esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the layer thickness, defect density, and interface defect density for an all-inorganic PSC device were optimized using CsPbI\({}_{3}\)Br\({}_{3-x}\), ZnO, and NiO\({}_{x}\) as the inorganic absorber layer, ETL, and HTL, respectively. The optimal thicknesses were determined to be 300 nm for ZnO, 1000 nm for the absorption layer, and 60 nm for NiO\({}_{x}\). Based on this optimized structure, a V\({}_{\rm oc}\) of 1.05 V, J\({}_{\rm sc}\) of 25.56 mA/cm\({}^{2}\), FF of 75.75%, and PCE of 20.26% were achieved. In addition, the effects of the absorber defect density and interface defect density between ETL/PAL and PAL/NiO\({}_{x}\) were analyzed. Based on this, maintaining the absorber layer defect density at approximately \(10^{15}\) cm\({}^{-3}\) and the interface defect density at \(10^{11}\) cm\({}^{-3}\) or less is recommended. Through optimization, we achieved a V\({}_{\rm oc}\) of 1.09 V, J\({}_{\rm sc}\) of 25.58 mA/cm\({}^{2}\), FF of 82.89%, and PCE of 23.07%. Thus, this study on the optimization of each factor of a solar cell is expected to improve the performance of inorganic PSC devices.  
Conceptualization, H.S. and B.-S.J.; software, H.S.; methodology, H.S. and B.-S.J.; formal analysis, H.S. and B.-S.J.; writing--original draft preparation, H.S.; writing--review and editing, B.-S.J.; supervision, B.-S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Industrial Technology Innovation Program funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea) (No. 20015778).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Mechanical Properties of Mortar  
#### 3.2.1 Compressive Strength  
The effects of MGWP on the compressive strength of mortar are presented in Figure 6. The compressive strength tests were conducted at curing intervals of 3, 7, 28, 56, and 91 days, utilizing an average of three mortar cubes. Results revealed a significant improvement in compressive strength as the percentage of MGWP increased, peaking at a 10% replacement level. Specifically, the compressive strength at 28 days increased by 25.6% and 17.26% at 10% MGWP replacement compared to the control mix. A slight reduction in strength was  
Figure 4: Setting time of binder with various replacement levels of MGWP.  
Figure 5: Slump flow of mortar with various replacement levels of MGWP.  
observed at 15% replacement, although the values still surpassed the control mix, showing an increase of 2.3% and 2.2% at 28 and 56 days, respectively.  
The increment of compressive strength with MGWP can be attributed to enhanced C-S-H gel formation by the pozzolanic activity of MGWP. The finer glass powder particles fill the capillary pores, reducing porosity and densifying the mortar matrix, which contributes to the strength improvement [46; 48; 49]. As curing age advanced to 91 days, the hydration reactions progressed further, leading to continued strength gains. However, at 30% MGWP replacement, compressive strength declined significantly due to the reduction in the cementitious material since MGWP replaced a larger proportion of cement [50; 51].  
Furthermore, the BET surface area analysis demonstrated that MGWP has a higher fineness compared to OPC, enhancing its reactivity. The pozzolanic reaction of the amorphous silica in GWP with calcium hydroxide results in secondary C-S-H, thereby improving the microstructure and long-term strength of the mortar. This reaction also minimizes the calcium hydroxide content, a known weak phase in concrete, further strengthening the composite [52; 53].
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 4 Results and Discussion
### Pressure Graph Discussion  
The pressure in the flow field of the airfoil directly determines the cavitation characteristics of the airfoil. In this section, the coordinates and pressure points of one hundred points on the upper surface are taken and compared. Figure 5 shows the pressure curves for upper surface of the original airfoil and the airfoil with different protuberances. The low-pressure area of the original airfoil starts from 0.03C (at the head of the airfoil) to 0.6C (in the middle of the airfoil). This part of the low-pressure area is the cause of cavitation generation and development. Additionally, it is consistent with the cavitation nephogram. The total length of the low-pressure area of the airfoil with a maximum variable of 3 mm is about 0.4C, which is 30% smaller than that of the control group. Additionally, the pressure curve produces some fluctuations at 0.05C, which are caused by the sheet cavitation in front of the bulge. After the degree of convexity of the covering from 1 mm to 3 mm, it obstructs the flow and forms a small high-pressure zone at 0.1C, and the total length of the low-pressure area is about 0.25C, which is about 61% less than the control group. For the airfoil with maximum variable of 6 mm, the fluctuations still exists at 0.05C, but due to the increase in the bulge, the pressure does not reach the critical pressure of cavitation, so the sheet cavitation before the bulge no longer occurs. It can be observed from the figure that the low pressure area is distributed in 0.1-0.2C, the cavitation range is reduced by more  
Figure 3: Adaptive change nephogram of airfoil structure in cavitation area.  
than 80%, and it is completely within the covering range, so the cavitation is far away from the wall and the cavitation collapse will not cause erosion damage to the near wall of the airfoil.  
