esa-sceva/satcom-chunk-collection huggingface dataset:
# Detailed Design and Aerodynamic Performance Analysis of a Radial-Inflow Turbine
## 3 Preliminary Design
### Nozzle Preliminary Design  
The nozzle blade profile is usually designed and optimized according to flow velocity data [7,22]. However, it is necessary to define an initial profile for the nozzle blades. Though it is applicable to use NACA 4-digit and 5-digit airfoils to design and modify the nozzle blades in an iterative process, we have employed a general blade thickness distribution on a parabolic-arc camberline according to [23]:  
\[x^{2}+\frac{c-2a}{b}xy+\frac{(c-2a)^{2}}{4b^{2}}y^{2}-cx-\frac{c^{2}-4 ac}{4b}y=0 \tag{16}\]  
Equation (16) includes a general parabolic-arc camberline. Since a symmetric nozzle blade will be easier to design and manufacture, Equation (16) can be simplified to a straight line (\(x=0\)) by assuming \(a=0.5c\). The blade thickness distribution is adopted according to:  
\[t=\ t_{ref}+\Big{[}t_{max}-t_{ref}\Big{]}g^{e} \tag{17}\]  
\[t_{ref}=t_{LE}+[t_{TE}-t_{LE}](x/d) \tag{18}\]  
where  
\[\left\{\begin{array}{ll}\xi=x/d&x\leq d\\ \xi=(c-x)/(c-d)&x>d\end{array}\right. \tag{19}\]  
\[e=\sqrt{0.4d/c}[0.95(1-\
icefrac{{x}}{{c}}/c)(1-\xi)+0.05] \tag{20}\]  
Default values presented in Table 2 were considered for the constants in the Equations (17)-(20) according to [PERSON] [7].  
[PERSON] et al. [24] recommend a minimum ratio of the nozzle exit radius to the rotor inlet radius given by Equation (21). The tangential velocity, meridional velocity and other flow data at the nozzle exit can then be computed by conservation of angular velocity, mass balance and equation of state. In addition, the nozzle passage width (blade height) is assumed to be constant.  
\[r_{3}/r_{4}=1+2b_{4}\sin\alpha_{4}/r_{4} \tag{21}\]  
The nozzle throat width is defined by the sine rule for subsonic and choked flow exit according to:  
\[\left\{\begin{array}{ll}\
icefrac{{o}}{{s_{3}}}=\sin\alpha_{3}&M<1\\ \
icefrac{{o}}{{s_{3}}}=\
icefrac{{o}}{{s_{3}}}/(\
icefrac{{o}}{{e}},\
icefrac{ {a}}{{s_{*}}})&M=1\end{array}\right. \tag{22}\]  
To set the nozzle setting angle, \(\gamma_{3}\), an iterative process will be used based on the nozzle throat width, the number of nozzle blade, and exit blade pitch to chord ratio. [PERSON] et al. [25] and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(t_{LE}/c\) & 0.025 \\ \(t_{TE}/c\) & 0.012 \\ \(t_{max}/c\) & 0.06 \\ \(d/c\) & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constant parameters for the thickness distribution of the nozzle blade profile [7].  
[PERSON] [9] suggested selecting 0.75 and 0.8 for the nozzle exit blade pitch to chord ratio, respectively. The number of nozzle blade should be the lowest value, which satisfies Equation (23) [7]:  
\[\frac{4s_{3}\sin(\beta_{2^{\prime}}-\alpha_{3})}{c\sin\beta_{2^{\prime}}[1+r_{3} \sin\alpha_{3}/(r_{2^{\prime}}\sin\alpha_{2^{\prime}})]}\leq 1 \tag{23}\]  
where \(\alpha_{2^{\prime}}\) and \(\beta_{2^{\prime}}\) are absolute flow angle and blade angle at the nozzle inlet station, respectively.
