esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Early Age Concrete Pavement Strength by Combined Nondestructive Tests
## 2 Materials and Methods  
The test pavement was constructed on 13 July 2020, in Imperial, Pennsylvania, U.S., using a slipform payer in a work vehicle parking lot. The dowelled join clone crap pavement (JPCP) consisted of 3.7 m \(\times\) 3.7 m \(\times\) 0.2 m (12 ft \(\times\) 12 ft \(\times\) 0.67 ft) concrete slabs. The concrete mixture met the requirements for Long-Life Concrete as specified by the Pennsylvania Department of Transportation (PennDOT) [27]. The ultimate compressive strength of this mixture was 37.9 MPa (5500 psi) and the ultimate flexural strength was 5.9 MPa (850 psi). Additional information on the dataset is available elsewhere [20].  
Maturity testing was performed according to ASTM C1074 [14]. The temperature was monitored using thermocouples in two locations within the slab: one at the center and another near the lane edge. Data were recorded at 5-min intervals, beginning immediately after placement and continuing to 14 days. thermocouples were also placed at the center of three cylinder and three beam specimens in a laboratory and monitored similarly to the field.  
Ultrasonic testing was used to measure the shear wave velocity at six locations on each slab: one at the center, another near the lane edge, and one in each corner, taking care to avoid dowel bars. A linear array ultrasonic device, A1040 MIRA from Acoustic Control Systems, Saarbrucken, Germany, was used at a frequency of 50 kHz for wave velocity measurements. MIRA has 48 dry point contact transducers located on the bottom of the device to emit and receive signals in 12 linear array channels. The shear waves are recorded, stored, and analyzed for every scan in 66 signal time histories, and the average wave velocity over multiple measurements is reported. Data collection began approximately five hours after placement once the concrete tolerated walking on the slab surface. Measurements were collected regularly through the first day with additional testing 1, 3, 5, 7, and 14 days after placement.  
Cylinder and beam specimens were cast in a laboratory using the same concrete placed. Compressive and flexural strength testing was performed using ASTM standards C39 and C78, respectively [28; 29]. Three cylinder and three beam specimens were tested for strength at 1, 3, 5, 7, and 14 days. The specimens monitored for temperature were tested for strength on day 14. Before strength testing, each beam specimen was scanned on each side using MIRA for wave velocity measurements. Table 1 shows a summary of the relevant field data.
## 2 Alternative Silicon Materials to Avoid Boron-Oxygen (B-O) Light-Induced Degradation (LID)  
Fundamentally, B-O related LID occurs in industrial p-type Cz silicon solar cells due to the simultaneous presence of boron and interstitial oxygen (O\({}_{\text{i}}\)) in the silicon wafer. Some studies have investigated the impact of the concentration of both boron and O\({}_{\text{i}}\) on the extent of B-O LID. The recombination rate from B-O defects is often used to provide a quantitative estimation of the defect concentration, denoted as the normalised defect density (N\({}_{\text{BO}^{*}}\)) [20]. The N\({}_{\text{BO}^{*}}\) is strongly dependent on the interstitial oxygen concentration ([O\({}_{\text{i}}\)]) [21], scaling approximately quadratically with [O\({}_{\text{i}}\)] [8; 22; 23; 24]. A strong dependence of N\({}_{\text{BO}^{*}}\) on the boron doping concentration has also been observed. For the most common Cz wafers used in the industry [5], doped solely with boron, the N\({}_{\text{BO}^{*}}\) scales approximately linearly with the boron doping concentration [20; 22; 23; 25; 26]. This boron-doping dependence is also the case for p-type Cz wafers co-doped with boron and gallium [27]. Even phosphorous-doped n-type Cz-Si partially compensated with boron shows B-O LID, although in that case, the kinetics differ, most probably due to the fundamentally different background conditions (carrier concentrations, Fermi level position etc.) [28; 29; 30]. For further details, see Ref. [12].  
For p-type Cz wafers doped solely with boron, an empirical model of the stable carrier lifetime after the formation of B-O defects was presented in early work. This work established a reduction in carrier lifetime scaling approximately linearly with increasing boron concentration and quadratically with increasing [O\({}_{\text{i}}\)] [23]. Subsequently, one option to avoid B-O related LID is to modify the source material used to make the solar cells. In particular, a reduction in the extent of B-O LID can be achieved by reducing either the boron or O\({}_{\rm i}\) concentration in the silicon material.
