esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 5 Engineering Applications  
Relying on Chengdu Tianfu International Airport, a slipform paving test was conducted using a GOMACO GP-2400 slipform paver with a concrete pavement length, width, and height of 25 m, 5 m, and 0.42 m, respectively. The raw materials and the concrete mix ratio used in the test section were the same as those used in the indoor test (Table 1).  
Before paving, the vibrating rod, the horizontal angle \(\theta\), and the vibrating frequency of the rod were set to 15 and 8000 r/min, respectively. The indoor test measured that the vibrating frequency of 8000 r/min for 33.7 s rose 42 cm under the mold concrete sphere. The vibrating frequency of the outermost vibrating rod on both sides is adjusted to 1000 r/min in a field test section of slipform paving construction to ensure that both sides of the Yin and Yang Enterprise mouths were densely formed (Figure 14).  
This study observed a high vibrating performance of slipform concrete and a fast speed of slurry, which met the requirements of site construction (Figure 14). In addition, there was a high overall forming effect of the road surface and no large collapsed edges in the concrete, slippery shoulders, or fallen corners. Therefore, the Yin and Yang Enterprise mouths were more completely retained and vibrated compactly.  
Figure 14: _Cont._This study took cores from the road surface after 28 days of curing (Figure 15) to examine the macroscopic pores on the surface. It was observed that the large pores were discharged, the overall concrete was well compacted, and the separation rate \(SC\) was <0.15.  
The overall effect of the actual slipform paving and vibrating construction of the test section was roughly the same as the indoor experimental results. After vibrating and forming, there is no collapse of edges, shoulders, and corners, and the fresh concrete has high cohesiveness and complete and dense retention of Yin and Yang openings. In addition, the equations for the optimum vibration parameters and paving speed for slipform construction were feasible and showed significant reasonable control of the field vibration construction process of slipform paving of large-thickness concrete.
## 3 Postulated Models Describing the Mechanism of Very High Cycle Crack Initiation and FGA-Formation
### Numerous Cyclic Pressing  
The mechanism of \"numerous cyclic pressing\" (NCP) was postulated by [PERSON] et al. and assumes that the necessary conditions for the FGA-formations are inert environmental conditions, frequently recurring fracture contact, and a low crack propagation speed [14; 97]. It is stated that a crack initiates in the interface between inclusions and matrix because of highly localized plastic deformation and the formation of the fine-grained layer is attributed to crack-closure effects. The crack flanks of the initiated VHCF-crack are supposed to lead to the formation of the observed fine-grained layer due to the repeated contact during the cyclic loading. This process is continuing, until the crack length or the FGA size, which is present at the transition from FGA to fish-eye, reached the critical size of the FGA for a SIF in the range of the threshold value for long-crack growth. The crack initiation process, as well as the crack propagation with low crack propagation rates are not described in more detail. The FGA-formation is therefore not considered responsible for the slow crack initiation or the VHCF-failure but is described as a consequence or a marginal phenomenon. The NCP model further describes two necessary conditions for FGA-formation, the presence of compressive stresses between the fracture surfaces resulting in repeated fracture surface contact and a sufficient number of load cycles to generate sufficient number of fracture surface contacts [17].
