esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Design
### Loading Scheme and Measuring Point Arrangement  
With reference to the _\"Regulations on Seismic Test Methods for Buildings (JG)/T 101-2015)\"_[26], the vertical loading device was first loaded to 40-60% of the design value, and then gradually loaded to 100%. The vertical loading value was kept constant during a cyclic loading test. The horizontal loading was controlled by displacement, and cyclic loading was performed according to the loading curve. The loading curve is given in Figure 3. In summary, before the specimen yielded, each level was loaded once, and after the specimen yielded, each level was cyclically loaded three times. When the specimen load dropped below 85% of the peak load, the test was completed [27]. The test setup is shown in Figure 4.  
Figure 2: Schematic diagram of three-dimensional model of specimen.  
The main measurement items and the arrangement of measurement points are shown in Table 4.  
The strain gauge numbers were A1-A19, and these strain gauges were used to monitor the yield of the edge reinforcement. The specific location is shown in Figure 5. The strain  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Measurement Points** & \begin{tabular}{c} **Arrangement and Means for** \\ **Measuring Points** \\ \end{tabular} \\ \hline \multirow{3}{*}{Overall deformation} & Lateral displacement of wallboard & \begin{tabular}{c} Displacement meter arranged along \\ the length direction of the \\ specimen section \\ \end{tabular} \\  & Relative displacement between top & \begin{tabular}{c} Displacement meter arranged along \\ top and bottom of wallboard \\ \end{tabular} \\  & \begin{tabular}{c} Fracture characteristics and width \\ \end{tabular} & \
\begin{tabular}{c} Manual observation \\ Strain gauge measurement. For the \\ measuring point arrangement, see \\ Figures 5 and 6 \\ \end{tabular} \\ \hline \multirow{2}{*}{Load} & Cracking, yield, and ultimate load &
\begin{tabular}{c} Sensor measurement connected \\ with loader \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: The main measurement items.  
Figure 3: Loading device. Notes: 1. Wall panel specimens; 2. Actuator; 3. Reaction wall; 4. Hydraulic jack; 5. Sensor; 6. Displacement gauge; 7. Reaction beam; 8. Upright stanchion; 9. Rigid strut beam; 10. The pull rod connection device; 11. Positive electrode method; 12. Anchor bolt; 13. Set bolt.  
gauge numbers for the concrete were H1-H11, and a vertical strain gauge was pasted at the center of the lower edge of the north wall plate. The conductor of the southern steel bar strain gauge was connected to the strain gauge outward through the lower central position, so this strain gauge was pasted on the northern wall plate.  
Figure 5: Layout diagram of steel strain gauge.  
Figure 6: Concrete strain gauge layout. Notes: The unit of length is mm.
# Influence of Polymer Types on the Mechanical Properties of Polymer-Modified Cement Mortars
## 3 Materials and Methods
### Materials and Test Variables  
Four different commercially available polymers were investigated in this study. Tables 1-3 show the chemical and mineral compositions of Type I ordinary Portland cement (OPC) satisfying the Korean Standard (KS), the particle size distribution of fine aggregate, and properties of polymers, respectively. The mixture proportions of the PCMs and substrate concrete are summarized in Table 4. The PCM specimens were cast with a cement-fine aggregate ratio of 0.5, a water-cement ratio of 0.4, and a polymer-cement ratio of 0 or 0.03. The amount of water was determined based on previous studies [14].  
The substrate concrete specimens were prepared with a cement content of 340 kg/m\({}^{3}\) and a water-cement ratio of 0.24 to produce a high strength based on the result of previous research [20]. The averaged compressive strength of the substrate concrete measured at 28 days was 82.3 MPa. As illustrated in Table 4, four different polymers, namely, acrylic, PVA, SBR, and EVA, with two different cement replacement ratios (0%, 3%), were added to modify cement mortars.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Chemical (\% by weight)**} & \multicolumn{3}{c}{**Mineral (\% by weight)**} \\ \hline Al\({}_{2}\)O\({}_{3}\) & CaO & SO\({}_{3}\) & SiO\({}_{2}\) & Fe\({}_{2}\)O\({}_{3}\) & MgO &
\begin{tabular}{c} Loss of \\ ignition \\ \end{tabular} & C\({}_{3}\)S & C\({}_{2}\)S & C\({}_{3}\)A \\
5.0 & 62.3 & 2.1 & 20.5 & 3.4 & 3.6 & 2.4 & 53.1 & 18.5 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical and mineral compositions of ordinary Portland cement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sieev Size** & **Mass Retained (\%)** & **Cumulative Mass Retained (\%)** \\ \hline
1.0 mm & 0.00 & 0.00 \\
850 \(\upmu\)m & 0.01 & 0.01 \\
600 \(\upmu\)m & 1.20 & 1.21 \\
425 \(\upmu\)m & 6.50 & 7.71 \\
300 \(\upmu\)m & 28.00 & 35.71 \\
212 \(\upmu\)m & 33.94 & 69.65 \\
150 \(\upmu\)m & 21.05 & 90.70 \\
106 \(\upmu\)m & 7.50 & 98.20 \\
75 \(\upmu\)m & 1.50 & 99.70 \\ Pan & 0.30 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sieve analysis of silica sand used.
# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 2 Basic Theory
### Bat Algorithm  
Bat Algorithm (BA) is a heuristic search algorithm, which was proposed by [PERSON] in 2010. By simulating the bat foraging behavior, local and global optimality can be obtained through appropriate iteration. The bat algorithm search process is as follows:  
Step 1.1: Parameter initialization. \(P\) is the initial bat population size. The maximum number of iterations is \(N\). The pulse frequency is in the range \([f_{\min},f_{\max}]\). \(r_{i}^{\,0}\) is the pulse rate. \(A\) is the maximum pulse sound intensity. The sound intensity attenuation coefficient is \(\alpha\). The frequency increase coefficient is \(\gamma\). \(D\) is the dimension of the search variable.  
Step 1.2: Population initialization. Randomly initialize the position of the bat individual \(x_{i}\) (\(i=1\), \(2\),..., \(D\)) within the search range. Find the best bat position \(x^{*}\) in the current search. Calculate the fitness value \(f(x_{i})\) of each bat, and find out the best location and fitness value of the current bat.  
Step 1.3: Movement speed and position update. Let the bats search in the \(D\)-dimensional space, the position and velocity of the bat \(i\) are \(x_{i}^{\,t-1}\) and \(v_{i}^{\,t-1}\) at (\(t-1\)) time. The current global optimal position is \(x^{*}\). For bat \(i\) at time \(t\), its position \(x_{i}^{\,t}\) and speed \(v_{i}^{\,t}\) update formula are given by:  
\[\left\{\begin{array}{l}f_{i}=f_{\min}+(f_{\max}-f_{\min})\beta\\ v_{i}^{\,t}=v_{i}^{\,t-1}+\Big{(}x_{i}^{\,t-1}-x^{*}\Big{)}f_{i}\\ x_{i}^{\,t}=x_{i}^{\,t-1}+v_{i}^{\,t}\end{array}\right. \tag{3}\]  
where \(\beta\in[0,1]\) is a random vector drawn from a uniform distribution.  
Step 1.4: Local search. Each bat randomly selects one from the current optimal solution, and its new solution is generated by random walks. The formula is shown as follows:  
\[x_{new}=x_{old}+\varepsilon A^{t} \tag{4}\]  
where \(\varepsilon\in[0,1]\) is the random number, while \(A^{t}=<\)\(A^{t}_{i}\)\(>\) is the average loudness of all the bats at this time step.  
Step 1.5: Pulse emission loudness and emission frequency update. First, a new random number is generated within [0,1], and then the size relationship between the current bat and the new bat adaptation is compared. New bats are accepted if the following conditions are met. The adjustment formula of the ultrasonic loudness \(A_{i}\) and the pulse rate \(r_{i}\) is:  
\[A_{i}^{t+1}=\alpha A_{i}^{t} \tag{5}\]  
\[r_{i}^{t+1}=r^{0}(1-\exp(-\gamma t)) \tag{6}\]  
where \(\alpha\) and \(\gamma\) are constants.  
Step 1.6: Global optimal evaluation. Calculate the fitness of all individuals in the current population, and record the optimal bat position and its fitness value.  
Step 1.7: Iteration termination. If the current iteration number \(t\) reaches the maximum number of iterations \(N\), the iteration is stopped, otherwise, repeated Steps 1.3-1.6. The iteration process is presented in Figure 1.
