esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Evaluation of Nano-Silica Sustainable Concrete
### Comparison between Nano-Silica and Micro-Silica  
Both of them are not crystalline materials (amorphous); therefore, they will not dissolve in concrete. Nano-silica is more reactive than micro-silica, because it contains more silicon dioxide content. Nano-silica has a smaller particle size than micro-silica. Consequently, it has a larger surface area. Therefore, nano-silica has a larger impact on the reactivity, strength development, and the refinement of the pore structure of the concrete. The weight per unit volume (bulk density) of nano-silica is lighter than both as produced and densified micro-silica due to its lighter mass. Both of them have a specific gravity of 2.2, which is lighter than Portland cement. Thus, adding nano-silica or micro-silica will not increase the density of the concrete. Nano-silica has a larger specific surface than micro-silica due to its smaller particle size. However, due to its smaller particle size, it has a higher water demand. Thus, it is necessary to use a water-reducing admixture or a superplasticizer in the mixture.  
The purpose of this study is to develop nano-silica sustainable concrete mixtures and evaluate the fresh and mechanical properties of the developed nano-silica concrete mixtures. Furthermore, the purpose is to study the morphological characteristics of nano-silica concrete.
## 3 Results and Discussion
### Phenomenology of Resisting Mechanisms
According to test results, the interface between old concrete and HPFRC was able to resist the reflected wave in RC-U35 series but not in RC-U55 series. It is apparent that, for RC-U55 series, the presence of longitudinal reinforcement has weakened the interface which was unable to resist the reflected wave. This result can be explained by the reduced contact area between old concrete and HPFRC due to the presence of the steel bars; according to the dimensions of the beam (width of 125 mm) and the diameter of the steel bars (\(2\times 16\) mm), the contact area at the interface of RC-U55 series was 26% smaller than that of RC-U35 series.
# A General Solution for the Errors in Variables (EIV) Model with Equality and Inequality Constraints
## 4 Experiment Analysis
### Experiment with Equality Constraints  
In this example, we use the data with equality constraints presented by [PERSON] and [PERSON] [19]. The coefficient matrix \(\mathbf{A}\), observation vector \(\mathbf{L}\) and cofactor matrix \(\mathbf{Q}\) are represented as follows:  
\[\mathbf{A}=\begin{bmatrix}-0.5&1&0\\ 0&1&0\\ 0&0&1\\ 1&0&1\end{bmatrix},\ \mathbf{L}=\begin{bmatrix}6\\ 3\\ 4\\ 10\end{bmatrix},\ \mathbf{Q}=\mathbf{I}_{16}\]  
Equality constraints: \(\begin{bmatrix}-2&0&3\end{bmatrix}\mathbf{X}=16\).  
The general solution for EIC-EIV model presented in this paper and the algorithm presented by [PERSON] and [PERSON] [19] are adopted to solve the EIV model with equality constraints. The corresponding adjustment results are listed in Table 1.  
The results of Table 1 indicate that the estimated parameters obtained by the two approaches are all identical. This shows that the processing method adopted in this paper is feasible. The equality constraint is an inequality constraint that does not satisfy the constraint conditions. Our general solution for EIV model with equality is simpler than other methods in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fang** & **EIC-EIV** \\ \hline \(\mathbf{X_{1}}\) & 2.36823 & 2.36823 \\ \(\mathbf{X_{2}}\) & 5.69850 & 5.69850 \\ \(\mathbf{X_{3}}\) & 6.91215 & 6.91215 \\ \(\mathbf{TS}\mathbf{R}\) & 0.21284 & 0.21284 \\ \(D(\mathbf{X_{1}})\) & 2.91866 & 2.91866 \\ \(D(\mathbf{X_{2}})\) & 5.09582 & 5.09582 \\ \(D(\mathbf{X_{3}})\) & 1.29718 & 1.29718 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adjustment results of equality constraints.
## 3 Automatic Intelligent Optimization
### Automatic Optimization with Intelligent Method
#### 3.3.1 Optimization Results  
Optimization based on the MIGA method was processed to obtain the global optimized scheme. Figure 16 shows the variation of the optimization target with the increase of iteration steps. The influence of the optimization variables on the efficiency of the automotive electronic water pump is complicated. The value of the optimization target fluctuates significantly with the increase of the iteration steps, and there are many schemes that do not satisfy the constraint conditions. As the iteration steps increase, the fluctuation value of the target becomes small. It means the multi-island genetic algorithm gradually locates the optimal solution region after a period of searching. The optimal solution (scheme 3) occurred at 798 iteration steps. All the calculations of the optimization works were done by using a normal desktop computer (Dell 7060 MT) without running in parallel. The optimized hydraulic scheme was obtained within the limited 375 h.  