Figure 4: Effect of local deformation on airfoil cavitation suppression. (**a**) Cavitation nephogram of airfoil with maximum variable of 1 mm. (**b**) Cavitation nephogram of airfoil with maximum variable of 3 mm. (**c**) Cavitation nephogram of airfoil with maximum variable of 6 mm. (**d**) Cavitation nephogram of original airfoil.
## 1 Introduction
Tests using field-collected single-frequency measurement data of GPS and BDS generated by our self-developed receivers from a zero baseline and two short baseline experiments are conducted to validate the proposed model. Results show that high-precision baseline solutions with the same precision level as a sole GPS or BDS RTK method can be obtained by processing the dual-constellation measurements with the proposed model, the percentage of integer ambiguities and fixed baseline in all epochs using double-constellation is higher than using either single constellation, and a shorter convergence time to first fix can be obtained as well. This proposed model with implementation details can help the reader to realize his/her own application and also can be extended to multi-constellation and multi-frequency scenarios and can be a guidance for future applications which is the main contribution of this research work. In the following parts of this paper, we first introduce the GNSS carrier phase and pseudorange measurement model as the foundation of the analysis and derivations in the remaining sections. Next, a Kalman filter-based float ambiguity estimation algorithm is proposed and improved to process the combined GPS and BDS measurements. The architecture and work process of the model are developed and presented in detail. In the experiment part, a zero baseline and two short baseline field tests to validate the proposed model are conducted from which both carrier phase and pseudorange raw measurements of GPS L1 and BDS B1 frequencies are collected. These data are processed by the proposed model to produce baseline and ambiguity solutions. The results from both single GPS and BDS and their combination are compared and analyzed. Finally, a conclusion is drawn and suggestions for future work are presented.
Moreover, the ease of meeting received the highest evaluation in a condition of ambient noise of 50 dB for discussions requiring creative ideas. The results of a multiple regression analysis suggested that a quiet sound environment was not necessarily desirable in discussions requiring creative ideas [17]. These findings suggest that an extremely quiet sound environment reduces the perceived ease of meeting in discussions requiring creative ideas by multiple people. These previous studies have argued primarily that a sound environment perceived as being too quiet gives workers the impression that it is difficult to have a conversation. However, none of these studies have considered the influence of the sound source or the acoustical characteristics of spaces on knowledge creative activities. To promote creative thinking and enhance the workplace environment as a conducive space for knowledge creative activities by multiple people, smooth and active communication is required in meeting rooms and common areas. In recent years, as a result of changes in the physical workspace, office workers are frequently engaged in knowledge creative activities in open areas with high ceilings. Knowledge creative activities are also carried out in general construction companies; opportunities for discussions in common areas of the office are increasing. There are some examples of designing office spaces with a sense of openness in general construction companies. Consequently, the further accumulation of research outcomes is needed to construct a sound environment that can enhance knowledge creative activities. Therefore, the present study aimed to examine the relationship between intellectual productivity and the sound environment in situations in which multiple people engage in knowledge creative activities. In knowledge creative activities by multiple people, it is important for the participants of the meeting to communicate actively and smoothly with each other. In this study, \"good conversation\" was used as a comprehensive item to evaluate the degree of active and smooth communication. To investigate the influences of sound pressure level (i.e., the signal-to-noise ratio [SNR]), the kind of sound, and the reverberation time in meeting rooms on the impression of a \"good conversation\", and to clarify the psychological factors that contribute to this impression, we conducted psychoacoustic experiments and considered a causal model for the psychological evaluation of a \"good conversation\" by performing a multiple regression analysis of data from psychological evaluations.
## 7 Conclusions  
The key influencing factors on the load-carrying performance of a multiple-layer interference cylinder were studied by finite element simulation of a turbine shrink disk. The influences of assembly clearance, machining derivation, and propel stroke were obtained by analyzing the contact pressure of the mating surfaces and the load torque. The conclusions drawn are as follows.  
