esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Early Age Concrete Pavement Strength by Combined Nondestructive Tests
## 2 Materials and Methods
### Wave Velocity-Strength Relationship  
In this study, the ultrasound wave propagation velocity was analyzed in relation to the concrete's Young's modulus, which strongly correlates to the concrete's flexural and compressive strength. Therefore, it can be used to estimate the strength of a given concrete mix. A linear array device called MIRA was used to determine the concrete's ultrasound velocities.  
To develop the strength correlation, strength and wave velocity data must be collected from beam specimens that use a similar concrete mixture as the slab. The beam must first  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test Time** & **Compressive Strength,** & **Modulus of Rupture,** & **Cylinder Maturity,** & **Shear Wave Velocity,** \\  & **MPa (psi)** & **MPa (psi)** & \({}^{\circ}\mathrm{C}\mathrm{-}\mathrm{h}\mathrm{r}\) & **m/s** \\ \hline
1 day & 22.8 (3311) & 4.1 (597) & 847 & 2622 \\
3 day & 29.8 (4329) & 5.1 (741) & 2006 & 2720 \\
5 day & 30.5 (4426) & 5.0 (719) & 3192 & 2721 \\
7 days & 34.7 (5040) & 5.6 (819) & 4409 & 2766 \\
14 days & 36.1 (5237) & 5.7 (824) & 8604 & 2798 \\ \hline \end{tabular}
\end{table}
Table 1: Test pavement dataset.  
be scanned using the ultrasonic device to determine the average wave velocity at that age. Immediately following the scan, the beam should be tested for flexural strength. The test section used five suggested days of testing, 1, 3, 5, 7, and 14, but additional times can be added if necessary. At least 15 beam specimens are needed, so three specimens are tested at each time increment to establish the average strength for that day. ASTM C78 and C293 are procedures that can be used to determine the flexural strength of the beams [29; 30]. The following relationship between flexural strength and shear wave velocity is assumed:  
\[\mathrm{M_{r}}=\mathrm{M_{ru}}\times\mathrm{e^{a_{s}SWV+b_{s}}}, \tag{4}\]  
where SWV is the shear wave velocity in m/s and \(\mathrm{a_{s}}\) and \(\mathrm{b_{s}}\) are shear wave velocity calibration coefficients depending on the concrete mix properties.
## 2 Materials and Methods
Normalized power density of the artificial lighting system: 2.50 (W/m\({}^{2}\)-10 lux), model design illuminance: 300 lux, visible fraction of 0.18, radiation fraction of 0.42. DesignBuilder includes five luminaire types, which affect the output data of lighting loads. The \"Suspended\" luminaire type was chosen in this simulation. These lights hang from the ceiling by a rod, cord, or chain; suspended fixtures provide uniform light distribution. The house is heated and cooled using an HVAC template (fan coil unit (4-pipe), air-cooled chiller) as the default setting in DesignBuilder. Because buildings are formed of numerous integrated systems, changes made to a specific element can affect other connected elements. Therefore, to determine the impact \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Building Components** & **Layer Name** & **Thermal Conductivity (W/m \(\times\) K)** & **Thickness (cm)** \\ \hline \multirow{4}{*}{**External walls**} & Stone & 2.2 & 5 \\ \cline{2-4}  & Reinforced concrete & 2.5 & 10 \\ \cline{2-4}  & Extruded polystyrene & 0.03 & 5 \\ \cline{2-4}  & Concrete block & 1.6 & 10 \\ \cline{2-4}  & Cement plaster & 1.2 & 1 \\ \cline{2-4}  & **U-Value (W/m\({}^{2}\)\(\times\) k)** & \multicolumn{2}{c}{**0.56**} \\ \hline \multirow{4}{*}{**Internal walls (partitions)**} & Cement plaster & 1.2 & 3 \\ \cline{2-4}  & Concrete block & 1.6 & 10 \\ \cline{2-4}  & Cement plaster & 1.2 & 3 \\ \cline{2-4}  & **U-Value (W/m\({}^{2}\)\(\times\) k)** & \multicolumn{2}{c}{**2.5**} \\ \hline \multirow{4}{*}{**Ground Floor**} & Ceramic/clay tile & 0.52 & 3 \\ \cline{2-4}  & Miscellaneous materials–aggregate & 1.3 & 10 \\ \cline{2-4}  & Concrete & 1.7 & 32 \\ \cline{2-4}  & **U-Value (W/m\({}^{2}\)\(\times\) k)** & \multicolumn{2}{c}{**1.88**} \\ \hline \multirow{4}{*}{**Flat Roof**} & Asphalt & 0.7 & 2 \\ \cline{2-4}  & Extruded polystyrene & 0.03 & 5 \\ \cline{2-4}  & Miscellaneous materials–aggregate & 1.3 & 10 \\ \cline{2-4}  & Reinforced concrete & 2.5 & 32 \\ \cline{2-4}  & Cement plaster & 1.2 & 2 \\ \cline{2-4}  & **U-Value (W/m\({}^{2}\)\(\times\) k)** & \multicolumn{2}{c}{**0.54**} \\ \hline \multirow{4}{*}{**Window Frames**} & Aluminum & 160 & 0.2 \\ \cline{2-4}  & Polyvinyl chloride (PVC) & 0.17 & 0.5 \\ \cline{1-1} \cline{2-4}  & **U-value (W/m\({}^{2}\)\(\times\) K)** & \multicolumn{2}{c}{**5.01**} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Building materials’ thermal conductivity and their thicknesses. of efficient lighting systems on the size of windows, each of the building parameters was set as previously described: a total of 11 WWR situations, ranging from 0% to 100% with increments of 10%, were evaluated for various design options to determine the optimum WWR for the LED system. The height of the windows was fixed at 1.5 m for the external windows layout. After the completion of all the necessary steps in the preparation of the model to simulate energy as described, the simulation was performed and a general DesignBuilder report was produced for the different WWR situations.
# THz Range Low-Noise SIS Receivers for Space and Ground-Based Radio Astronomy +
## 3 Experimental Results
### High Band (790-950 GHz) Mixers
In addition to the integrated IF data in Figure 13, we measured [51] the IF spectra for all LO points; the best IF noise temperature in the 4-12 GHz range on LO frequency is shown in Figure 14. As expected, it is lower than the integrated results, although the consistency of the curves does not change--the mixer with the lowest noise temperature in Figure 13 remains the best and in Figure 14 [51]. The data in Figure 14 demonstrate the receiver performance at typical spectral lines radio astronomy observations.
## 5 Understanding Decentralised Identity Systems
### ShoCard
#### 5.9.1 ShoCard Architecture  
It operates through a three-phase process: bootstrapping, certification, and validation (see Figure 8) [71].  
* In the bootstrapping phase, the ShoCard mobile app generates a cryptographic key pair and scans the user's credentials. These user credentials are encrypted with the data and stored as signed hash in a Bitcoin transaction. The unique ShoCardID serves as a reference point on the blockchain.
* In the next phase, certification, users interact with SPs to add verified attributes to their identity, which are hashed, signed, and stored on the blockchain.
* During the validation phase, a relying party retrieves and verifies these certifications by checking signatures and comparing the data against the blockchain records.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 4 Design Improvement of XY Stage
#### Experimental Verification of Effectivity of the Design Improvement of XY Stage  
The manufactured test bed in Figure 26 includes an improved XY linear stage and a vision camera. Artifact glass, in Figure 27, was utilized for position calibration and the repeatability test.  
Calibration artifacts were precisely printed to be 0.5 mm in diameter at 5 mm intervals by light exposure to glass.  
The position compensation of the XY stage was performed recognizing this circular dot using the vision camera.  
The XY stage started from the origin down a path passing through 15 points on the artifact glass, as shown in Figure 27, according to the actual electrical test machine operations. The cycles were repeated 25 times. The \(x\) and \(y\) data of each point were gathered through a camera. Ideally, the coordinates of the 15 points should be aligned with the center of the camera. The actual points deviated from the center due to errors from the stage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Axis** & **Length** & **Width** & **Balancing** & **FO** & **Yaw Error** \\  & **(mm)** & **(mm)** & **_I/uv_** & **Weight (kg)** & **(\%)** & **(\(\upmu\)m)** \\ \hline \(x\) & 370 & 895 & 0.41 & 17.8 & 80 & \(\pm 0.67\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Platform dimensions and predicted yaw error for the improved x-axis stage.  
Figure 25: Sectional view of the simply supported blocks in Figure 24.  
A set of the actual positions of the 15 points with respect to the center of the camera for the repeated 25 cycles is shown in Figure 28a points without the balancing weight and Figure 28b with the balancing weight. In Figure 28a, the scatter in the \(x\)-axis was large and out of the \(\pm 1.0\) um target, and in Figure 28b, the scatter range was within the target. The six sigma analyses of the point coordinates show changes from \(1.62\sigma\) to \(5.51\sigma\) in the x-axis and from \(3.68\sigma\) to \(3.21\sigma\) in the y-axis. The process's capability has been greatly improved by more than \(5\sigma\) in the x-axis with a slight setback in the y-axis, but still allowable beyond the practical limit of \(3\sigma\). Thus, the beneficial effects of \(CS\) and \(CM\) modifications were clearly verified.  