Table 11 illustrates the comparison of geometric parameters between scheme 2 and scheme 3. As seen from the table, the outlet width of scheme 3 increases, the blade outlet angle and wrap angle both decrease slightly, and the impeller diameter becomes smaller. Figure 17 shows a comparison of the three-dimensional model of the two pump impellers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Numerical Value** \\ \hline Generations & 10 \\ Subpopulation Size & 10 \\ Number of Islands & 10 \\ Crossover rate & 0.9 \\ Migration rate & 0.01 \\ Interval of Migration & 5 \\ Mutation Rate & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimization parameters adopted in MIGA.  
Figure 16: The iteration process of optimization.  
Table 12 shows the head and efficiency of the optimized scheme 3 under three flowrates. It is clear that the head under the design flowrate has significantly improved compared with the original one. The weighted average efficiency under the three flowrates is 55.24%. The maximum efficiency of the pump is increased by 2%, the efficiency of the large flow condition is increased by 3.09%.
## 5 Discussion
### The Consideration of the Diversity of Spatial Distances and Adjacency Relationships  
To comprehensively measure spatial information, this paper proposes two indicators: adjacency strength and visual distance factor. Adjacency strength is a distributed thinking approach to measuring the diversity of adjacency relationships. It represents the proportion of the geometric shape of the current area objects used to establish adjacency relationships with neighboring objects. This approach avoids information redundancy caused by the redundant accumulation of geometric information of the current areal object and fully combines geometric morphological features and spatial proximity to measure the diversity of adjacency relationships. Since the value range of the adjacency strength is between [0, 1], it essentially serves as a weight factor without affecting the dimension of the eigenvalue. Visual distance also exhibits relativity, meaning that the same visual distance has different cognitive effects for different neighboring objects. Therefore, visual distance appears as an intensity factor for adjacency relationships. Spatially closer areal objects are more likely to form a holistic cognition, resulting in a smaller visual distance. In contrast, objects with a greater visual distance are more like two independent objects, requiring separate cognition and thus increasing the intensity factor of visual distance. After transformation using the sigmoid function, the value range of visual distance also falls within [0, 1], without affecting the dimension of the eigenvalue. These two indicators effectively balance the spatial adjacency relationship characteristics between areal objects and play a crucial role in enhancing the accuracy and comprehensiveness of spatial information.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Multi-Scale Feature Fusion Hybrid Convolutional Attention Model(MUSCA)
#### 2.2.3 Multi-Scale Category Weight Loss Function  
Cross-entropy loss used for multi-classification tasks, outputting a probability vector for each sample, represents the model's predicted probability distribution for each category. However, in the case of imbalanced categories, cross-entropy loss may cause bias in accuracy and other indicators, as the model may perform well in most categories and perform poorly in a few categories [17; 18]. This article introduces category weight cross-entropy. The calculation formula for its loss function in Equation (6):  
\[L(y,\hat{y})=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}\ w_{c}\cdot y_{ic}\cdot \log(\hat{y}_{ic}) \tag{6}\]  
Figure 7: Structure diagram of multi-layer feature fusion and multi-layer perceptron.  
Among them, \(N\) is the number of samples, \(c\) is the number of categories, \(y_{ic}\) is the true label (0 or 1) of category c corresponding to sample \(i\), and \(\hat{y}_{ic}\) is the probability predicted by the model that sample \(i\) corresponds to category \(w_{c}\), the sample weight for the \(c\) category.  
In deep learning, binary cross-entropy is used as a loss function in binary classification problems. It measures the difference between the predicted label and the real label of each sample in the binary classification task through the model. The relevant description is in Equation (7).  
\[L(y,\hat{y})=-\frac{1}{N}\sum\limits_{i=1}^{N}\left[y_{i}\text{log}(\hat{y}_{i })+(1-y_{i})\text{log}(1-\hat{y}_{i})\right] \tag{7}\]  
where \(N\) is the number of samples, \(y_{i}\) is the true label of the \(i\) sample, where the value is 0 or 1, and \(\hat{y}_{i}\) is the predicted output of the model for the \(i\) sample, which represents the probability that the sample belongs to the first positive class.  