## 3 Results and Discussion  
### Performance of the Perovskite Solar Cell (PSC) under the Initial Conditions  
Figure 2 shows the current density-voltage (\(I\)-\(V\)) and QE of the ITO/ZnO/CsPbIxBr3-x/NiOx/Au PSC device under the initial conditions. The V\({}_{\text{oc}}\), J\({}_{\text{sc}}\), and FF were measured to be 1.03 V, 23.86 mA/cm\({}^{2}\), and 67.27%, respectively. The FF and PCE values were calculated using Equations (5) and (6) [38]. Accordingly, the PCE was determined to be 16.61%. Based on these basic performance indicators, from Section 3.2, the effect of the layer thickness, defect density, and interface defect density on the PCE were systematically analyzed:  
\[\text{FF}=\frac{\text{V}_{\text{max}}\text{J}_{\text{max}}}{\text{V}_{\text{oc }}\text{J}_{\text{sc}}} \tag{5}\]  
\[\text{PCE}=\frac{\text{V}_{\text{oc}}\text{J}_{\text{sc}}\text{FF}}{\text{P}_ {\text{in}}} \tag{6}\]  
In these equations, we use the following:  
* FF = the fill factor;  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **ZnO**[34] & **CsPbIxBr3-x** [35] & **NiOx**[36] \\ \hline Thickness (nm) & 100 & 800 & 50 \\ \hline Band gap (eV) & 3.3 & 1.78 & 3.8 \\ \hline Electron affinity (eV) & 4.1 & 3.95 & 1.46 \\ \hline Dielectric permittivity (relative) & 9 & 6 & 10.7 \\ \hline CB effective density of states (cm\({}^{-3}\)) & 4.0 \(\times\) 10\({}^{18}\) & 1.1 \(\times\) 10\({}^{20}\) & 2.8 \(\times\) 10\({}^{19}\) \\ \hline VB effective density of states (cm\({}^{-3}\)) & 1.0 \(\times\) 10\({}^{19}\) & 8.0 \(\times\) 10\({}^{19}\) & 1.0 \(\times\) 10\({}^{19}\) \\ \hline Electron thermal velocity (cm/s) & 1.0 \(\times\) 10\({}^{2}\) & 1.0 \(\times\) 10\({}^{2}\) & 1.0 \(\times\) 10\({}^{2}\) \\ \hline Hole thermal velocity (cm/s) & 1.0 \(\times\) 10\({}^{2}\) & 1.0 \(\times\) 10\({}^{2}\) & 1.0 \(\times\) 10\({}^{2}\) \\ \hline Electron mobility (cm\({}^{2}\)/Vs) & 100 & 16 & 12 \\ \hline Hole mobility (cm\({}^{2}\)/Vs) & 25 & 16 & 2.8 \\ \hline Shallow uniform donor density N\({}_{\text{D}}\) (cm\({}^{-3}\)) & 1.0 \(\times\) 10\({}^{18}\) & - & - \\ \hline Shallow uniform acceptor density N\({}_{\text{A}}\) (cm\({}^{-3}\)) & - & 1.0 \(\times\) 10\({}^{15}\) & 1.0 \(\times\) 10\({}^{18}\) \\ \hline Defect density N\({}_{\text{t}}\) (cm\({}^{-3}\)) & 1.0 \(\times\) 10\({}^{14}\) & 1.0 \(\times\) 10\({}^{14}\) & 1.0 \(\times\) 10\({}^{14}\) \\ \hline \end{tabular}
\end{table}
Table 1: Physical parameters of ZnO, CsPbIxBr3-x, and NiOx used in the ITO/ZnO/CsPbIxBr3-x/NiOx/Au PSC.  
\(\mathrm{V_{max}}\) = the maximum voltage;
* \(\mathrm{J_{max}}\) = the maximum current density;
* \(\mathrm{V_{oc}}\) = the open-circuit voltage;
* \(\mathrm{J_{sc}}\) = the short-circuit current density;
* PCE = power conversion efficiency;
* \(\mathrm{P_{in}}\) = the incident power of the sun.
# Water Basin Effect of Cofferdam Foundation Pit
## 3 Deformation Evolution Mechanism of Cofferdam Foundation Pit
### Deformation Development Characteristics of Steel Pipe Pile
#### 3.3.4 Stage 4: Precipitation Completion  
During the precipitation completion stage, the steel pipe pile is simplified as a three-span cantilever continuous beam model (Figure 3d). Direct superposition of statically determinate system deflections would similarly distort results, so an incremental method analogous to Stage 3 is adopted.  