## 5 Empirical Test and Result Analysis
### Heterogeneity Analysis of Industry Monopoly
In regression (1), the regression coefficient of _Ludigital_ is \(-0.0519\), which is significant at the 1% level, indicating that digital transformation does not utilize the ESG performance of enterprises under high industry monopolies. The regression coefficient for _Ludigital_ in Regression (2) is \(0.0140\), significant at the 1% level, indicating that digital transformation has leveraged the ESG performance of firms under low industry monopolies. Columns (3) and (4) report the regression results of the high industry monopoly group and the low industry monopoly group in the case of adding control variables, and the results are basically the same as above. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\ \cline{2-4}  & **High Industry Monopoly** & **Low Industry Monopoly** & **High Industry Monopoly** & **Low Industry Monopoly** \\ \cline{2-4}  & **ESG** & **ESG** & **ESG** & **ESG** \\ \hline _Ludigital_ & \(-0.0519\)*** & \(0.0140\)*** & \(-0.0233\)** & \(0.0120\)*** \\ \hline  & (0.0085) & (0.0027) & (0.0096) & (0.0025) \\ \hline lnsize & & & \(0.0786\)*** & \(0.0683\)*** \\ \hline  & & & (0.0103) & (0.0033) \\ \hline lev & & & \(-0.0140\) & \(-0.0673\)*** \\ \hline  & & & (0.0617) & (0.0209) \\ \hline growth & & & \(-0.0025\) & \(-0.0037\) \\ \hline  & & & (0.0066) & (0.0030) \\ \hline roa & & & \(-0.1120\) & \(-0.0898\) \\ \hline  & & & (0.1880) & (0.0555) \\ \hline cashflow & & & 0.1640 & 0.1110 *** \\ \hline mb & & & (0.1800) & (0.0474) \\ \hline mb & & & \(-0.0227\) & 0.0158 * \\ \hline  & & & (0.0316) & (0.00887) \\ \hline fish1 & & & 0.0136 & 0.0623 *** \\ \hline  & & & (0.0678) & (0.0206) \\ \hline dlab & & & 0.2500 & 0.0222 \\ \hline  & & & (0.1820) & (0.0512) \\ \hline big4 & & & 0.1400 *** & 0.1500 *** \\ \hline  & & & (0.0331) & (0.0105) \\ \hline  & & & 0.0176 & 0.0993 *** \\ \hline  & & & (0.0263) & (0.0095) \\ \hline \_cons & 2.9320 *** & 2.7550 *** & 1.0390 *** & 1.0250 *** \\ \hline  & (0.0355) & (0.0301) & (0.2280) & (0.0844) \\ \hline Industry & control & control & control & control \\ \hline Year & control & control & control & control \\ \hline \(N\) & 787 & 8359 & 771 & 8091 \\ \hline \(R^{2}\) & 0.233 & 0.148 & 0.447 & 0.292 \\ \hline adj. \(R^{2}\) & 0.206 & 0.142 & 0.407 & 0.287 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Heterogeneity analysis of industry monopoly (* \(p<0.1\), ** \(p<0.05\), *** \(p<0.01\)).
## 2 System Model
### Model Constraints
#### 2.2.2 Diesel Generator Constraints  
For the diesel generator, we first need to consider its ramping power constraint:  
\[-\Delta P_{DG}\leq P_{DG}(k)-P_{DG}(k-1)\leq\Delta P_{DG} \tag{4}\]  
This means that the change in the generator's power output between adjacent time periods cannot exceed \(\Delta P_{DG}\). Additionally, the generator's power output must not go beyond its maximum or minimum power limits:  
\[P_{DG}^{\text{min}}\leq P_{DG}(k)\leq P_{DG}^{\text{max}} \tag{5}\]  
Furthermore, we need to consider the minimum on/off duration constraints for the generator:  
\[t_{on}(k) \geq T^{on} \tag{6}\] \[t_{off}(k) \geq T^{off} \tag{7}\]  
Here, \(t_{on}(k)\) represents the current continuous running time of the generator, and \(t_{off}(k)\) represents the current continuous off time of the generator. This means that the generator needs to run continuously for \(T^{on}\) time periods before it can be turned off and must remain off for \(T^{off}\) time periods before it can be turned on.
# Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
## 2 Problem Formulation
### Construction of Classification Rules  
Through the above processes, the FCM algorithm can be applied to the classification of any type of dataset. The membership matrix \(\mathbf{U}\) and category prototypes \(\mathbf{v}_{i}\) are obtained for constructing classification rules. Generally, the maximum membership is used to determine a datum point's belonging category. However, we intuitively believe that data points with a membership close to 0.5 could not be convincedly classified into any categories, e.g., the uncertain regions in Figure 1. Therefore, the proposed approach describes the uncertain region first, then constructs classification rules with consideration of both uncertain regions and the membership matrix. Assuming the uncertain region is expressed as \(\Omega\), the classification rules could be presented as seen below.  