# Influence of Blade Wrap Angle on the Hydrodynamic Radial Force of Single Blade Centrifugal Pump
###### Abstract  
To investigate the effect of blade wrap angle on the hydrodynamic radial force of a single blade centrifugal pump, numerical simulation is conducted on the pumps with different blade wrap angles. The effect of the wrap angle on the external characteristics and the radial force of a single blade centrifugal pump was analyzed according to the simulation result. It is found that, with the increase of the blade wrap angle, the head and efficiency of the single blade centrifugal pump are improved, the H-Q curve becomes steeper, and the efficiency also increased gradually, while the high-efficiency area is narrowed. The blade wrap angle has a great effect on the radial force of the single blade centrifugal pump. When the blade wrap angle is less than 360\({}^{\circ}\), the horizontal component of the radial force is negative and the value is reduced with the increase of the wrap angle of the blade. When the wrap angle is larger than 360\({}^{\circ}\), the horizontal component of the radial force is positive and the value increases with the increase of the wrap angle. Under part-loading conditions, the radial force of the single blade pump is significantly reduced with the increase of the blade wrap angle. When the wrap angle is smaller than 360\({}^{\circ}\), the radial force decreases with the flow rate increase. In the condition that the wrap angle is larger than 360\({}^{\circ}\), the radial force increases with the flow rate increase.  
blade wrap angle; hydrodynamic radial force; single blade centrifugal pump; numerical simulation +
Footnote †: journal: Applied Sciences  
**Keywords:** blade wrap angle; hydrodynamic radial force; single blade centrifugal pump; numerical simulation
## 4 Radiometric Processing Workflow
### Sensor-Related Calibration
#### 4.2.3 Absolute Radiometric Calibration  
Absolute radiometric calibration determines the coefficients for the transformation between DN and the physical unit radiance for each spectral band [W m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\)]. Typically, a linear model with gain and offset parameters is appropriate [122; 141]. Two procedures have been published to accomplish this. The first approach uses a radiometrically calibrated integrating sphere. [PERSON] and [PERSON] [126] used a sphere equipped with an optometer for measuring the total radiance that is regularly calibrated against German national standard (PTB), and stated that calibration was valid for the spectral range from 380 nm to 1100 nm with a relative uncertainty of 5%. The second approach is to cross-calibrate a new device with an already radiometrically calibrated device. [PERSON] et al. [22] cross-calibrated their Ocean Optics STS point spectrometer with a radiometrically calibrated ASD FieldSpec Pro 4 by aligning the FOVs of both devices such that they pointed on almost the same area on a white reference panel. Calibration coefficients were obtained by comparing several spectra that were collected at different solar zenith angles to provide measurements covering different light levels and a linear relationship between ASD radiance values and STS digital counts at different light levels, which were normalized for different instrument integration times. A similar approach was performed by [PERSON] [131] for the 2D imager Tetracam mini-MCA. Both procedures require that the spectral response function of the sensors is known, since the spectral bands of the reference and the sensor need to be convolved to a common level to derive the band-specific calibration coefficients.  
The absolute calibration is often a challenging process, because the source must be traceable to radiance standards. The next section shows that absolute radiometric calibration can be omitted in cases where only reflectance, is needed and radiance is not. Besides, many factors influence the system radiometric response, such as for example, the shutter, stray light effects, impacts of temperature, and pressure [123; 142]. For applications that require very precise data, such as solar induced fluorescence estimation, these parameters also need to be considered.
## 2 Materials and Methods
### SDS-PAGE, Immunoblot and Lectin Blot Analysis  
The proteins from the cell extracts and supernatants of each strain were quantified using the Bradford Assay (Bio-Rad, Hercules, CA, USA) and 20 \(\upmu\)g of each sample was resolved with SDS-PAGE 12% at 100 V for 2 h. For the immunoblot detection, the proteins were transferred to PVDF Amersham(tm) Hypond(r) P Western blotting membranes (Amersham Biosciences, Piscataway, NJ, USA) in a Trans-Blot(r) SD Semi-Dry Transfer Cell (Bio-Rad, Richmond, CA, USA) at 20 V for 30 min. Subsequently, the PstS-1 and Ag85B antigens were immunodetected with TB71 and \(\alpha\)-Ag85 monoclonal antibodies (BEI Resources, TBVTRM Contract, Colorado State University, USA), respectively. Briefly, the transferred membrane was blocked with blocking solution (5.0% skimmed milk in TBS-Tween-20, 136 mM NaOH, 2 mM KCl, 25 mM Tris and 0.05% Tween-20) at room temperature (RT) and stirred for 2 h. The membrane was then incubated three times, for 10 min each in TBS-Tween and incubated overnight with the diluted primary antibodies: 1:100 for TB71 and 1:1000 for \(\alpha\)-Ag85 in blocking solution at 4 \({}^{\circ}\)C with shaking. The membranes were then washed three times with TBS-Tween 20 and incubated with secondary goat anti-mouse IgG (Jackson ImmunoResearch Inc., West Grove, PA, USA) for the PstS-1 membranes and anti-rabbit IgG (ZYMED, Carlsbad CA, USA) for the Ag85B membranes in blocking solution at RT for 2 h with stirring. The membranes were then washed three times for 10 min with TBS-Tween 20. Finally, the membranes were revealed with a substrate diluted with luminol and detected using a chemiluminescent substrate (DuoLuX; Vector Laboratories, Burlingame, CA, USA).  
