esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: During cochlear implantation, the electrode array has significant friction with the sensitive endocochlear lining and causes mutual mechanical trauma while the array is being inserted. Both, the impact of insertion speed on electrode friction and the relationship of electrode insertion force and friction have not been adequately investigated to date. Methods: In this study, friction coefficients between a CI electrode array (31.5 mm) and a tissue simulating the endocochlear lining have been acquired, depending on different insertion speeds (0.1, 0.5, 1.0, 1.5, and 2.0 mm/s). Additionally, the electrode insertion forces during the placing into a scala tympani model were recorded and correlated with the friction coefficient. Results: It was shown that the friction coefficient reached the lowest value at an insertion speed of 0.1 mm/s (0.24 \(\pm\) 0.13), a maximum occurred at 1.5 mm/s (0.59 \(\pm\) 0.12), and dropped again at 2 mm/s (0.45 \(\pm\) 0.11). Similar patterns were observed for the insertion forces. Consequently, a high correlation coefficient (0.9) was obtained between the insertion forces and friction coefficients. Conclusion: The present study reveals a non-linear increase in electrode array friction, when insertion speed raises and reports a high correlation between friction coefficient and electrode insertion force. This dependence is a relevant future parameter to evaluate and reduce cochlear implant insertion trauma. Significance statement: Here, we demonstrated a dependence between cochlear electrode insertion speed and its friction behavior and a high correlation to insertion force. Our study provides valuable information for the evaluation and prevention of cochlear implant insertion trauma and supports the optimization of cochlear electrode arrays regarding friction characteristics.  
friction coefficient; friction conditions; insertion force; insertion speed; insertion trauma; cochlear electrode array +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Characterization  
Crystalline phases were characterized by X-ray diffraction (XRD, X'Pert PRO Theta/2 theta of Analytical, Cu K\(\alpha\) radiation, PANalytical, The Netherlands). The pattern was recorded over the angular range 20\({}^{\circ}\)-70\({}^{\circ}\) (20) with a step size of 0.0334\({}^{\circ}\) and a time per step of 100 s, using Cu K\(\alpha\) radiation (\(\lambda\) = 0.154056 nm), with a working voltage of 40 kV and current of 100 mA. The morphology of samples was evaluated using primary electrons images of field emission scanning electron microscopy (FE-SEM, Hitachi S-4700, Tokyo, Japan). An image processing and analysis program (Leica Qwin, Leica Microsystems Ltd., Cambridge, UK) were performed to determine the average particle size from FE-SEM micrographs. In addition, a detailed morphology and crystal structure of the sample was evaluated using a transmission electron microscope (TEM/STEM, JEOL 2100F, Tokyo, Japan) operating at 200 kV and equipped with a field emission electron gun providing a point resolution of 0.19 nm. For TEM sample preparation, the particles were carefully suspended in ethanol and dispersed usingan ultra-sonication bath for 10 min. The suspension was dropped on a lacey carbon copper TEM grid. After the evaporation of ethanol, the particles were kept at the grid.  
Antifungal activity test. The pathogenic fungus selected for testing is Aspergillus niger (CECT 2807). Antifungal tests were performed by the Bauer-Kirby disk diffusion assay with some modifications. First, the culture of A. niger (initial concentration of \(5.20\times 107\) spores/mL) was inoculated on the surface of Petri dishes. After that, filter paper disks were impregnated with a suspension of 0.5% different inorganic composites ZnO/Clay. Then, the inoculated Petri dishes were incubated at 37 \({}^{\circ}\)C for 7 days. The effectiveness of ZnO/Clay samples was evaluated by measuring the inhibition diameter of the grown fungus in the Petri dish. For the paint antifungal activity, no filter paper disks were used. The paint was in direct contact with the fungi culture. Two kinds of the test were performed to know the antifungal activity of modified paint, one with fresh paint and another with dry paint. The dry paint was obtained after a 90 \({}^{\circ}\)C thermal treatment for 24 h. All tests were performed in triplicate and the values were expressed in millimeters.
## Model
### Solution Method  
Essentially, the model described involves the propagation of elastic waves. To solve it, a finite difference method has been selected in this section. The governing differential equation has been dispersed using a central difference scheme. After this, the differential equations are converted into algebraic equations and solved using MATLAB programing.  