## 5 Load Segmentation and Aggregation  
### Load Aggregation  
Load aggregation is a basic requirement for the participation of small customers in the markets, but it can become a difficult task from technical, regulatory, and educational points of view. From a regulatory perspective, small customer segments usually face important barriers. For instance, Spain is one of the countries that have proposed significant barriers to small consumers [29] with respect to response requirements (e.g., a threshold above \(1\) MW for the capacity of response). This threshold involves a more complex aggregation trough more and different appliances and, consequently, more heterogeneous loads, consumers (e.g., load service patterns, dwelling characteristics), and kinds of demand-side resources. From the point of view of EWH, this aggregation encompasses that load must be split into homogeneous (HCG) and quasi-homogeneous control groups (QHCG) according to the water tank size, customer patterns, or energy conversion technology. This segmentation of the overall end-use (water heating) makes the flexibility of demand easier and more effective. The participation of controllable HCG and QHCG loads in several markets and services seems necessary to achieve greater revenue and cost-effectiveness for customers and aggregators through sharing costs of more complex enabling technology (smart meters and controllers, sensors, gateways, etc.) necessary for more complex management (aggregation) of resources.
# Alternative Pedicle Screw Design via Biomechanical Evaluation
## 3 Mechanical Strength of the New Design
### Numerical Testing for Pull-Out Strength
#### 3.2.1 Establishment of the FEA Model  
Among the integrated models, the lumbar vertebra model was derived from computed tomography (CT). A typical adult L4 was scanned using CT with a constant thickness of 1 mm, and the three-dimensional (3D) structure was obtained by processing the image. The CT image data was used to extract vectorized images of the outer and inner boundaries of each section. Figure 5 shows the 3D structure of L4. The vertebra consists of a gray posterior element and a yellow disk. The posterior elements are composed of pedicles, transverse processes, facet joints, and spinous processes.  
Lumbar vertebrae comprise various materials: a dense outer layer of cortical bone (1 mm thick) and internal cancellous bone. The posterior elements are more flexible than the cancellous bone.  
Figure 5: Three-dimensional (3D) structure for the L4 vertebra FEA model.  
The disk has an external cortex and the top and bottom endplates. The endplates are thin cartilage tissue (1 mm thick). The material is assumed to be homogeneous and isotropic. [23; 24]. Table 3 reports the properties of the material.  
The 8-node hexahedral elements were used to generate meshes in the disk. The meshes have concave curved shapes to exactly match the undulating surface of the disk. Eight-node hexahedron and six-node tetrahedral elements were applied to create meshes in the posterior elements. This combination of meshes matched the complex shape of the posterior element.  
To integrate the lumbar model with the pedicle screw model, a cavity was created in L4 that fully coincided with the screw, after which the pedicle screw model was inserted into the cavity. The cavity was oriented at 30\({}^{\circ}\) from the center of the pedicle to the disk. Since complete osseointegration was assumed, the tie-contact condition was applied to the interface between the bone and the screw. The element type of the screw was a 10-node tetrahedron, which is a general type of FEA solver. The material of the pedicle screw was a biocompatible titanium alloy (Ti-6 Al-4V ELI), the material properties of which are summarized in Table 4[25]. The number of nodes and elements in all of the unified models was 106,582 and 57,938, respectively. The integrated model is shown in Figure 6.  
Each node on the upper and bottom endplates was constrained to apply pull-out force that mimics the tensile force during human spine movement. In addition, 600 N of pull-out force was applied to the pedicle screw head in the direction from the cavity to the screw head. These forces are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bony Structure** & **Young’s Modulus (MPa)** & **Poisson’s Ratio** \\ \hline Ti-6 Al-4V ELI & 114,000 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Material properties of the pedicle screw.  
Figure 6: Integrated FEA model with L4 and pedicle screws: (**a**) Top view with insertion angle and (**b**) posterior view with insertion location.
## 3 Data and Methods
### Method  
The POT technique does not require phase decoupling, can overcome problems such as negative correlation, and is widely used in the monitoring of large-gradient surface deformation, such as glacier flow, landslide displacement, and seismic deformation fields [24]. Additionally, with the small-baseline subset (SBAS) technique, deformation time-series results are obtained through the combination of spatiotemporal baselines formed by two baseline constraints in time and space. The research process is shown in Figure 2 and contains three key steps: (1) obtaining the range-oriented displacement using the POT technique; (2) applying the POT-SBAS technique; and (3) implementing the POT-stacking technique.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Sentinel-1A** & **LuTan-1** \\ \hline Polarization mode & VV & HH \\ Orbit mode & Ascending & Descending \\ Radar wavelength (cm) & 5.6 (C-band) & 23.8 (L-band) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the satellite data used in this study.  