1. The theoretical model and finite element model of the turbine shrink disk were constructed, and a test device has been developed to verify the reasonability of the proposed models for load-carrying performance evaluation.
2. Different assembly clearances have different effects on the load-carrying performance of a turbine shrink disk. The maximum assembly clearance has a large effect on the contact pressure of the mating surfaces and the load torque, whereas the effect of the minimum assembly clearance is relatively small. The effect of the assembly clearance must be taken into consideration during the design and calculation, and the assembly clearance should be reasonably determined.
3. Machining deviation has a relatively small effect on the contact pressure and load torque of the mating surfaces. If the machining deviation is within reasonable range, then the load-carrying performance of a multiple-layer interference fit can be ensured.
4. The propel stroke has a significant effect on the contact pressure of the mating surface and the load torque. To avoid the abnormal operation of equipment caused by insufficient load torque, the propel stroke during practical assembly should be at least 0.95 times that of the designed propel stroke.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Position-Based Solutions
#### 4.4.1 Kalman Positioning RPL (KP-RPL)
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Category** & **Working Principle** & **Issues** & **Adressed** & **Advantages** & **Disadvantages** \\ \hline ME-RPL [42] & Parent node from DODAG is selected when it is within the RSSI range. & Lack of identification of mobile nodes. & Improved PDR to 92\% and energy consumption to 1.2 MJ/min. & Details over nodes variations to obtain the desired results are missing. \\ \hline GTM-RPL [38] & Uses mobility and density metric parameters. & Lack of identification of mobile nodes. & GTM-RPL achieves better QoS compared to mRPL. & Only few protocol comparison is done. \\ \hline MoMoRo & Detects and notifies route disconnectivity for the reconstruction process. & Increase in hand-off delay. & PRR for downward traffic to and from mobile nodes by MoMoRo is 90\%. & An adequate information on achieving low PRR is not discussed given. \\ \hline Enhanced Hand-off Macha-Mechanism [65] & Average RSSI level of each neighboring node is used for selecting the best route. & Increase in hand-off delay, Loop in the network. & Achieves better performance when traffic burst interval is increased. & Solution to overhead by control messages during DODAG formation is not discussed. \\ \hline mRPL [46] & Uses RSSI; neighboring nodes within the child set are ignored to avoid loops. & Increase in hand-off delay, Loop in the network. & 100\% PDR is provided by mRPL because of the fast hand-off process. & Solution to the overhead caused due to high traffic is not discussed. \\ \hline EMA-RPL [66] & RSSI of a mobile node is used for connectivity of a mobile node in a network. & Issue with the rank of detached mobile nodes. & EMA-RPL reduces signaling cost, energy, and handover delay compared to RPL. & None of the scenarios are discussed in which mobile node can be chosen as preferred parent. \\ \hline DMR [41] & Uses rank information and LQI. & Loops in-network, issue with the rank of rank of detached mobile nodes. & DMR reduces average per node energy consumption by 25\%. & To reconstruct a DODAG using DMR broadcasts is not thoroughly discussed. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of RSSI-based Solutions.
## 3 Effects of Joints on the Tensile Properties  
### Effect of the Dip Angle of A Single Joint  
In this study, all joints are assumed to cut through the specimen. Seven models with different dip angles (\(\alpha\) = 0, 10, 20, 30, 40, and 50\({}^{\circ}\)) were built, as illustrated in Figure 1. The stress-strain curves and the tensile properties with respect to dip angles are shown in Figure 2 and Table 3, respectively.  
As can be seen, the general shapes of the stress-strain curves are similar. The stresses of specimens linearly increase after the tension loading starts, and undergo a sudden drop after failure, due to the brittle nature of rocks. The dip angle of the joint clearly has a notable effect on the tensile properties  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Lithology**} & **Density** & **Bulk** & **Shear** & **Cohesion \(C\)** & **Internal** & **Tensile** \\  & **(kg/m\({}^{3}\))** & **modulus \(K\)** & **modulus \(G\)** & **(MPa)** & **friction** & **strength** \\  & **(GPa)** & **(GPa)** & **(GPa)** & **angle \(\varphi\) (\({}^{\circ}\))** & **(MPa)** \\ \hline Sandstone & 2630 & 26.49 & 19.05 & 3.75 & 25.9 & 2.25 \\ \hline \end{tabular}
\end{table}
Table 1: Rock mass properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Lithology**} & **Cohesion \(C\)** & **JKN (GPa/M)** & **JKS (GPa/M)** & **Joint friction** & **Tensile strength** \\  & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline Sandstone & 3.67 & 26.49 & 19.05 & 25.9 & 1.88 \\ \hline \end{tabular}
\end{table}
Table 2: Joint properties.  
of the rock specimens. The peaks (\(\sigma_{t}\)) of the pre-peak curves vary monotonically increases with the increase of \(\alpha\).  