To begin with, the drill-string is segmented into \(N\) sections, spanning from the top to the bottom of the well. Difference grids are formed while setting time step \(\tau\) and space step \(h\). Subsequently, the node displacement of the drill-string is represented as \(u(p,k)\) where \(p\) and \(k\) represent the position and time, respectively, and are abbreviated as \(u(p\cdot h,k\cdot\tau)\). There is nonlinear term \(\frac{\partial(EA\frac{\partial u}{\partial k}  
\[F_{f}(p,k)=\text{sgn}(u(p,k)-u(p,k-1))\int_{(p-\frac{1}{2})h}^{(p+\frac{1}{2})h}F \text{\emph{t}}ds \tag{18}\]  
\[C^{p}=\int_{(p-\frac{1}{2})h}^{(p+\frac{1}{2})h}cds \tag{19}\]  
\[A^{p}=\int_{(p-\frac{1}{2})h}^{(p+\frac{1}{2})h}\rho Ads \tag{20}\]  
\[g^{p}=\int_{(p-\frac{1}{2})h}^{(p+\frac{1}{2})h}g_{s}\text{cos}\overset{-}{ \text{\emph{a}}}ds \tag{21}\]  
Substitute Equations (16)-(21) into (15), we can obtain the recurrence algorithm of drill-string axial displacement:  
\[u(p,k+1)=\frac{2\tau^{2}}{2A^{p}+C^{p}\tau}\bigg{[}T^{p+\frac{1}{2}}-T^{p- \frac{1}{2}}-F_{f}(p,k)+g^{p}+\frac{2A^{p}}{\tau^{2}}u(p,k)+(\frac{C^{p}\tau-2 A^{p}}{2\tau^{2}})u(p,k-1)\bigg{]} \tag{22}\]  
Difference solution format (22) is explicit. If we know the displacement of two former time steps, we can calculate drill-string displacement at any time step, and then the drilling parameters, such as axial tension force and WOB, can be obtained.  
In order to achieve convergence of the solution, there must be a specific relationship between the time step \(\tau\) and the space step \(h\). By utilizing the Fourier error analysis method, it has been proven that the difference scheme of Equation (22) is only stable under certain conditions. This condition can be expressed as the convergence condition:  
\[\tau\leq\frac{h}{\sqrt{\frac{\bar{\rho}}{\rho}}} \tag{23}\]
## 3 Results
### Simulation of Confocal, Linear Array, Transducer  
The mechanism affecting the performance of the 1-3 piezoelectric composite was examined through the electric impedance responses. The simulation was conducted with free boundary conditions to observe the vibration mode shape related to the transducer structure only. The unit and zero voltages are applied at the top and the bottom surfaces, respectively. Figure 9 compares the electric impedances of the single-phase and the 1-3 composite transducers at each frequency condition. Electric impedance in the single-phase transducer did not show the clear resonance peak at the designed thickness of the  
Figure 10: Vibration mode shapes at the operation frequency (2 MHz) of (**a**) the single-phase and (**b**) the 1–3 composite transducers, where the solid and the dashed lines denote the mode shapes and the neutral line.  
Figure 9: Electric impedance responses of the single- and the 1–3 composite transducers designed for (**a**) 2 MHz, (**b**) 3 MHz, (**c**) 4 MHz, and (**d**) 5 MHz.  
piezoelectric material. For example, Figure 10 demonstrates the vibration mode shapes in the single and the 1-3 composite transducers. The 1-3 composite transducer predominantly deformed along the thickness direction while the lateral vibration was well suppressed. In contrast, vibration pattern in the single-phase transducer was highly affected by the higher modes of the lateral vibration. Nonetheless, at higher frequency conditions (e.g., 5 MHz in Figure 9d), the resonance peak became more dominant, approaching the designed frequency range. Therefore, the 5-MHz, single-phase transducer could produce a relatively high acoustic pressure output compared to the lower frequency cases.  
In contrast, electric impedance in the 1-3 composite transducer exhibited the predominant resonance peak at the designed frequency range. The frequency bandwidth was also relatively broad with the 1-3 composite design. Moreover, the secondary resonance frequencies were far apart from the main resonance band. Figure 11 shows the variation of the electric impedance curve with respect to the change in the dimension ratio of the piezoelectric rod in the 1-3 piezoelectric composite. In a high dimensional ratio (e.g., \(>\)200/320 \(\upmu\)m, width/thickness), the frequency peak at the resonance seems like being affected by the other vibration mode, showing a kind of discontinuity around the resonance and the anti-resonance frequencies. Nonetheless, in the width cases below 200 \(\upmu\)m, the impedance level and the resonance frequency were all very similar with a difference of about 2%.
