieve research and analysis of the performance and behavior of optical systems. The non-sequential mode is a more rigorous method of geometric ray tracing. In this mode, the scattering, reflection, and absorption processes of light on object surfaces are fully considered. Therefore, simulation analysis of stray light in instruments is typically based on the non-sequential mode. According to the system simulation parameters, ray tracing of the optical and mechanical structure of the spectrometer module was carried out in Zemax with a non-sequential structure. The total diffraction efficiency of the grating is 90%, with the main diffraction orders being \(-\)1, 0, +1, and +2, and their respective proportions being 1:2:4:2. Other diffracted light is scattered and absorbed. Other critical surfaces undergo blackening treatment, with a surface absorptance of 90% and a Lambertian scattering model with a scattering function of 0.8. Nine light sources are placed at positions corresponding to the slit size to characterize the incident light within the entire slit area. The energy of each of the nine light sources is 1 W. The ray tracing relative threshold is set to 1.0 \(\times\) 10\({}^{-8}\).  
As shown in Figure 13, the cross-section analysis diagram of the detector surface irradiance under the different configurations of the spectrometer is given. The peak wavelength in the diagram represents the seven target wavelengths set during the optical tracing process, and the other small peaks represent the distribution of stray light. Figure 13a shows the irradiance distribution of the detector surface under ideal conditions, and Figure 13b shows the irradiance of the detector surface after the mechanical structure surface is blackened. It is worth noting that compared with the ideal optical system, the detector also receives a considerable amount of stray light in addition to receiving signals from the target wavelength. Figure 13c shows the distribution of stray light on the detector surface after extinction threading and the addition of blocking ring treatment on the mechanical inner surface of the spectrometer (see Figure 12 for the specific mechanical structure). Compared with the smooth surface only after blackening, the stray light suppression level is significantly improved, and the stray radiation of the spectrometer can be controlled at the order of 10\({}^{-4}\). The influence of stray light on the system is reduced, and the effect of stray light suppression meets the design requirements of the system.  
Figure 12: (a) Light-shielding ring structure. (b) Ray tracing diagram after adding the light-shielding ring.
## 2 Capture Probabilities Under Different Fading Channels  
The capture effect is a common phenomenon in wireless networks. There are two models for analyzing the capture effect [24; 25; 26]. In the first model, the capture effect occurs when the received signal power which comes from one transmitter is greater than each of the other interferer's power by a minimum threshold factor \(z\), which is called the capture ratio. In the second model, one signal is captured by the receiver when its power level exceeds the sum of the power of all the other interfering signals by capture ratio \(z\). Since the second model resembles more accurately the way a real receiver operates [27], we will adopt it to evaluate the capture probabilities under Rayleigh, Rician and Nakagami-_m_ fading channels, respectively.  
For the sake of analysis, we disregard the effect of propagation path loss and assume that all signals transmitted have same average power value \(\gamma\)[25; 28]. We consider \(n\) (an integer greater than one) packets transmitted simultaneously, which means that they choose the same slot for transmission. Among them, one is a test packet \(t\) and the other \(n-1\) packets are interfering packets. The condition for capture is \(\dfrac{\gamma_{t}}{\sum_{k=1}^{n-1}\gamma_{k}}>z\), and the capture probability is given by [25; 28]  
\[\begin{split} p_{cap}&=n\int_{0}^{\infty}f_{\gamma _{t}}(\gamma_{t})\cdot Pr\left[\dfrac{\gamma_{t}}{\sum_{k=1}^{n-1}\gamma_{k}}> z\right]d\gamma_{t}\\ &=n\int_{0}^{\infty}f_{\gamma_{t}}(\gamma_{t})\left[\int_{0}^{ \frac{\gamma_{t}}{2}}g_{\gamma_{n-1}}(\gamma_{n-1})d\gamma_{n-1}\right]d \gamma_{t},\end{split} \tag{1}\]  
where \(f_{\gamma_{t}}(\gamma_{t})\) is the probability density function (PDF) of received power, and \(g_{\gamma_{n-1}}(\gamma_{n-1})\) is the compound PDF that result from the convolution of \(n-1\) PDFs of received power.  
Next, we will derive the expressions of capture probability under Rayleigh, Rician and Nakagami-_m_ fading channels, respectively.
## 3 Implemented Strategy
### Steps for Implemented Methodology  
The following are the steps involved in getting the optimal location and sizes of the capacitors in order to minimize the cost.  