Figure 2: The workflow of the study.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 3 Algorithms
### Projection onto the Set \(\Lambda\) Described by LMIs  
The projection of \((X_{1},X_{2})\) onto the set \(\Lambda\) in (15) can be described as convex optimization with LMIs [34; 35].  
\[\begin{split}\underset{Z_{1},Z_{2},W\in\mathcal{S}_{u}}{\text{ minimize}}&\text{tr}(W)\\ \text{subject to}&\quad F(Z_{1},Z_{2})\preceq 0,\\ &\quad\begin{bmatrix}W&(Z-X)^{\top}\\ Z-X&I\end{bmatrix}\succeq\epsilon I,\end{split} \tag{29}\]  
where \(Z=[Z_{1}\;Z_{2}]^{\top}\) and \(X=[X_{1}\;X_{2}]^{\top}\).  
**Remark 2**.: _[PERSON] and [PERSON] [13] have also proposed an alternating projection method. This method is described as_  
\[\begin{split} Z[k]&=\Pi_{\mathcal{R}}\big{(}\Pi_{ \mathcal{D}}(X[k])\big{)}\\ X[k+1]&=\Pi_{\Lambda}(Z[k]),\quad k=0,1,2,\ldots,\end{split} \tag{30}\]_where_  
\[\mathcal{D} \triangleq\bigg{\{}Z\in\mathcal{S}_{2n}:Z=\begin{bmatrix}X_{1}&0\\ 0&X_{2}\end{bmatrix},X_{1},X_{2}\in\mathcal{S}_{n}\bigg{\}},\] \[\mathcal{R} \triangleq\big{\{}Z\in\mathcal{S}_{2n}:\text{rank}(Z+J)\leq r\big{\}}, \tag{31}\] \[J \triangleq\begin{bmatrix}0&I\\ I&0\end{bmatrix}.\]  
_We note that \(\Omega_{r}\) in (14) is the intersection of \(\mathcal{D}\) and \(\mathcal{R}\). The difference between the proposed algorithm (16) and the above algorithm (30) is the projection onto the set \(\Omega_{r}\) of rank-constrained structured matrices. Namely, the algorithm in [13] approximates the projection \(\Pi_{\Omega_{r}}\) by a composite projection \(\Pi_{\mathcal{R}}\Pi_{\mathcal{D}}\)._  
Finally, we summarize the proposed algorithm to solve Problem 3 in Algorithm 1.  
```
0: Initial guess \((X_{1}[0],X_{2}[0])\in\mathcal{S}_{n}\)
0:\((X_{1},X_{2})\in\Omega_{r}\cap\Omega_{\Lambda}\) for\(k=0,1,2,\ldots,N-1\)do \(X_{1}\gets X_{1}[k]\)\(\triangleright\) Projection onto \(\Omega_{r}\) \(X_{2}\gets X_{2}[k]\) \(\tilde{Z}[0]\gets 0\) \(W[0]\gets 0\) for\(i=0,1,2,\ldots,M-1\)do \(Z_{1}[i+1]\leftarrow\big{(}1+\frac{\rho}{2}\big{)}^{-1}\big{(}X_{1}+\frac{ \rho}{2}M_{11}[i]\big{)}\) \(Z_{2}[i+1]\leftarrow\big{(}1+\frac{\rho}{2}\big{)}^{-1}\big{(}X_{2}+\frac{ \rho}{2}M_{22}[i]\big{)}\) \(\tilde{Z}[i+1]\leftarrow\Pi_{\mathcal{C}_{r}}\bigg{(}\begin{bmatrix}Z_{1}[i+ 1]&I\\ I&X_{2}[i+1]\end{bmatrix}-W[i]\bigg{)}\) \([i+1]\gets W[i]+\tilde{Z}[i+1]-\begin{bmatrix}Z_{1}[i+1]&I\\ I&Z_{2}[i+1]\end{bmatrix}\) endfor \(X\leftarrow[Z_{1}[M],Z_{2}[M]]^{\top}\)\(\triangleright\) Projection onto \(\Omega_{\Lambda}\) \((Z_{1},Z_{2},W)\leftarrow\underset{Z_{1},Z_{2},W\in\mathcal{S}_{n}}{\arg\min} \text{tr}(W)\) subject to \(F(Z_{1},Z_{2})\preceq 0\), \(\begin{bmatrix}W&(Z-X)^{\top}\\ Z-X&I\end{bmatrix}\succeq\epsilon I\), \(X_{1}[k+1]\gets Z_{1}\) \(X_{2}[k+1]\gets Z_{2}\) endfor \(X_{1}\gets X_{1}[N]\) \(X_{2}\gets X_{2}[N]\)
```  
**Algorithm 1** Algorithm to solve Problem 3
## 3 Results
### Relative Humidity Increase
(**a**) As function of inlet air velocity (\(u\)). (**b**) As function of pad thickness (\(L\)). (**c**) As function of dry-bulb temperature of inlet air (\(T_{dh_{z}}\)). (**d**) As function of relative humidity of inlet air (\(\phi_{i}\)). For each plot the other parameters remained constants: \(u\) = 1.5 m s\({}^{-1}\), \(L\) = 0.1 m, \(T_{dh_{z}}\) = 30 \({}^{\circ}\)C and \(\phi_{i}\) = 60%. \(CL\): solid black line; \(CS_{1}\): blue solid line; \(CS_{2}\): blue dashed line; \(BC\): red solid line; \(PS_{1}\): magenta solid line; \(PS_{2}\): magenta dashed line. Table 9 shows a comparison between the output pad variables: \(\eta\), THI, \(\Delta T\) and \(\Delta\phi\) for constant input variables: \(u\) = 1.5 m s\({}^{-1}\), \(L\) = 0.1 m, \(T_{db_{i}}\) = 30 \({}^{\circ}\)C and \(\phi_{i}\) = 50%. For this conditions, the \(CS\) pads present the best behaviour between the alternative pads: highest cooling efficiency, lowest THI, highest dry-bulb temperature drop but also the highest relative air humidity increasing. The lowest \(\Delta T\) of the \(PS\) pads do not compensate the increasing of \(\phi\), obtaining the highest values of THI.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 4 Critical Auxiliary Data Necessary to Support Detecting SCE from SAR Data