## 3 Improvement of [PERSON]'s \(p\)-\(y\) Curve  
### The Change in Undrained Shear Strength, \(C_{u}\), with Depth  
For cohesive soil, because the \(p_{u}\) in Table 1 is often described by \(C_{u}\), the undrained shear strength \(C_{u}\), has a great influence on the results, when a \(p\)-\(y\) curve is used to calculate the internal forces and lateral displacement of piles. _The Code for Pile Foundations of Port Engineering_[16] gives the \(p\)-\(y\) curve for \(C_{u}<96\) kPa and \(C_{u}>96\) kPa, respectively. \(\varepsilon_{50}\) and \(C_{u}\) is taken as a fixed value during calculation. According to the basic principle of soil mechanics [17], even for the same soil, undrained shear strength \(C_{u}\) increases with depth, and so is not a fixed value. At present, there are three main ways to obtain the undrained shear strength, \(C_{u}\): (1) direct measurement by field or indoor test; (2) the effective consolidation stress method [18], which considers the failure of a soil mass to be a sudden undrained shear process, where the water content and strength of soil elements remain unchanged; here, the undrained shear strength can be measured by the effective consolidation stress on the potential fracture surface before failure; and (3) the empirical fitting method, which is based on the main factors that affect the undrained shear strength, \(C_{u}\), of the soil mass, and can give the \(C_{u}\) according to several parameters that are easy to measure, hence this method's name.  
[PERSON] [19] compared the undrained shear strength measured by in situ field tests and indoor tests, with the undrained shear strength obtained by an empirical formula. The results obtained from the empirical fitting method and the tests were consistent, but the calculated results from the effective stress method were roughly twice the measured ones. The main reason for this was that the undrained cohesion, \(c\), had a large impact. [PERSON] [20], [PERSON] [21], and others found that the undrained shear strength of cohesive soil increases linearly with depth.  
In this paper, the authors adopt the empirical fitting method due to a lack of measured data. That is, the undrained shear strength is assumed to follow a linear growth rule along the depth, and the reference value of \(\varepsilon_{50}\) is obtained from Table 4:  
\[C_{u}=ar^{\prime}z \tag{14}\]  
where, \(r^{\prime}\) is the effective weight of the soil, and \(a\) is the effective coefficient, which is \(0.244\)[19].
## 3 Results and Discussion
### Optimizing Thermal Load Predictions: Best Hybrid IFS-ML Models
This demonstrates that highly accurate thermal load predictions can be achieved using multiple combinations of predictor variables, rather than relying on a single \"best\" configuration. These outcomes outperform those reported in previous studies on thermal load prediction for LEBs, confirming the robustness and versatility of the proposed hybrid IFS-ML framework. When we compare our findings with those of other research publications, we may conclude the following:  
* The incorporation of IFS significantly improved prediction accuracy by isolating critical predictors such as building geometry and material properties. This aligns with recent studies emphasizing feature optimization, including hybrid models combining metaheuristic algorithms (e.g., Particle Swarm Optimization) with XGBoost and SVR [8] and interpretable classifiers prioritizing variables like glazing area and relative compactness [37]. Our results extend these approaches by demonstrating that systematic feature engineering (via IFS) reduces overfitting while maintaining robustness across climates;
* The XGBoost model emerged as the optimal predictor during training across all climates, corroborating its dominance in long-term load prediction tasks reported in  
Figure 13: A statistical examination of the best combinations of input for predicting cooling loads. prior work [38]. However, in Mediterranean climates, the SVM model outperformed others for cooling loads, while ELM showed niche superiority for heating loads. This climate-specific divergence contrasts with studies that prioritize general model performance (e.g., LightGBM achieving R\({}^{2}\) = 0.9959 globally [39]), highlighting the need for regionally tailored frameworks--a gap underexplored in recent literature [40]; Our models achieved near-perfect Pearson correlation (close to 1) and low dispersion, surpassing benchmarks set by state-of-the-art techniques such as hybrid CNN architectures (MAE < 2 MW [40]) and LightGBM ensembles (CVRMSE = 5.25% [39]). Unlike studies focusing on single-model superiority (e.g., TPE-LightGBM with R\({}^{2}\) = 0.9981 [8]), we identified multiple predictor combinations that maximize accuracy, offering flexibility for diverse design scenarios; By bridging feature selection, model optimization, and climate adaptability, this work contributes to the operationalization of hybrid AI systems for sustainable architecture--a priority underscored in recent frameworks integrating ML with climate models [41]. Our methodology aligns with calls for interpretable, actionable tools to guide HVAC optimization and envelope design [8,37].