1. Read system data, specify base values for S, V, I, and Z.
2. Run load flow to find \(P_{loss}^{Tot}\) and \(Q_{loss}^{Tot}\), \(V_{min}\) and \(V_{max}\) at each node, i.e., uncompensated values.
3. Calculate \(LSIs\) for each bus using (12) and (13) and arrange in respective order. Top 50% of buses from \(LSI\) list will be stored in '\(a\)' (reduced search space) and considered for capacitor placement.
4. Set '\(nvar\)', i.e., total number of capacitors to be placed. In this work, it is 10-15% of total buses.
5. Initialize PSO parameters, i.e., set number of particles \((Np)\), \(w\), \(c_{1}\), \(c_{2}\), \(r_{1}\), and \(r_{2}\).
6. Generate random particles (candidate solutions) with position and velocity.
7. Set the iteration count \(i\) to zero.
8. Using (14) and (15), the velocity and the position of the particle are updated, respectively.
9. The local best, i.e., \(bparticle(i)\) and global best, i.e., \(gparticle(i)\) solutions are found as: \(if\quad particle(i).cost<bparticle(i).cost\quad than\)\(bparticle(i)=particle(i)\); \(else\quad bparticle(i).cost<gparticle.cost\quad than\)\(gparticle(i)=bparticle(i)\); \(end\)
10. Among total \(N_{p},gparticle.pos[i]=(Q_{\mathbb{C}1},...,a)\) correspond to the number of candidate buses and \(gparticle.pos[i+a]\) correspond to each capacitor size to be placed.
11. Place the capacitors at the respective buses and calculate \(P_{loss}^{Tot}\), \(Q_{loss}^{Tot}\) and \(Q_{\mathbb{C}}^{Tot}\).
12. Evaluate fitness function given by (1).
13. If \(i\geq N_{p}\), display results and end, else go to step 7.  
These steps are briefed in the flowchart given in Figure 3.  
Figure 2: Movement of a single particle in swarm.
# The Volatile Compounds Change during Fermentation of _Saccharina japonica_ Seedling
## 2 Materials and Methods
### GC-MS Analysis  
Thirty milliliters of distilled water and 1 g of _S. japonica_ seedling were added to an extraction vial containing 10 \(\upmu\)L of the internal standard (cyclohexanone). The sample was equilibrated at 60 \({}^{\circ}\)C for 30 min, followed by extraction for 30 min with a 50/30 \(\upmu\)DVB/CAR/PDMS extraction fiber.  
A QP2010 gas chromatography-mass spectrometry (GC-MS) system (Shimadzu, Kyoto, Japan) was equipped with an Rtx-5 MS column (60 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m, Restek Corporation, Bellefonte, PA, USA). Helium (99.999% purity) was used as the carrier gas. The column flow rate was 3.16 mL/min in splitless injection mode. The injection port temperature was 230 \({}^{\circ}\)C. The column temperature was 40 \({}^{\circ}\)C at the start, increased to 100 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, and then further increased to 230 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for 5 min. The temperatures of the ion source and the interface were 220 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively [23]. The MS was set to electron impact (EI) ionization mode with a mass scan range from 35 to 500 amu, and the electron energy was set at 70 eV. The solvent delay time was 3 min.  
Mass spectra were compared to the NIST library (NIST11, NIST11s, FFNSC1.3) to identify relevant molecules. The compounds with a mass spectrometry match greater than 80% were screened out. The identification was then confirmed by combining the base peak, characteristic ion peak, and retention index (RI). The retention index was calculated based on a standard mixture of n-alkanes (C\({}_{8}\)-C\({}_{20}\)) using Equation (1):  
\[\mathrm{RI_{x}}=100\mathrm{n}+\ 100\times\left(\frac{\mathrm{RT_{x}-RT_{n}}}{ \mathrm{RT_{n+1}-RT_{n}}}\right) \tag{1}\]  
In this equation, \(\mathrm{RI_{x}}\) is the retention index of the component to be measured, \(\mathrm{n}\) is the number of carbon atoms in the n-alkanes, \(\mathrm{RT_{x}}\) is the retention time of each odorant (x), and \(\mathrm{RT_{n}}\) and \(\mathrm{RT_{n+1}}\) are the retention times of the n-alkanes eluted before and after the odorant (x) under identical chromatographic circumstances conditions.  