\[\left\{\begin{array}{ll}Rule\,1:&\text{IF}\,\mathbf{x}_{k}\in\Omega,\text{ THEN}\,y_{k}\text{ needs ``clerical review''}\\ Rule\,i:&\text{IF}\,\mathbf{x}_{k}\
otin\Omega\text{ and }u_{ik}>\underset{\forall j \
eq i}{\text{\emph{ijk}}},\text{ THEN }y_{k}\text{ is in Class }i\end{array}\right. \tag{4}\]  
where \(\mathbf{x}_{k}\) is a tested point and \(y_{k}\) is the final label based on the given classification rules. Based on these rules, it is seen that when the testing data \(\mathbf{x}_{k}\) belongs to an uncertain region, then \"clerical review\" is required to determine its final class. If not, then its belonging can be determined according to the general decision-making rules.
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
## 2 Materials and Methods
### Test Methods
#### 2.2.2 Durability Performance
It is still arguable for the testing age of concrete is suitable and the discrepancy to the real filed conditions because of ignoring the drying of concrete before exposing to freezing and thawing [36]. In this study, the rapid freeze-thaw experiments on thermally-enhanced concrete for all mixtures were performed after 14 days in water prior to testing in accordance with Procedure A of ASTM C666 [35] in which the specimens were periodically frozen and thawed in water at the specified cycle per day. All freeze-thaw cycling tests were performed at six cycles per day, up to 300 cycles, and the freeze-thaw resistance of specimens was evaluated by the changes in the relative dynamic modulus at every 30 cycles until its value dropped below 60% of the initial value.
###### Abstract  
The carriage of methicillin-resistant _S. aureus_ (MRSA) strains may determine the subsequent occurrence of infection, which mainly affects patients after surgeries. Therefore, its eradication with the use of antibiotics or antibiotics is a crucial method of preventing staphylococcal self-infections. The observed increase in bacterial resistance to known antibacterial substances poses a huge challenge to scientists. The aim of this study was to analyze the interaction of selected commercial antiseptics (Braunol, CITROclorex 2%, Microdacyn 60 Wound Care, Octenisept and Prontoderm Solution) with natural products (carvacrol, \(\beta\)-citronellol, geraniol, eugenol, farnesol, (-)-menthone, eucalypttol, limone, linalyl acetate and _trans_-anethole) against the MRSA standard strain. The antistaphylococcal activity of commercial antiseptics in combination with natural products against MRSA was determined using the checkerboard method. The obtained results showed that most combinations decreased the MIC level of the commercial antiseptic and natural compounds. A statistically significant decrease in the MIC value of the antiseptic and natural constituent was observed for the Braunol-\(\beta\)-citronellol combination. Such a significant decrease in the MIC value of the natural compound against the analyzed strain was also detected for the Octenisept-\(\beta\)-citronellol and Prontoderm Solution-\(\beta\)-citronellol combinations. The interaction analysis showed that out of all 20 combinations of individual antiseptics with substances of natural origin, two combinations showed a synergistic effect (Braunol-\(\beta\)-citronellol, Braunol-carvacrol), and three combinations showed an additive effect (Braunol-eugenol, Braunol-geraniol, Prontoderm Solution-\(\beta\)-citronellol). The use of Braunol in combination with \(\beta\)-citronellol or carvacrol may become an alternative method of eradicating MRSA strains. However, further studies are needed to determine the toxicity of the analyzed combinations.  
MRSA; natural products; essential oil compounds; antiseptics; checkerboard method; synergistic activity +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
We propose a simple design of an IPD-based BPF for L-band applications. A circular spiral inductor with an embedded capacitor is used to reduce the chip size to 1.25 mm\({}^{2}\). High performance in terms of return loss and insertion loss with values of 25.6 dB and 0.26 dB, respectively, were measured throughout the passband. The theoretically predicted values are in good agreement with the actual measured values. By fabrication on a GaAs substrate, it is possible to perform a relatively high Q-factor and a compact spiral circular pattern. Additionally, fabrication costs and time can be saved with this simple IPD processing technology, by using three layers of metal. IPDs can effectively combine plenty of passive components together to save more space and achieve more features than discrete passive devices.  