The scatter patterns in Figure 28 seem to be elongated, with their major axes covering the second and the fourth quadrants. This is due to one-way travel in the \(x\)-axis direction during the 15 point track illustrated in Figure 27. The yaw and tilt of the platform cause the elongation through the camera.  
It is evident that the application of a balancing weight to the y-axis motion would produce similar results.  
Figure 27: The calibration artifact with a pitch circle of 5 mm mounted on the platform of XY stage. The origin and 15 point track viewed from a stationary camera.  
Figure 26: Test bed for improved XY linear stage.
## 3 Data Processing Parameters  
The statistical parameters for both acoustic sensors were calculated and are discussed in this section; they are shown in Table 2. The RMS of a signal is not affected by noise, so it could be used to compare sensors' sensitivity. The higher the value of the signal's RMS, the higher the sensitivity of the sensor. The value of RMS for the signal received by the MFC sensor was almost twice the value of the signal received by the PZT sensor, which could indicate that the MFC sensor is twice as sensitive as the PZT sensor. Standard deviation (STD) is a measure of the amount of variation or dispersion of a set of values; a low standard deviation shows that the values tend to be close to the mean of the signal. If the mean of the signal tends to zero, STD and RMS become equal. The impulse factor compares the height of a peak to the mean level of the signal, and a high impulse factor shows that the signal has more peaks. This parameter is widely used to monitor signals with frequent and transient changes and is useful in monitoring the impact behavior of the signals. The amount of energy of a signal indicates the amount of its perturbation. The energy of a signal is another parameter that indicated that the MFC sensor wis more sensitive than the PZT sensor. Kurtosis is the length of the tails of the signal distribution or, equivalently, how outlier-prone the signal is. Developing PDs can increase the number of outliers and, therefore, increase the value of the kurtosis metric. The value of kurtosis for a normal distribution is equal to 3; a higher kurtosis value means that the signal has fast edges or high amplitude. This parameter increases with the occurrence of a defect, and usually, if the value of kurtosis for a signal exceeds 3, that means the system is faulty.  
Faults can impact the distribution symmetry of a signal and, therefore, increase the level of skewness. Experiments have shown that every type of insulation fault has its unique statistical parameters that can be used to diagnose PD faults in transformers. In Table 2, data were obtained from an acoustic-based analysis of an artificial PD that was equivalent to corona discharges that happen inside transformers. For the online monitoring of transformers, information such as the type of fault and its repetition rate can be obtained by extracting these parameters and storing them into a database. Parameters such as STD, skewness, and kurtosis can be used for partial discharge identification and classification [21; 22; 23]. RMS and energy are two parameters that can be used to compare sensors' sensitivity. Increases in the values of some parameters, such as impulse factor, energy, and kurtosis, mean that there is a fault in the system; thus, in online monitoring, these parameters can be set as a threshold for the system to react. As discussed, these parameters are powerful tools for finding the nature of a fault, and an in-depth data analysis can be used in partial discharge fault identification and classification.