When \(\alpha=0^{\circ}\), i.e., a horizontal joint, the \(\sigma_{t}\) and \(E_{t}\) of the specimen are 0.78 MPa and 0.80 GPa, respectively. When \(\alpha\) reaches 50\({}^{\circ}\), the \(\sigma_{t}\) and \(E_{t}\) increase to 1.05 MPa and 0.81 GPa, which are approximately 1.35 and 1.01 times the values in the case of \(\alpha=0^{\circ}\), respectively. In comparison, the changes in \(\alpha\) have a greater influence on \(\sigma_{t}\) than \(E_{t}\). In reality, joint surfaces are usually rough, and the asperities provide resistance to shear stress, and the joint roughness is positively correlated to the shear strength [35]. The joint roughness is described with normal stiffness, shear stiffness, cohesion, etc. in 3 DEC. When \(\alpha\) is not \(0^{\circ}\), the shear strength of the joint will provide extra resistance to the tensile load on rock failure and deformability, which leads to the high \(\sigma_{t}\) and \(E_{t}\) when is \(\alpha\) higher. However, when \(\alpha=0^{\circ}\), the joint is perpendicular to the tensile load, so no extra
## 3 Methodology
### Principle Component Analysis for HRTF Characteristics Parameters  
Principle component analysis is a statistical technique for analyzing and simplifying data set. It is often used to reduce the dimensionality of a data set while retaining the features that contribute the most to the variance of the data set. In previous research [20; 21; 22], PCA had been proved of effective in reconstruction of HRTF logarithmic magnitude. In current research, we applied PCA on HRTF logarithmic magnitude to extract the principle components as the characteristic parameters.  
Figure 4: Schematic diagram of the integrated head-related transfer functions (HRTF) individualization method. MLR: Multiple Linear Regression; PCA: Principle Component Analysis.  
Figure 3: ITD contrast of different subjects in a same direction. X-axis denotes the azimuth of the sound source, Y-axis denotes the ITD value, and curves in different color denote the ITD variation trends of different subjects.  
The logarithmic magnitudes of HRTF at \(M\) directions on the horizontal plane can be denoted as:  
\[[\mathbf{H}_{\text{log}}]_{N\times M}=[\ H_{\text{log}1}(f)\quad H_{\text{log}2}( f)\quad\cdots\quad H_{\text{log}M}(f)]\quad=\left[\begin{array}{cccc}H_{\text{ log}}(\theta_{1},f_{1})&H_{\text{log}}(\theta_{2},f_{1})&\cdots&H_{\text{log}}( \theta_{M},f_{1})\\ H_{\text{log}}(\theta_{1},f_{2})&H_{\text{log}}(\theta_{2},f_{2})&\cdots&H_{ \text{log}}(\theta_{M},f_{2})\\ \vdots&\vdots&\ddots&\vdots\\ H_{\text{log}}(\theta_{1},f_{N})&H_{\text{log}}(\theta_{2},f_{N})&\cdots&H_{ \text{log}}(\theta_{M},f_{N})\end{array}\right] \tag{1}\]  
where \(f=1\), \(2\), \(\ldots\), \(N\) denotes the frequency bin of HRTF. Then, the covariance matrix could be calculated as:  
\[[\mathbf{R}]_{N\times N}=\frac{1}{M}[\mathbf{H}_{\text{log}}]_{N\times M}[ \mathbf{H}_{\text{log}}]_{M\times N}^{+} \tag{2}\]  
where \([\mathbf{R}]\) is an N by N Hermite matrix, and its eigenvalue is real. \(+\) denotes the generalized inverse of matrix \([\mathbf{H}_{\text{log}}]\). Its eigenvector is extracted and arranged as the eigenvalue reduced-order \(\mathbf{u}_{1},\mathbf{u}_{2},\ldots,\mathbf{u}_{Q}\). Then the first Q eigenvectors are taken out as the base-vectors and to build a matrix:  
\[[\mathbf{D}]_{N\times Q}=[\mathbf{u}_{1},\mathbf{u}_{2},\ldots,\mathbf{u}_{Q}] \tag{3}\]  
Because \(\mathbf{u}_{1},\mathbf{u}_{2},\ldots,\mathbf{u}_{Q}\) are orthogonal, \([\mathbf{H}_{\text{log}}]_{N\times M}\) can be decomposed on the use of these base-vectors, then the weight matrix can be achieved accordingly:  
\[[\mathbf{W}]_{Q\times M}=[\mathbf{D}]_{Q\times N}^{+}[\mathbf{H}_{\text{log}} ]_{N\times M} \tag{4}\]  
herein, \([\mathbf{W}]_{Q\times M}\) is the characteristic parameter of HRTFs. Figure 5 gives the result of PCA on a left ear's HRTF logarithmic magnitudes. As can be seen, more than 95% accuracy of the HRTF logarithmic magnitudes can be reconstructed from six principle components.