### Digital Elevation Model, Influence of Topography on SAR-Based Snow Detection
[169] revealed that the change of H and \(\alpha\) caused by the snowpack would be smaller for lower LIA. [PERSON] et al. [180] observed that the degree of polarization decreases when the LIA increases. Furthermore, several studies found that the impact of surface roughness on the signal is more significant for high LIA [94; 181].
# Deep-Learning Software Reliability Model Using SRGM as Activation Function
## 3 Software Reliability Model
### Software Reliability Model Using Deep Learning
#### 3.2.3 Proposed Software Reliability Model  
Typical failure occurrences follow either an S shape, in which failures occur in increasing numbers and then decrease past a certain point, or a concave shape, in which many failures occur at the beginning and end. The SRGMs developed thus far are based on the assumption of these shapes. The S-shaped form is similar to the sigmoid function in deep-learning activation functions. The sigmoid function has an S-shaped or sigmoidal curve with \(1/2\) when \(x\) is 0 in Equation (9) [49, 50, 51].  
\[f(x)=\frac{1}{1+\exp(-x)} \tag{9}\]  
In this study, the sigmoid function was the activation function used because it most closely resembles the shape of the software reliability model, and a generalized software reliability model was developed with a \(b(t)\) function whose input value is not a simple indexing number but the failure detection rate of the software reliability model. The failure detection rate function \(b(t)\) utilizes \(\frac{b^{2}t}{b!+1}\) wherein parameter \(b\) is the shape parameter and has a structure in which the failure probability changes with time. The final model follows Equation (10).  
\[m(t)=\frac{1}{1+\exp\left(-\frac{b^{2}t}{bt+1}\right)} \tag{10}\]  
As mentioned previously, this model is a modified sigmoid form. In this study, instead of using the well-known sigmoid or ReLU activation function in deep learning, the proposed model was employed by converting it into a modified form of the proposed software reliability model. This is the proposed deep-learning software reliability model, which applies mathematical and statistical assumptions instead of only data-dependent models, and the structure of this model is the same as that of the model defined in Section 3.2.1. There are a total of four hidden layers, and the number of nodes in the hidden layer is the same as the number of data. We do not have more nodes than the number of data, as this can lead to overfitting problems. The activation function utilizes the proposed software reliability model that is similar to the form of the sigmoid function. An optimizer method utilizes Adam, which simultaneously considers momentum and adaptive learning rate [52]. Based on this, we structure a deep learning software reliability model and train it for 100 epochs. Table 2 shows a description of the software reliability model using deep learning and the proposed deep learning software reliability model. In addition, 20 models (the four software reliability models using the deep learning introduced in Sections 3.2.1 and 3.2.2,the proposed deep-learning software reliability model introduced in Section 3.2.3, and the 15 models introduced in Section 3.1, are compared.