The fourth-stage displacement increment is approximated by the trapezoidal load \(W_{\text{C}}\)&\(W_{B}\) acting on the two-span cantilever continuous beam, \(BE\), in Figure 3f. The deflection equation incorporates segment \(BC\) bending moment \(M_{B}\) (Condition 1), trapezoidal load \(W_{\text{C}}\) & \(W_{B}\) (Conditions 2), \(\text{----}5)\), end \(C\) bending moment \(M_{\text{C}}\) (Condition 2), segment \(CD\) bending moment \(M_{D}\) (Condition 2) and ending moment \(M_{\text{C}}\) (Condition 1), segment \(DE\) bending moment \(M_{D}\) (Condition 1), and geometric compatibility conditions: equal rotation angles at end \(B\), \(\theta_{BD}=0\), support \(D\), \(\theta_{DE}=\theta_{DC}\), and support \(C\), \(\theta_{CD}=\theta_{CB}\). For the cantilever continuous beam \(BE\), the following relationships are satisfied:  
\[F_{E}h_{1}=M_{D}\] (21a) \[F_{D}h_{2}=M_{\text{C}}-M_{\text{D}}\] (21b) \[F_{\text{C}}h_{3}=M_{B}-M_{\text{C}}+\frac{1}{2}W_{\text{C}}{h_{3}}^{2}+\frac{1} {6}(W_{B}-W_{\text{C}}){h_{3}}^{2}\] (21c) \[F_{E  
\[F_{B}=\Big{[}\frac{h_{2}}{2h_{1}(h_{1}+h_{2})}-\frac{1}{h_{2}}-\frac{1}{2(h_{1}+h_{ 2})}+\frac{3}{2h_{3}}\Big{]}M_{C}-\frac{3}{8}W_{C}h_{3}-\frac{1}{10}(W_{B}-W_{C} )h_{3}+\frac{1}{2}(W_{B}+W_{C})h_{3} \tag{22g}\]  
By solving the above equations and maintaining the coordinate system with support \(B\) as the origin, the incremental deflection expression is obtained via superposition of conditions 1, 3-5, 2 and 2, 1 as well as 1.  
\[\omega(x)=\frac{M_{C}x}{6E\Pi h_{3}}\Big{(}2{h_{3}}^{2}-3h_{3}x+x^{2}\Big{)}+ \frac{M_{C}x}{6E\Pi h_{3}}\Big{(}{h_{3}}^{2}-x^{2}\Big{)}+\frac{W_{C}x}{24E \Pi}\Big{(}{h_{3}}^{3}-2h_{3}x^{2}+x^{3}\Big{)} \tag{23a}\] \[\omega(x)=\frac{M_{C}(x-h_{3})}{6E\[PERSON] h_{2}}\Big{[}2{h_{2}}^{2}-3h_{2}(x-h_{ 3})+(x-h_{3})^{2}\Big{]}+\frac{M_{D}(x-h_{3})}{6E\[PERSON] h_{2}}\Big{[}{h_{2}}^{2} -(x-h_{3})^{2}\Big{]},\ \ \ (h_{3}\leq x\leq h_{3}+h_{2})\] (23b) \[\omega(x)=\frac{M_{D}(x-h_{3}-h_{2})}{6E\[PERSON] h_{1}}\Big{[}2{h_{1}}^{2}-3h_{1}(x- h_{3}-h_{2})+(x-h_{3}-h_{2})^{2}\Big{]},\ \ \ (h_{3}+h_{2}\leq x\leq H_{w}) \tag{23c}\]  
This incremental deflection (Equation (23)) is superimposed onto the Stage 2 (Equation (17)) and Stage 3 (Equation (20)) deflections to derive the total deformation. A full-load analysis based on Figure 3d is necessary to calculate the additional bending moment and horizontal load at end \(B\).
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 2 Architecture Design
### Satellite-Based IoT (SIoT)
#### 2.1.2 Satellite and Ground Communications Complement Each Other  
In terrestrial communication systems, it is very difficult to establish ground base stations in poor geographical conditions such as oceans and deserts [7]. As a result, the coverage of terrestrial wireless networks is very limited. Satellite communications, on the other hand, may cover a wide region regardless of geographical constraints and thus can supplement the coverage of terrestrial communication. Nevertheless, the spectrum resources of satellite IoT are limited [9], so satellite IoT needs to share a spectrum with ground IoT which we call \"SIoT\". Satellites can realize communication with high quality and high reliability, which is of great significance to terrestrial communications. By supplementing a network with satellites, it is possible to meet the demand for global coverage. Satellites have become an important and powerful element in the future of communication systems.