For the lectin blot analysis, 1 \(\upmu\)g of the sample proteins was resolved by 12% PAGE, silver-stained and transferred to a PVDF membrane as previously described. The membranes were then analyzed for glycoprotein identification using a DIG Glycan Differentiation KitTM(Roche, Mannheim, Germany) according to the manufacturer's instructions. Additionally, glycosylation prediction profiles were performed to evaluate the in silico potential glycosylated residues using predefined algorithms from the bioinformatic software NetOGlyc-4.0 [61], Glycoslation Predictor GPP [62] and GLYCOPP V1.0 [63]. The SDS-PAGE immunoblots and lectin blots were visualized using a GS-800 TM Calibrated Imaging Densitometer (Bio-Rad). The image processing and further analysis was performed using the Quantity OneTM Image Analysis Software v.4.6 (Bio-Rad, Hercules, CA, USA) and Image J.1.53k.
## 8 Conclusions  
Robot-assisted surgery continues to evolve at a rapid pace, driven by innovations in artificial intelligence, imaging, augmented reality, and surgical platforms. These advancements are transforming every stage of urologic oncology care--from preoperative planning and intraoperative precision to postoperative recovery and remote surgical capabilities. Technologies such as machine learning-guided decision support, real-time tissue recognition, and immersive AR-based navigation are not only enhancing oncologic and functional outcomes but expanding access through single-port and telesurgical systems. While challenges remain, in terms of cost, training, and infrastructure, the integration of these emerging tools signals a shift toward more personalized, precise, and globally accessible urologic cancer care. Continued interdisciplinary collaboration, clinical validation, and equitable implementation will be essential to fully realize the potential of next-generation robotic surgery. Although many technologies show early promise, most require further validation in multicenter prospective trials. Key areas for future research include the impact of AR and biosensor tools on margin control and functional outcomes, cost-effectiveness evaluations of AI-driven tools, and safe scalability of telesurgery. Ultimately, success in this field will depend not only on technological innovation but on clinical validation, regulatory support, and equitable implementation across diverse healthcare settings.  
Conceptualization: S.K.; literature search: S.K., T.B., L.T., G.F., I.T. and N.K.; writing--original draft preparation: S.K. and T.B.; writing--review and editing: S.K., L.T., I.T. and N.K.; supervision: A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & **Maturity Level** & **Key Strengths** & **Key Study/Reference** \\ \hline AI-based Performance Prediction & Validated in multi-institutional studies & Outcome prediction, tailored recovery & [PERSON] et al. (2018, 2019) [7, 8] \\ \hline Hyperaccuracy 3D AR & Applied in complex RAPN and RAPP & Anatomical fidelity, margin accuracy & [PERSON] et al. (2020) [13] \\ \hline Confocal Microscopy (FCM) & Validated ex vivo; clinical feasibility shown & Digital workflow, rapid turnaround & [PERSON] et al. (2019) [23]; [PERSON] et al. (2021) [24] \\ \hline Raman Spectroscopy (RS) & Pilot intraoperative use; promising results & High diagnostic accuracy, integration with da Vinci & [PERSON] et al. (2019) [29] \\ \hline Indocyanine Green (ICG) Imaging & Routine in RAPN; well established & Perfusion mapping, tumor contrast & [PERSON] et al. (2020) [32]; [PERSON] et al. (2013) [33] \\ \hline iKidney AR System & First-in-human case; early stage & Automation, eliminates manual overlay & [PERSON] et al. (2023) [16] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technology Maturity and Strengths.