The volatile compounds with chemical standards were quantitatively analyzed using their respective calibration curves. The volatile compounds without chemical standards were tentatively identified, and their concentrations were approximately estimated using the internal standard according to Equation (2):  
\[\mathrm{C_{x}}=\frac{\mathrm{P_{x}\times C_{i}}}{\mathrm{P_{i}}} \tag{2}\]  
In this equation, \(\mathrm{C_{x}}\) represents the concentration of the compound to be tested, \(\mathrm{P_{x}}\) represents the peak area of the compound being tested, \(\mathrm{C_{i}}\) represents the concentration of the internal standard, and \(\mathrm{P_{i}}\) represents the peak area of the internal standard.
## 4 Direct Dynamics of the 6-Dof Redundant Planar Manipulator Robot  
To obtain the dynamic equations of the 6-DoF redundant planar manipulator robot, the L-E Method is applied to the robot shown in Figure 2. Taking into account the prismatic and rotational motions, the generalized coordinate vector is \(\mathbf{q}=[\theta_{1}\ d_{1}\ \theta_{2}\ d_{2}\ \theta_{3}\ d_{3}]\).  
where: \(l_{ci}\) is the length of the mass center of the link \(i\), \(i=1,2,3,4,5,6\).  
The dynamic model of the manipulator of 6-DoF, composed of fixed links interconnected by means of joints, can be described in its compact form (matrix), as shown in Equation (5) [29]:  
\[\boldsymbol{\tau}-\boldsymbol{\tau}_{0}=\mathbf{M}(q)\ddot{\mathbf{q}}+ \mathbf{C}(q,\dot{q})\dot{\mathbf{q}}+\mathbf{G}(q)+\mathbf{F}(q) \tag{5}\]  
where: \(\boldsymbol{\tau}\) is the (6 \(\times\) 1) vector of the generalized forces applied by the actuators on the joints; \(\boldsymbol{\tau}_{0}\) is the (6x1) vector of the input unknown disturbances; \(\mathbf{M}\) is the (6\(\times\)6) inertia matrix; \(\mathbf{C}\) is the (6 \(\times\) 6) centrifugal and Coriolis forces matrix; \(\mathbf{G}\) is the (6 \(\times\) 1) gravitational forces vector; \(\mathbf{F}\) is the (6 \(\times\) 1) vector of the frictions, and \(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}}\) are the vectors of the joint displacement/angle, speed and acceleration joint vectors, respectively.  
Since the manipulator works in a horizontal plane, then \(\mathbf{G}(q)\) is zero (gravity acts perpendicular to the motion plane). Vectors \(\boldsymbol{\tau}_{0}\) and \(\mathbf{F}\) are not considered in the construction of the model. The elements of the matrix \(\mathbf{M}(q)\) are presented in Appendix A.  
The centrifugal and Coriolis forces matrix \(\mathbf{C}(q,\dot{q})\) can be obtained through the Christoffel coefficients \(C_{ijk}(q)\) defined in Equation (6) [30]:  
\[C_{ijk}(q)=\frac{1}{2}\left[\frac{\partial M_{kj}(q)}{\partial q_{i}}+\frac{ \partial M_{ki}(q)}{\partial q_{j}}-\frac{\partial M_{ij}(q)}{\partial q_{k}}\right] \tag{6}\]  
where: \(M_{ij}(q)\) denotes the \(ij\)-th element of the inertia matrix \(\mathbf{M}(q)\). Thus, the \(kj\)-th element of the \(\mathbf{C}(q,\dot{q})\) matrix can be obtained as:  
\[\mathbf{c}_{kj}(q,\dot{q})=\left[\begin{array}{c}C_{1 jk}(q)\\ C_{2 jk}(q)\\ \vdots\\ C_{njk}(q)\end{array}\right]^{T}\dot{\mathbf{q}} \tag{7}\]  
Then, the resulting centrifugal and Coriolis forces vector is: \(\mathbf{c}(q,\dot{q})=\mathbf{C}(q,\dot{q})\dot{\mathbf{q}}\)  
Figure 2: Resting location and position of the mass centers of the 6 links.  
Using Equations (6) and (7), the centrifugal and Coriolis forces vector \(\mathbf{c}(q,\dot{q})\) shown in Equation (8) is obtained. The components of this vector are presented in Appendix A.  
\[\mathbf{c}(q,\dot{q})=[C_{i1}];\ i=1,\ldots,6 \tag{8}\]