## 5 Experimental Verification
### Idle Condition  
Table 8 shows the comparison of the vibration isolation rates of the three mounts before and after optimization in the idle condition (air conditioning off). It can be seen that the vibration isolation rates have increased significantly after optimization.  
Figure 8: Diagram of the sensor installation position: (**a**) the acceleration sensors on the active and passive ends of the mount; (**b**) the driver’s right ear sound pressure sensor.  
Although the vibration isolation rates of the three mounts in some directions are not greater than 20 dB, as desired by the industry after optimization, the state of the system can be judged in conjunction with the vibration of the passive end of the mount. Generally speaking, in the state of idle air conditioning being off, the vibration of the passive end of the mounts less than or equal to 0.05 m/s\({}^{2}\) meets the design requirements. From the vibration amplitude of the passive end of the three mounts shown in Figure 9, the vibration of the passive end of all three mounts meets the design requirements.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mount** & **Vibration Isolation Rate** & **Vibration Isolation Rate** \\
**Direction** & **before Optimization (dB)** & **after Optimization (dB)** \\ \hline SFL,X & 22.72 & 26.83 \\ SFL,Y & 15.15 & 18.24 \\ SFL,Z & 23.34 & 24.50 \\ SFR,X & 20.45 & 20.51 \\ SFR,Y & 14.84 & 17.62 \\ SFR,Z & 23.75 & 24.67 \\ SRR,X & 20.38 & 22.75 \\ SRR,Y & 24.72 & 23.42 \\ SRR,Z & 23.89 & 26.53 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of the vibration isolation rate at idle condition (air-conditioning off) before and after optimization.  
Figure 9: Optimized vibration of the passive end: (**a**) front-left mount; (**b**) front right-mount; (**c**) rear mount.  
At the same time, it is more important to judge the vibration perception of the driver from the perspective of the whole vehicle, such as the vibration of the driver's seat rail and the steering wheel. The optimized three-dimensional vibration of the seat rail and the steering wheel in the vehicle is shown in Figure 10. The RMS acceleration values in the X-, Y-, and Z-directions were also calculated and compared with the limit values [31], as shown in Table 9. The vibration of the driver's seat rail and steering wheel at idle condition with the air conditioning off are below the corresponding limits.
## 2 Materials and Methods
### Time-Frequency Representation  
We filtered the signal of each iEEG contact with a Kaiser window-based finite impulse response bandpass filter of 1-70 Hz. As Figure 1b shows, each iEEG signal was then segmented into 3 s epochs, and the Morlet wavelet transform was used to convert the time-domain signal into a time-dependent spectrum. Using the wavelet transform, the signal is convolved with various filter kernels of specific characteristics called wavelets, resulting in a spectrum with amplitude and phase. There are several families of wavelets, each representing a specific frequency band. The general equation for signal decomposition using wavelets is given by Equation (1).  
\[S_{w}(f,t)=s(t)*w_{f}(t) \tag{1}\]  
where \(w_{f}(t)\) is the wavelet, \(s(t)\) is the input signal, and \({}^{*}\) represents convolution. The Morlet wavelet is commonly used in neuroscience for EEG signal analysis [38; 39; 40]. The envelope of this wavelet is a Gaussian.  
\[W^{w}(t,f)=exp\biggl{(}-\frac{t^{2}}{2\sigma^{2}}\biggr{)}\cdot exp(i2\pi ft) \tag{2}\]  
In order to preserve the time and frequency resolution when the data are fed to the following neural network for visual complexity analysis (see next section), we fixed the time resolution to 8.9 ms and the frequency resolution to 0.30 Hz. This resulted in an image with a dimension of 224 \(\times\) 224. The spectrum was normalized to a range of [0, 1] andeliminated any effects of power differences across contacts and between subjects. This was then coded into RGB space using a jet color map (Figure 1b) as it uses all three-color channels, unlike incremental color maps, which mainly use a particular color channel. The resultant 224 \(\times\) 224 \(\times\) 3 spectrum was used for the following deep learning-based analysis of the image's visual complexity.  