## 2 Materials and Methods
### Samples Used for Measurements
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Powder** & **Powder Name** & **Grain Size** [mm] & **Bulk Density** [g/cm\({}^{3}\)] & **Areal Density** **per Grain** **Layer [g/cm\({}^{2}\)] & **Standard** **Deviation of Grain Size** [mm] & **Porosity** & **Plot on Figures** \\ \hline \multirow{2}{*}{A1-1} & Hollow glass beads & & & & & & \\  & (Hobbico, Inc., & 0.0291 & 0.080 & \(2.33\times 10^{-4}\) & \(1.49\times 10^{-3}\) & N/A \({}^{*}\) & \(\bullet\) \\  & Champaign, IL, USA) & & & & & & \\ \hline \multirow{2}{*}{A1-2} & Hollow glass beads d \(>\) & & & & & & \\  & 38 \(\upmu\)m (Hobbico, Inc., & 0.0675 & 0.064 & \(4.30\times 10^{-4}\) & \(1.71\times 10^{-3}\) & N/A \({}^{*}\) & \(\bullet\) \\  & Champaign, IL, USA) & & & & & & \\ \hline \multirow{2}{*}{A1-3} & Hollow plastic beads d \(>\) & & & & & & \\  & 38 \(\upmu\)m (Japan Filite Co., & 0.0423 & 0.030 & \(1.26\times 10^{-4}\) & \(1.41\times 10^{-2}\) & N/A \({}^{*}\) & \(\bullet\) \\  & Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{A1-4} & Hollow urethane & & & & & & \\  & balloons (OK Model Co., & 0.1205 & 0.0472 & \(5.69\times 10^{-4}\) & \(3.77\times 10^{-2}\) & N/A \({}^{*}\) & \(\bullet\) \\  & Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{A2-1} & Granulated silica (Featherfield Co., Ltd., & 0.0428 & 0.057 & \(2.43\times 10^{-4}\) & \(2.23\times 10^{-2}\) & 0.974 & \(\bullet\) \\  & Hiroshima, Japan) & & & & & & \\ \hline \multirow{2}{*}{A2-2} & Calcium silicate (Xonoffite powder (XJ)) & & & & & & \\  & (Japan Insulation Co., & 0.0181 & 0.0805 & \(1.46\times 10^{-4}\) & \(8.29\times 10^{-3}\) & N/A \({}^{*}\) & - \\  & Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{A2-3} & Calcium silicate (Tobermorite powder (TK)) & & & & & & \\  & Co., Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{A2-4} & Calcium silicate (Tobermorite powder (TJ)) (Japan Insulation Co., Ltd., Osaka, Japan) & & & & & & \\  & 38 \(\upmu\)m (Japan Insulation & & & & & & \\  & Co., Ltd., Osaka, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-1} & Tak (Featherfield Co., & 0.00450 & 0.5126 & \(2.31\times 10^{-4}\) & \(3.67\times 10^{-3}\) & 0.810 & \(\times\) \\  & Ltd., Hiroshima, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-2} & Hollow glass beads (Sphericl\({}^{\text{\textregistered}}\) (110P8)) & & & & & & \\  & (Potters-Ballotini Co., & 0.0102 & 0.3642 & \(3.70\times 10^{-4}\) & \(4.30\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Ltd., Ibaraki, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-3} & Crystalline cellulose (Ceolus\({}^{\text{\textregistered}}\) TG-F20) (Asahi & 0.0110 & 0.2961 & \(3.25\times 10^{-4}\) & \(8.94\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Kasei Corporation. & & & & & & \\  & Tokyo, Japan) & & & & & & \\ \hline \multirow{2}{*}{B-4} & Hollow glass beads (Cell spheres) (Taheiyo & 0.0053 & 0.1319 & \(6.99\times 10^{-5}\) & \(2.33\times 10^{-3}\) & N/A \({}^{*}\) & \(\times\) \\  & Cement Corporation.
## 4 Results and Discussion
### DA-OA-ICOS Combined with White Noise Perturbation
#### 4.1.3 Data Calibration for DA-OA-ICOS  
Following these experiments, the white noise perturbation of 30% modulation depth was finally chosen for system optimization. During the further calibration, standard CH4 gas was mixed with nitrogen by the gas mixing system to prepare seven gas mixtures with concentrations in the range of 700 ppm to 1200 ppm. The gas mixtures were then used to measure the direct absorption amplitude with the original signal and the white noise perturbation. Each concentration was measured 20 times. It could be concluded that the direct absorption amplitudes of the original signal and the white noise perturbation at different concentrations were significantly different, and these differences could be used for concentration inversion analysis. By fitting the experimental data with the quadratic polynomial, the data fitting curves for the original signal and the white noise perturbation were obtained. The fitting curves between CH4 concentration and the direct absorption peak under original signal and white noise perturbation are shown in Figure 5, and the relationship can be expressed by Equation (6). It could be concluded that the curve fitting of the original signal (black) was 0.99928, while the curve fitting of the white noise perturbation (red) was 0.99954. The ranges of relative deviation for the original signal and the white noise perturbation were 0.015-0.021 V and 0.007-0.008 V, respectively. The results showed that the optimization method with the white noise perturbation of 30% modulation depth significantly improved the curve fitting and enhanced the accuracy of the concentration inversion.  