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 2 Parallel Auto-Encoder Framework
### Motivation  
As mentioned above, modal information such as frequencies and their corresponding mode shape parameters will be utilized as input separately. In the given steel frame structure shown in Section 3.1 below, there are in total seven frequencies and seven groups of mode shape parameters. Because one mode shape is associated with one frequency and different mode shapes are actually unrelated to each other. It is more reasonable to deal with each of them specifically than consider them together. Also, frequencies and mode shapes are in different scale magnitudes and it is numerical unstable to deal with them in one vector, especially in the normalization process. The frequencies generally have much larger values than mode shapes and they could dominate the training of the model. In addition, as vibration-based methods are generally vulnerable to diverse types of noise effect in the damage identification process, such as the measurement noises in vibration data or uncertainties in the system. In consequence, some unnecessary information (e.g., measurement noises, uncertainties and redundant data) may not be well excluded at the first few layers in the high dimensional features which are formed by all frequencies and mode shape parameters. Furthermore, introducing sparsity constraint in the training of an auto-encoder model can achieve better de-noising performance at the dimension reduction stage. Therefore, the proposed framework has been designed carefully as a parallel architecture based on sparse auto-encoders, with considering the difficulties mentioned above.
## 3 Results and Discussion
### Combustion Modes Comparison
As the RCCI concept relies on highly diluted combustion, some of the exhaust gases are recirculated to avoid high pressure gradients. Therefore, the total exhaust mass is reduced. This parameter is used to convert the concentration values of the emissions to mass flow quantities. Then, the emission values on a mass basis are considerably reduced for this combustion mode. The same can be stated for the RCCI-SHV concept, as the ICE relies on RCCI combustion. Compared to CDC, the dual-mode combustion presented a total reduction of almost 30% while the hybrid concept decreased the emissions by half, which confirms that advanced combustion modes in addition to hybrid powertrains present a huge potential to decrease the GHG emissions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cycle** & **Mode** & **NOx [g/km]** & **HC [g/km]** & **CO [g/km]** & **Soot [g/km]** & **CO\({}_{2}\) [g/km]** \\ \hline \multirow{3}{*}{WLTC} & CDC & 0.366 & 0.748 & 1.162 & 0.015 & 132.065 \\  & Dual-mode & 0.263 & 1.849 & 5.120 & 0.003 & 95.007 \\  & RCCI-SHV & 0.078 & 1.138 & 1.726 & 0.001 & 62.292 \\ \hline \multirow{3}{*}{RDE} & CDC & 0.256 & 0.944 & 1.451 & 0.013 & 123.606 \\  & Dual-mode & 0.165 & 1.806 & 5.105 & 0.002 & 84.591 \\ \cline{1-1}  & RCCI-SHV & 0.056 & 1.147 & 1.690 & 0.001 & 60.012 \\ \cline{1-1}  & - & 0.08 & 0.17 \({}^{*}\) & 0.5 & 0.005 & - \\ \hline \hline \end{tabular}  
* The limit for HC is a function of the total emission of NOx in Euro 6 legislation. The value of 0.17 g/km stands for the sum of HC + NOx.  