Figure 2 schematizes the whole methodological workflow we propose in this study to analyze brief interictal iEEG data.  
Figure 2: Methodological flowchart. Schematic outline of the iEEG signal processing, the TF image analysis with the deep learning (VGGnet) architecture (where conv represents the convolution layer; ReLU is the rectified linear unit, and FC the fully connected layers), and the point-of-interest identification. POI stays for “Point Of Interest”, and SVM for “support vector machine”.
# RESTful API Testing Methodologies: Rationale, Challenges, and Solution Directions
## 4 Results
### RQ3--What Are the Existing Solutions Available to Meet Testing and Unit Test Generation Challenges? Different categorical solutions have been proposed and used in the selected primary studies. Those fall in the following categories:  
* Tools;
* Technique/method;
* Framework/model. Figure 4 shows the contribution classification of the primary studies. According to Figure 4, most of the papers are proposing a methodology/approach and the least number of contributions is related to the framework/model. This indicates that researchers mostly prefer proposing a methodology/approach instead of a model/framework in this field. However, the development of frameworks/models is as important as approaches/methodologies, and more research is needed to develop novel models/frameworks. Table 3 describes where each primary study falls in terms of the proposed solution. Figure 4: Contribution classification. RQ4-What Support Do Solutions Provide for Authentication-Enabled RESTful APIs' Testing and Unit Test Generation? Developing authentication for RESTful APIs has several methodologies to follow. Those RESTful APIs are known as protected ones, which cannot be accessed by an anonymous user. The login process must be completed successfully first in order to get the identification, which can be then used to access the protected part of the RESTful APIs' endpoint collection. The variance is quite high when it comes to actual implementation. Based on the analysis of primary studies, it can be inferred that it is extremely difficult to come up with a solution that takes care of that aspect. Several studies excluded unit test generation for protected (i.e., authentication enabled) RESTful APIs' endpoints. Some of the common authentication implementation methods for RESTful APIs are described as follows:  
* Token-based authentication: After successful authentication (i.e., log in using user-name and password), a long identification string is assigned to the user session, which is called a token. This is usually encrypted and sent back and forth manually between request-response sessions. The creation and validating mechanisms of tokens totally depend on the developer or organization; therefore, it can be done in many different ways and, as such, there is no strict standard. This poses a huge problem when trying to incorporate protected RESTful APIs into test generation. Every token usually has a validity time limit, after which it expires and a new token is required. * Cookie-based authentication: This mechanism depends on generating a piece of authentication information called a cookie, which is sent back and forth between
## 2 Delivery ratio  
This parameter represents the probability of the node receiving Prelearn messages from its neighbors. The delivery ratio \(D_{node}\) can be calculated as \(D_{node}=D_{rec}/D_{sen}\), where \(D_{sen}\) is the number of Prelearn messages sent by message carriers and \(D_{rec}\) is the number of Prelearn messages received by the node.
## 4 Experimental Evaluation of the VLC System and Experimental Results
### Experimental Setup No. 3--Long Communication Distance in Low SNR Conditions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modulation** & **Coding** & \begin{tabular}{c} **Data Rate** \\ **(k.b/s)** \\ \end{tabular} & \begin{tabular}{c} **VLC Distance** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **BER * \\ \end{tabular} & **Conditions** \\ \hline DSSS & SIK & 3 & 40 & \(<\)10\({}^{-6}\) & \\ OOK & Manchester & 11 & 40 & \(<\)10\({}^{-6}\) & \\ OOK & Miller & 11 & 40 & \(<\)10\({}^{-6}\) & with HB perturbing \\ OOK & Manchester & 24 & 40 & \(<\)10\({}^{-6}\) & LEDs \\ OOK & Miller & 24 & 40 & \(<\)10\({}^{-6}\) & \\ OOK & Manchester & 50 & 40 & \(5.71\times 10^{-4}\) & \\ OOK & Miller & 50 & 40 & \(2.23\times 10^{-3}\) &
\begin{tabular}{c} with HB perturbing \\ LEDs \\ \end{tabular} \\ OOK & Manchester & 50 & 40 & \(<\)10\({}^{-6}\) & without HB perturbing \\ OOK & Miller & 50 & 40 & \(1.7/10^{-6}\) & LEDs \\ \hline \hline \end{tabular}  
* The bit error ration (BER) \(<\) 10\({}^{-6}\) has been achieved based on minimum 10 data sets, each test of one million bits.  