\[\left\{\begin{array}{l}C_{Original-signal}=6449.99895-2269.64523A_{da}+197. 18295A_{da}^{2}\\ C_{White-Noise-Perturbation}=6936.1842-2581.21399A_{da}+243.91479A_{da}^{2}\end{array}\right. \tag{6}\]
###### Abstract  
This paper investigates the acoustic radiation characteristics of reinforced laminated cylindrical shell structures in the medium-low frequency range (1-1000 Hz) of aircrafts, submarines, and deepwater thrusters by combining simulation analysis and experimental testing. The research focuses on reinforced laminated plates, where experimental setup is established to measure structural modal parameters and structural acoustic power, and a finite element model is developed to verify the reliability of the finite element modeling method through comparison with experimental and simulation results. Simultaneously, a finite element model of the reinforced laminated cylindrical shell structure is established, and finite element simulation analysis is conducted to investigate the effects of different reinforcement methods, reinforcement quantities, reinforcement lengths, and reinforcement thicknesses on the acoustic radiation characteristics of the structure. The results indicate that in the frequency range of 1-1000 Hz, ribbed reinforcement exhibits better performance in reducing structural noise radiation compared to longitudinal reinforcement. With the increase in reinforcement quantity and thickness, the structural acoustic radiation decreases. Meanwhile, with the increase in reinforcement length, the overall sound power level of the structure shows a trend of first increasing and then decreasing.  
reinforced laminated cylindrical shell; finite element analysis; structural modes; acoustic radiation
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Stochastic Variable Node Decoder (SVND)  
The SVND module contains \(\eta_{v}=56\) parallel stochastic variable node processing units (SVNPUs). These SVNPUs can process one block column of the BGM \(H_{b1}\). Hence, each SVNPU processes one column of the PCM. Each SVNPU has multiple inputs and outputs equal to \(W_{v}+1\), where \(W_{v}\) is the highest column weight in the supported BGM code rate set. The extra input and output are utilized, respectively, for channel-intrinsic messages and the estimation of the decoded bit. It is essential to consider that the largest number of inputs and outputs must be taken into account to guarantee that each SVNPU can decode any arbitrary column-weight column in the code rate set. The SVND accepts the maximum SVN weight \(W_{v}=30\) input bits from the connected SCNs and an intrinsic bit from the intrinsic message memory unit (IMMU). By considering such values, SVND implements the SVN update operation, and the updated extrinsic values are sent to the corresponding SCNPUs.
## 3 Method
### Resulting Decision Matrix for Algorithm Selection
#### 3.2.1 Demanded Quality Criteria  
The demanded quality criteria are end-user requirements on the optimization algorithms. Their importance, which can vary for different end-users or different optimization problems, can be defined in the column \"Weight/Importance\" on a scale from 0 (not important) to 5 (very important). Each algorithm can be ranked in the \"Absolute Rating\" column for each criterion, again on a scale from 0 (does not fulfill criterion) to 5 (completely fulfills criterion).  
* Convergence to optimum value: The capability to always converge towards near-optimal solutions.
* Low computational time: The capability to find high quality solutions in a for the end-user acceptable time.
* Generation of new design alternatives: The capability to generate distinct design alternatives other than those obtained by small variations of the initial product configuration.
* Ease of use: The effort required to set up the algorithm for the problem. This includes, among others, the selection process for the algorithmic parameter values and how the user can interact with the algorithm.  
Figure 2: Design optimization decision matrix template.  
* Algorithm customization: The possibility and the effort required to make significant changes to the algorithm to increase the application range or to change parts of the algorithm.
* Applicability to whole product range: The effort required to apply the algorithm to the whole product range, e.g., lower/higher dimensional variants of the same problem or problems with slightly changed constraints.
* Low effort for integrating algorithm in operational system: The effort required to implement the algorithm into the system and to set up connections with the required external tools, e.g., CAD and CAE systems, databases, computing resources, and software libraries.
* Comprehensibility: The level of expert knowledge required to understand the solution creation process and the solution representation.
* Trade-off decision support: The capability to investigate different objectives and different constraint values with the same or a slightly modified algorithm.
## 4 Effect of Design Parameters on Seismic Performance of SCMSW Specimens
### Effect of Aspect Ratio on Hysteretic Response of SCMSW Specimens  
Four different aspect ratios were chosen to evaluate their effect on the hysteretic curve. Figure 13 shows the normalized load-displacement curve envelopes for comparable SCMSW specimens, which are plotted in terms of the observed lateral load and the lateral displacement. It is clearly visible that, when the aspect ratio of the SCMSW specimens decreased, the initial lateral stiffness increased. The lateral displacement increased as the aspect ratio increased. SW6 shows relatively high initial lateral stiffness and larger amount of lateral displacement. This is due to the existence of the boundary elements. SW3 and SW4 had the same amount of flexural reinforcement ratio and load ratio with different aspect ratios. Results indicated that there was a decrease in the maximum observed lateral load from 213 kN for SW3 to 115 kN for SW4, resulting in a strength decrease of 54% when the aspect ratio increased from 2 to 3.6.  
Figure 12: Yield length and plastic hinge length definitions.