## 3 Results and Discussion
### UPV and Damage Degree (DD)
[14] reported in their study  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Ultrasonic Pulse Velocity (m/s)**} \\ \hline  & \multicolumn{4}{c}{**Temperatures**} \\
**Mixtures** & **23 \({}^{\circ}\)C** & **400 \({}^{\circ}\)C** & **600 \({}^{\circ}\)C** & **800 \({}^{\circ}\)C** \\ \hline M1 & 4717 & 4292 & 2840 & 1220 \\ M1-R & & 4546 & 4357 & 2621 \\ M2 & 4651 & 4288 & 2780 & 1377 \\ M2-R & & 4511 & 4435 & 2508 \\ M3 & 4648 & 4230 & 2745 & 1369 \\ M3-R & & 4478 & 4348 & 2482 \\ M4 & 4642 & 4167 & 2814 & 1315 \\ M that the degree of damage of steel fiber concrete exposed to 600 \({}^{\circ}\)C decreased from 0.63 to 0.10 after re-curing with water. [PERSON] et al. [30] determined that the DD value decreased from 0.38 to 0.12 after 600 \({}^{\circ}\)C heating. When the temperature rose to 800 \({}^{\circ}\)C, deteriorations in the structure of the concrete caused more serious damage. The damage degree of the samples ranged between 0.70 and 0.74. Re-curing after 800 \({}^{\circ}\)C heating improved the DD value to between 0.42 and 0.48. In the samples (M3 and M4) where basalt fiber was replaced with steel fiber, no remarkable change was observed in the UPV values both after heating and water re-curing. However, the M5 series with PPF added is the series in which re-water curing is most effective after all temperatures. This is so much so that the UPV value obtained as a result of the water re-curing process after 400 \({}^{\circ}\)C heating was almost at the initial value. Evidence of self-healing of some cracks on fiber-reinforced concrete as a result of water re-curing is given in Figure 3. As seen in Figure 3, water voids and cracks are partially and completely filled.
## 4 Numerical Results with Fixed Time-Step Sizes
### Experiment 2 with Large Value of Parameter \(m\)  
In this experiment, the following parameters are used:  
\[m=7.2\;,\;\alpha=11.4\;,\;\;c_{2}=0.0042\;,\;\;N=500\;,\;x_{0}=0.48\;,\;\; \Delta x=5\times 10^{-4}\;,\;\;t^{0}=0.9\;,\;\;t^{\rm fin}=1.5 \tag{20}\]  
Because the value of \(m\) is larger, the diffusion coefficient depends on the \(x\) and \(t\) variables more strongly. The CFL limit is increasing from \(h_{\rm CFL}^{EE}(t^{0})=8.65\times 10^{-7}\) to \(h_{\rm CFL}^{EE}(t^{\rm fin})=5.44\times 10^{-6}\) and the stiffness ratio is decreasing from \(1.16\times 10^{6}\) to \(4.86\times 10^{5}\). The errors as a function of the time-step size are presented in a log-log diagram in Figure 8. In Figure 9, one can see the analytical \(u\) function at the initial and final time, as well as the numerical solution at the final time in the case of the CCL and the LH methods. In Figure 10, we again plotted the time development of the maximum errors of all but the RK4 schemes for a fixed time-step size \(h=10^{-3}\).  
Figure 5: Maximum errors as a function of the time-step size for Experiment 1. The numerical order of convergence of the algorithms are the slopes of the error curves.  
Figure 6: The variable \(u\) as a function of \(x\) in the case of the initial function \(u^{0}\), the analytical solution at the final time, the CCL algorithm for \(h=10^{-5}\), and the LH algorithm for \(h=0.0013\) in the case of small value of \(m\) (Experiment 1). It is worth emphasizing again that for these time-step sizes, explicit Runge–Kutta algorithms are unstable.  
Figure 7: The time development of the errors, i.e., the absolute difference between the analytical solution and that of the stable numerical methods, for \(h=2\times 10^{-4}\) as a function of time.  
Figure 8: Maximum errors as a function of the time-step size for parameter set (20) (Experiment 2).  
Figure 9: The variable \(u\) as a function of \(x\) in the case of the initial function \(u^{0}\), the analytical solution at the final time, the CCL algorithm for \(h=1.2\times 10^{-5}\), and the LH algorithm for \(h=0.0016\) in the case of large value of \(m\) (Experiment 2).