\end{table} 
Table 4: Summary of the experimental results for setup NO. 3. Figure 7: Oscilloscope screens illustrating the signal reconstruction process at the level of the VLC receiver for a 40 m communication distance: (**a**) in normal conditions (**b**) with white LED perturbing optical noise source. One can see the effect of the perturbing noise source on the data signal and the response of the logarithmic transimpedance circuit.
## 4 Conclusions  
This study investigated the use of certain agricultural, and industrial wastes byproduct to prepare environmentally friendly geopolymers for evaporative cooling system applications. The results showed that the concentration of alkali activator influenced the compressive strength of geopolymeric specimens. The ratio of sodium silicate solution-to-sodium hydroxide solution ratio in terms of mass was directly proportional to the compressive strength of the geopolymer paste. This correlation was also evident for the compressive strength, water absorption, and apparent porosity of the geopolymer paste specimens; with specimens exhibiting lower water absorption and porosity resulting in higher residual compressive strength. The percentage of water absorption and pore volume of the specimens increased via the usage of RH, MK, and RHA. In this study, a green geopolymer employing a sustainable approach was prepared with excellent comprehensive strength, water absorption, and capillary rise using different industrial and agriculture waste and byproducts. To the best of our knowledge, there is no work that utilizes Rice husk (RH), rice husk ash (RHA), metakaolin (MK), ground granulated blast furnace slag (GGBS) and palm oil fuel ash (POFA) in geopolymer binder for evaporative cooling applications. However, the compressive strength of prepared geopolymers is not as high when compared to the other geopolymers; it is within the accepted range for use as evaporative cooling materials.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Physical Properties** & \begin{tabular}{c} **Raw** \\ **RH** \\ \end{tabular} & **MK** & **POFA** & \begin{tabular}{c} **Raw RH** \\ **Ash** \\ \end{tabular} & **Slag** & \begin{tabular}{c} **Geopolymer** \\ **Sample 16** \\ **before Casting** \\ \end{tabular} & \begin{tabular}{c} **Geopolymer** \\ **Sample 16** \\ **Casting** \\ \end{tabular} \\ \hline \begin{tabular}{c} Surface area (m\({}^{2}\)/g) \\ Pore size (nm) \\ Total pore volume of \\ pores (cm\({}^{3}\)/g) \\ \end{tabular} & 1.11 & 12.26 & 3.45 & 16.55 & 4.22 & 13.22 & 5.78 \\ \
\begin{tabular}{c} Surface area (m\({}^{2}\)/g) \\ Pore size (nm) \\ Total pore volume of \\ pores (cm\({}^{3}\)/g) \\ \end{tabular} & 16.3 & 7.0 & 6.4 & 8.8 & 10.3 & 6.3 & 7.6 \\
\begin{tabular}{c} Total pore volume of \\ pores (cm\({}^{3}\)/g) \\ \end{tabular} & 0.004 & 0.053 & 0.011 & 0.038 & 0.022 & 0.043 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Texture properties of raw materials and sample 16.  
Figure 6: FT-IR spectra of raw materials and geopolymer sample 16 before and after casting.  
**Acknowledgments:** The authors would like to thank the DIP-2015-028 and DLP-2013-015 Research Funds for providing financial support for this study.  
**Author Contributions:** [PERSON]: conception and design of study; [PERSON]: acquisition and analysis of data; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]: interpretation of data, revising the manuscript and contributed analysis tools.  
**Conflicts of Interest:** The authors declare no conflict of interest.