## 4 Performance Analysis of QoS Metrics
### RSSI Analysis for Indoor and Outdoor Multi-Hop Communication
In an indoor environment, the RSSI improves by 14.73% at 40 m, which is 10.90% at 5 m of transmission distance while we switch to two-hop communication from a single hop. Similarly, in an outdoor scenario, two-hop communication improves the RSSI by 7.89% at a transmission distance of 40 m, which is 6.38% at 5 m. As the multi-hop communication improves the link quality significantly for both indoor and outdoor environments it facilitates increased transmission distance from the coordinator to the end node, as well as improves reliability. Thus, in such a scenario where LoS is minimum, and interference is high, multi-hop communication is preferred. The multi-hop mesh network also covers a much larger area in comparison with single-hop communication, thus it contributes to improving the scalability of the network. Figure 9: The link quality of transmission in terms of RSSI with different transmission power levels, distance, and deployment environment: (**a**) indoors and (**b**) outdoors.
## 3 Results and Discussion
### Analysis of 304 Stainless Steel Vessel  
With 304 stainless steel at a pressure of 35 MPa and a thickness of 25 mm, the vessel exhibits a moderate deformation of 0.8371 mm. However, the equivalent stress is significantly high at 412.88 MPa, surpassing the material's yield strength of 207 MPa. Consequently, the safety factor is relatively low at 0.5, suggesting a potential risk of failure at a pressure of 35 MPa. As the pressure increases to 50, 60, and 70 MPa, the deformation of the vessel increases to 1.1959, 1.435, and 1.6742 mm. Furthermore, as the pressure increases from 35 MPa to 70 MPa, the equivalent stress on the vessel increases significantly, from 412.88 MPa to 825.76 MPa. The safety factor decreases with increasing pressure, dropping to 0.35, 0.29, and 0.25 at 50, 60, and 70 MPa. When the thickness of the vessel is increased from 25 mm to 50 mm, there is an improvement in the deformation of the vessel, equivalent stress, and safety factor, but the safety factor remains below 1 for the 50 mm thickness. This indicates that 304 stainless steel cannot withstand pressures from 35 to 70 MPa with thicknesses of 25 mm and 50 mm. However, increasing thickness to 100 mm allows the vessel to perform well at pressures of 35 MPa and 50 MPa. At 35 MPa, the deformation is 0.2206 mm, and  
Figure 2: Mesh generation and boundary conditions for the compressed hydrogen vessel. (**a**) Mesh generation on 2D axisymmetric. (**b**) Boundary conditions applied on 2D axisymmetric.  
the stress is 138.93 MPa, which is below the yield strength of stainless steel of 207 MPa. Similarly, at 50 MPa, the equivalent stress is 198.47 MPa, and the safety factor exceeds 1, indicating that the vessel can withstand these pressures. However, the 100 mm thickness is insufficient to handle pressures of 60 MPa and 70 MPa, as the safety factor falls below 1. Increasing the thickness to 150 mm enables the stainless steel to withstand pressure of 60 MPa and 70 MPa, with safety factors of 1.1243 and 1, respectively. The deformation of a 304 stainless steel vessel can be seen in Figure 3. Figure 4 shows the relation of pressure and safety factors. The deformation for the stainless steel at different pressures and thicknesses is presented in Table 1, and the equivalent stress is given in Table 2.