IPD technology whose substrate possesses good insulation properties and high electron mobility in a high frequency range will have broad application prospects in medical, lighting, and wireless communication markets. In our future work, the chip will be packaged to prevent damage and overheating by adopting a SOT-6 packaging process, which also provides waterproofing and easy handling. Considering these advantages along with its high selectivity, the proposed BPF seems promising for applications in wireless communication systems.  
Conceptualization, C.-H.Q.; Methodology, C.-H.Q.; Software, Z.-J.W.; Validation, J.-C.L.; Formal analysis, C.-H.Q.; Investigation, C.-H.Q.; Resources, E.-S.K.; Data curation, Z.-J.W.; Writing--original draft, C.-H.Q.; Writing--review and editing, J.-C.L. and N.-Y.K.; Visualization, Z.-J.W.; Supervision, N.-Y.K.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No.2016R1D1A1B03935640).  
The authors declare no conflict of interest.
###### Abstract  
As an essential application of quantum mechanics in classical cryptography, quantum secret sharing has become an indispensable component of quantum internet. Recently, a differential phase shift quantum secret sharing protocol using a twin field has been proposed to break the linear rate-distance boundary. However, this original protocol has a poor performance over channels with asymmetric transmittances. To make it more practical, we present a differential phase shift quantum secret sharing protocol with asymmetric source intensities and give the security proof of our protocol against individual attacks. Taking finite-key effects into account, our asymmetric protocol can theoretically obtain the key rate two orders of magnitude higher than that of the original protocol when the difference in length between [PERSON]'s channel and [PERSON]'s is fixed at 14 km. Moreover, our protocol can provide a high key rate even when the difference is quite large and has great robustness against finite-key effects. Therefore, our work is meaningful for the real-life applications of quantum secret sharing.  
quantum cryptography; quantum secret sharing; differential phase shift Article
## 3 Results
### Study of Contrast Improvement after Scatter Correction  
Table 11 in the Appendix A shows the contrast between the region affected by COVID-19 and the healthy-lung area in the soft-tissue DES images. In 24 out of the 31 cases, the scatter-corrected ground-truth DES has higher contrast than the uncorrected DES, meaning that the scatter correction will help to better identify the lesion. In these cases, the average contrast improvement in the area of the lesion is 9.5%, with a maximum value of 20.7% (see Figure 9a).  
It can be observed in Figure 9a that the three scatter correction methods proposed in this work are able to provide a very accurate percentage contrast improvement factor if results are compared with the ground truth (taken from the MC simulation). The single-energy model yields an average contrast improvement of 7.7%, with a maximum of 17.4%, while the 1-output dual-energy model gives an average PCIF of 8.6% and a maximum of 18.7%, and the 2-output dual-energy NN the average PCIF is 9.4% and its maximum value is 20.5%. These numbers again indicate that the 2-output dual-energy model has the best performance, although the difference with the other two algorithms is small, and the three models are acceptable. Figure 9b represents the relative difference between the ground-truth contrast value and the contrast value estimated by the three NN models, and it reinforces what was explained for Figure 9a: the relative difference is smaller for the 2-output dual-energy model, being in the range of 0-4.4%, with a mean value of 1.0%. For its part, the relative difference for the single-energy results varies from 0.2% to 4.7%, and the average relative difference is 1.8%; while for the 1-output dual-energy model, the relative difference is between 0.2% and 4.1%, being on average 1.3%. All these results point out that the contrast improvement factor associated with the scatter correction is estimated with very high precision by the three deep-learning-based models.