\end{table} 
Table 7: Engine-out emissions for the different modes and the two driving cycles with the respective Euro 6 limits in g/km. Figure 16: SOC, ICE on-off state, and cumulative fuel consumption for the hybrid powertrain (**a**) and instantaneous fuel consumption comparison for the different concepts (**b**) during the RDE cycle.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 3 Architecture  
This section delves into the overall architecture of SDNPS. Learning from the traditional Internet, we adopt hierarchical thinking to organize brokers and manage routing. All of the participants are partitioned into multiple clusters according to their regional characteristics (belonging to the same data center or LAN). Each cluster is a logically-autonomous area containing a representative broker and some agent brokers, as well as a certain number of SDN-configurable switches, communicating with other clusters through border switches. All of the clusters are treated equivalently. Above this, we adopt a global server to manage topology and to compute routing holistically. It is important to note that we devise an alternative distributed schema (it is still under testing) in order to avoid single point failure and heavy burden on the server. As depicted in Figure 1, the system is logically structured in three layers: the switch hardware layer, the cluster controller layer and the global management layer. The switch hardware layer does not run complex control protocols. Its primary task is filtering and forwarding events. The controller layer runs both the OpenFlow protocol and network control applications. A controller is meanwhile a representative broker of the cluster where it is located. The global management layer consists of a major server and a standby server, which have global information about the physical topology and subscription topology. The servers enable central traffic engineering by constructing topic-connected overlay networks based on global traffic prediction and link information collection.  
As depicted in Figure 2, we devise a strategy-driven topic tree aggregation routing schema. From the subscription list and topic tree, we extract the subscription topology in incremental storage mode. From link state database(LSDB) and cluster information, we extract the cluster-level physical topology. Then, we compute multicast routing paths per topic based on the subscription topology and cluster-level physical topology, as well as the strategy base in our schema.
## 6 Open Challenges, Future Prospects and Research Directions
### Exploring Hybrid Systems
#### 6.4.5 Error Correction Codes and Modulated Signal Optimization  
The future directions for ECC and modulated signal optimization in FSO communication are promising and diverse, encompassing advancements in algorithm development, integrated coding and modulation techniques, and innovative approaches like capacity-approaching PAS.
# Enhancing 3D-Printed Clay Models for Heritage Restoration Through 3D Scanning
## 2 Materials and Methods
### 3D-Printing Methodology
#### 2.3.2 3D Printing  
The 3D printer used is a specialized model for ceramics, the DELTA WASP 40100 Clay, equipped with an LDM WASP Extruder XL controlled extrusion system designed to handle pasty materials such as clay. The device features a high-capacity extrusion chamber and a printing platform capable of supporting the variations in weight and size of ceramic pieces.  
A single nozzle size of 4 mm is used throughout the printing process for cases 1 and 2. This nozzle is chosen to optimise printing speed, ensuring efficient production while maintaining acceptable quality. Although finer details might have benefited from smaller nozzles, the 4 mm nozzle provided a good balance between detail reproduction and printing efficiency for the specific characteristics of the objects being reproduced. In the third part, however, a 2 mm nozzle is used for the presence of finer details. Case studies 1 and 2 are printed with a layer height of 1.8 mm, and case study 3 with a layer height of 0.9 mm. In all cases the printing speed is 2000 mm/min and a power of 0.5 MPa. In addition, due to the limited print size and the size of the parts, each part is printed in several parts. The printing properties of each part are described in Table 1 below.  
The printing process began with the preparation of the clay, ensuring a homogeneous mix and the elimination of air bubbles that could affect the quality of the pieces. Once prepared, the clay is loaded into the 3D printer's extrusion chamber. During printing, strict  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Plasticity** & **Nozzle Size** & **Layer Height** & **Printing Speed** & **Power** \\
**Study** & **Index** & & & & \\ \hline
1 & 23.3\% & 4 mm & 1.8 mm & 2000 mm/min & 0.5 MPa \\
2 & 11.8\% & 4 mm & 1.8 mm & 2000 mm/min & 0.5 MPa \\
3 & 11.8\% & 2 mm & 0.9 mm & 2000 mm/min & 0.5 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the print parameters for the case studies.  
control is maintained over extrusion speed and ambient temperature, critical factors to prevent deformations in the pieces. Figure 5 shows images of the printing process.  
The printing is conducted layer by layer, following the digital models previously obtained through 3D scanning. Printing times varied depending on the complexity and size of each object, with larger and more detailed pieces requiring more time.  
Figure 5: 3D-Printing process (**a**) Case study 1: Representative baluster of balustrades; (**b**) Case study 2: Pinnacle in water sheet; (**c**) Case study 3: Pinnacle in ornamental fountain.  
Finally, the printed pieces underwent a slow drying process to avoid cracking or deformation during 24 h at 105 \({}^{\circ}\)C, followed by firing in a ceramic kiln at suitable temperatures (980 \({}^{\circ}\)C) for the type of clay used, for 16 h and 45 min. This process solidified the structures, ensuring the durability and strength of the reproduced pieces.