## 2 Overview of [PERSON]'s Theories for Non-Uniform Sediment Transport
### Critical Bed-Shear Stress of Non-Uniform Sediment  
#### 2.2.1 Critical Bed-Shear Stress for Erosion  
The erosion rate _E(k)_ for non-uniform cohesive sediment is a power function of the dimensionless excessive shear stress:  
\[E(k)=\left\{\begin{aligned} & M\left(\frac{\tau_{b}-\tau_{ck}}{ \tau_{ck}}\right)^{m}&\quad for\;\tau_{b}\geq\tau_{ek}\\ & 0&\quad for\;\tau_{b}<\tau_{ck}\end{aligned}\right. \tag{3}\]  
where \(\tau_{b}\) is the bed-shear stress; \(\tau_{ck}\) is the critical bed-shear stress for erosion; \(M\) is the erosion coefficients; and \(m\) is an exponent.  
The critical bed-shear stress for erosion can be regarded as the bottom shear stress of flow when the incipient probability is equal to 0.05 [30]. In that case, the expression of critical bed-shear stress for erosion can be obtained from Formula (2).  
\[\tau_{ck}=0.032\frac{(\rho_{s}-\rho)gD_{k}}{\xi_{k}}+0.048C_{0}\left(\frac{ \rho_{s}^{\prime}}{\rho_{s*}^{\prime}}\right)^{l}\left(\frac{D_{m}}{D_{k}} \right)^{0.5}\frac{\rho g(H+h_{0})\delta}{\xi_{k}D_{k}} \tag{4}\]  
The first term of Formula (4) represents the effect of gravity, and the second term represents the effect of cohesive force. It is difficult to move the fine sediment because of the cohesive force, so the smaller the particle size, the greater the critical shear stress required. For coarse particles, the second term is a small value relative to the first term, so Formula (4) can be applied both to cohesive and non-cohesive sediment.
# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 6 Experimental Study
### Experiments
#### 6.3.6 Effect of Various Numbers of Peaks  
In an environment with moving peaks, the number of peaks is essential in determining the results. The numbers of different peaks indicate the algorithm's scalability in various states. This experiment is designed to examine the performance of the proposed algorithm when several peaks change. According to Table 1, the number of peaks changed within the range from 1 to 200. In this experiment, the proposed algorithm the CaAIS is compared with well-known algorithms such as multi-swarm optimization in two states, mCPSO and mQPSO [22], PSO with speciation (SPSO) [32], cellular differential evolution (CLDE) [54],  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**M = 5**} & \multicolumn{2}{c}{**M = 50**} & \\ \cline{2-5}  & **Offline Error** & \(t\)**-Test** & **Offline Error** & \(t\)**-Test** \\ \hline AIIA & 2.6098 \(\pm\) 0.43 & + & 3.7534 \(\pm\) 0.31 & + \\ SAIS & 12.1631 \(\pm\) 0.12 & + & 11.5783 \(\pm\) 0.13 & + \\ BCA & 2.2566 \(\pm\) 0.49 & \(\sim\) & 3.1245 \(\pm\) 0.66 & \(\sim\) \\ CLONALG & 3.3376 \(\pm\) 1.25 & + & 10.5300 \(\pm\) 0.21 & + \\ Opt-aiNet & 2.3963 \(\pm\) 0.05 & + & 4.7600 \(\pm\) 0.06 & + \\ LAIA & 2.7813 \(\pm\) 0.35 & + & 2.9497 \(\pm\) 0.36 & \(\sim\) \\ CPSOC & 2.1923 \(\pm\) 0.13 & \(\sim\) & 2.9546 \(\pm\) 0.15 & \(\sim\) \\ CaAIS & 2.2979 \(\pm\) 0.12 & \(\sim\) & 3.0707 \(\pm\) 0.19 & \(\sim\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of OE \(\pm\) standard error for the CaAIS versus other AIS algorithms with \(t\)-test results.  
Figure 9: OE for different values of severity shift.  
fast multi-swarm optimization (FMSO) [71], dynamic population differential evolution (DynPopDE) [72], speciation-based firefly algorithm (SFA) [33], particle swarm optimization with composite (PSO-CP) [73], learning automata-based immune algorithm (LAIA) [46], cellular PSO (CLPSO) [53], multi-swarm cellular PSO with local search (CPSOL) [74], and multi-population differential evolution (DE) algorithm with learning automata (Dyn-DE+LA) [75]. The effect of the varying number of peaks is listed in Table 3. It should be noted that the results of the compared algorithms are the same as those of their papers; therefore, in some cases, the results are not presented. According to Table 3, the CaAIS outperforms peer algorithms for 40 and 100 peaks, but for different numbers of peaks, another algorithm may have been the best. Table 3 shows that the CaAIS delivers marginally superior results for different numbers of peaks. The CaAIS produces better results as the number of peaks