# Development of a Mobile Buoy with Controllable Wings: Design, Dynamics Analysis and Experiments
Based on large eddy simulation and the Ffowcs-Williams and Hawkings equations, [PERSON] et al. (2020) performed hydrodynamic analysis for a Slocum glider and a blended-wing-body glider, and concluded that blended-wing-body gliders have lower energy cost and low noise performance. [PERSON] and [PERSON] (2020) used computational fluid dynamics (CFD) methods to acquire the hydrodynamic characteristics of a torpedo-shaped underwater glider, demonstrating the practicality of CFD methods in glider design. To reflect the underwater glider motion more realistically, [PERSON] et al. (2020) proposed a fluid-multibody coupled analysis method considering the interactions between multibody dynamics and fluid dynamics, enabling a complex dynamic analysis of the underwater glider. Inspired by the characteristics of Argo profiling buoys and underwater gliders, a novel mobile buoy is proposed in this study. Figure 1 shows the working scheme of the proposed mobile buoy, which can navigate with Argo or glider operating modes by rotating the wings at a certain angle. In Argo mode, long-term observations can be carried out in a low-power way by drifting with the ocean currents, and oceanographic data such as temperature and salinity can be obtained. Considering the influence of ocean eddies and ocean fronts on the observation, the wing can be controlled to rotate at a certain angle to switch to Glider mode, which could correct the drift introduced by environmental disturbance through horizontal displacement. The features and contributions of the proposed mobile buoy can be summarized as follows: (1) A novel mobile buoy design that breaks the limitation of using propellers to improve buoy maneuverability, making low-energy operation possible. Moreover, it can realize the mode switch through controlling wing rotation instead of the attitude control system, simplifying the internal structure and facilitating control and operation. (2) A fluid-multibody coupling model considering the interplay of the mobile buoy and the fluid, which analyzes the dynamics characteristics of the mobile buoy in two motion modes. This paper is organized as follows. Section 2 introduces the working principle and the design of the mobile buoy. Section 3 develops a fluid-multibody coupling method considering the multibody dynamics and hydrodynamics to investigate the dynamic response of the mobile buoy. In Section 4, the dynamic characteristics of the proposed mobile buoy in Argo mode and Glider mode are discussed, mainly concerning the vertical profile movements and sawtooth gliding motions. Section 5 describes the tank experiment results for verifying the feasibility of the proposed mobile buoy concept and fluid-multibody coupled analysis method. Finally, some conclusions and future work are summarized in Section 6.
## 3 Methods
### C3k2_SC-Conv
#### 3.2.1 Self-Calibrated Convolutions
This is a significant advantage over many alternative approaches that attempt to achieve similar benefits--such as an expanded receptive field or sophisticated feature dependency modeling--by incorporating separate, learnable modules (e.g., attention mechanisms) or a greater number of filters, which inevitably escalate the computational cost. SC-Conv, instead, leverages its intrinsic self-calibration process to efficiently re-weight and refine features, primarily by reconfiguring the use of existing filter information rather than adding new parameterized layers. Furthermore, its straightforward, plug-and-play nature facilitates easy integration into existing neural network architectures, making it a practical solution for improving model performance without a heavy computational overhead.
## 2 Overall Design of the Model
### MobileNetV3 Model  
Pathology images are often characterized by significant noise and complex information, which poses a considerable challenge for classification algorithms. MobileNetV3, a lightweight deep learning model [16], is widely adopted in mobile devices and edge computing due to its compact size and high computational efficiency. It utilizes the NetAdapt algorithm [17] to determine the optimal number of convolutional kernels and channels, thereby enhancing model efficiency. MobileNetV3 retains the depthwise separable convolution from its V1 version [18], which effectively reduces the number of parameters and computations, making it well suited for mobile applications. Additionally, it incorporates the linear bottleneck residual structure from the V2 version [19], allowing the model to learn deeper network structures while maintaining low computational costs. Including the squeeze-and-excitation (SE) channel attention mechanism [20] further enhances the network's ability to concentrate on significant feature channels, thus improving its representation capability. Due to its excellent performance and lightweight nature, MobileNetV3 is widely utilized in both academia and industry.  
MobileNetV3 is available in two versions: large and small. For the task of multi-categorization of breast cancer pathology images, the critical requirement is to capture subtle feature differences between images. Although MobileNetV3-Large has a slightly higher number of parameters and computational demands compared to MobileNetV3-Small, it can learn higher-level abstract features and complex patterns, aiding in differentiating breast cancer subtypes. Hence, MobileNetV3-Large was selected as the backbone network for this study.  
Table 1 outlines the parameters of the MobileNetV3-Large model. In the initial stage, the H-swish activation function is introduced, which reduces computation and improves  
Figure 1: Overall network design.  
operation speed compared to the swish activation function. In the intermediate stage, the SE attention block is incorporated to enhance classification accuracy. This process involves extracting the mean value of the input features using the average pooling layer, applying the ReLU and H-swish activation functions to compute the feature weights, and multiplying these weights with the original matrix to obtain weighted output features. This approach strengthens the model's learning capability by combining the feature channel. In the final stage, the average pooling layer is advanced, and the convolutional layer adjusts the number of feature channels to expand into a higher-dimensional space. This structural design allows the MobileNetV3-Large model to enhance computational speed and performance without sacrificing accuracy.