The loss function used in this article is described in Equation (8):  
\[\begin{array}{l}L(y,g)=\frac{1}{N}\sum\limits_{i=1}^{N}\sum\limits_{m=1}^{M }\left[-y_{mi}\text{log}(\hat{y}_{mi})-(1-y_{mi})\text{log}(1-\hat{y}_{mi}) \right]+\\ L(y,g)=\frac{1}{N}\sum\limits_{i=1}^{N}\sum\limits_{m=1}^{M}\left[-y_{mi}\text{ log}(\hat{y}_{mi})-(1-y_{mi})\text{log}(1-\hat{y}_{mi})\right]+\\ \left(-\frac{1}{N}\sum\limits_{i=1}^{N}\sum\limits_{c=1}^{C}w_{c}\cdot y_{ic} \cdot\text{log}(\hat{y}_{ic})\right)\end{array} \tag{8}\]  
In this context, \(M\) represents the loss across \(m\) scales, with the cumulative binary cross-entropy loss across multiple scales primarily addressing multi-scale task scenarios. This loss function facilitates the model's enhanced adaptability to input data of varying scales, consequently augmenting its generalization capability for multi-scale tasks. Weighted cross-entropy serves the purpose of balancing the sample sizes across diverse categories.
## 2 Literature Review  
### Bridge Inspection and Structural Health Monitoring  
Bridge inspection and structural health monitoring (SHM) have been critical in ensuring bridge safety and extending service life [11; 12]. Traditional inspection methods rely on visual inspections, which are often subjective and prone to human error. In recent years, SHM systems equipped with advanced sensors, such as accelerometers, strain gauges, and displacement transducers, have allowed for the continuous and real-time monitoring of bridge conditions [13; 14; 15; 16]. These systems enable the detection of structural damage, such as cracks, corrosion, or deformation, at an early stage, reducing maintenance costs and preventing catastrophic failures [17; 18].  
Recent studies have emphasized the importance of data-driven approaches in enhancing SHM effectiveness [19; 20]. A notable contribution is the integration of hybrid deep learning models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for analyzing time-series data from various sensors. This method significantly improves the accuracy of anomaly detection and structural degradation predictions,as demonstrated in [21]. By leveraging large datasets, these models can learn complex patterns in the data, thereby providing more reliable assessments of bridge conditions.  
Some studies focus on using the geometric properties and structural attributes of triangle mesh surfaces to capture areas of normal vector discontinuity [22]. On the other hand, many experts also devote themselves to strategies for directly obtaining discontinuous points from point cloud information through geometric attributes [23]. Researchers typically observe changes in the curvature behavior of geometric discontinuity areas on different curved surfaces and deduce the geometric segmentation points of the 3D point cloud data from these changes. Based on this, other scholars have proposed a feature extraction scheme suitable for dense point clouds. The main steps involve using the quadratic surface fitting of local parameters to determine the primary curvatures and directions of the 3D point cloud, and then setting standards for the principal curvatures to select potential edge points, thereby generating feature line segments. In particular, for cases where the model features are uneven or the surface is not smooth, different thresholds can be set, and boundary points can be clustered by analyzing regional information, and then corner points can be identified to obtain feature lines.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### RPL Integration
The figure uses the same scenario and nodes as in Figure 2 in which node 3 uses the heuristic to select a new parent and PHY. First, node 3 receives RPL DIO messages from each node containing the \(x\) score (in the Metric Container of the DIO message) from each originating node to node 0 (i.e., the root node). Normally, these RPL DIO messages are broadcast to all nodes, but this is omitted in order to make the figure clear. When receiving the DIO messages, node 3 determines the RSSI value of each link and subsequently calculates the reliability for each PHY per possible parent as defined in Algorithm 1 (and it is assumed in this example that an option like LINK_STATS_INIT_ETX_FROM_RSSI of the Contiki-NG firmware is used and implemented for each possible PHY). In the next step, node 3 calculates the lowest possible score, \(x_{n}\), using the new reliability values and the bonded slot lengths of the preferred PHY per possible parent, and as such selects a new preferred parent. Finally, node 3 broadcasts this new score to its neighbors. Subsequently, the new score originating from node 3 can influence the parent and PHY choice at the neighboring nodes and the same process can take place at each of the neighbors.
## 3 Error Estimation for the Acoustic Maps  
In this section, we evaluate the mean statistical errors incurred within the present method for predicting the hourly equivalent noise levels of each acoustic map, i.e., for each group of road stretches. For convenience, the six groups will be denoted by \(g=[g_{1},g_{2},g_{3},g_{4},g_{5},g_{6}]\). To be noted is that each group represents a subset of road stretches within Z9 to which we associate the same equivalent noise variation \(\delta_{x}\). In other words, all road stretches show the same dynamic noise behavior described by the group acoustic map. Local information regarding the single road stretch can be obtained according to the procedure discussed in Section 4.  
In order to estimate the total statistical error of each map, represented here by the corresponding variances of the different contributions to the noise variations, we rely on the fact that, for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Range of \(x\)** & **0.0–3.0** & **3.0–3.5** & **3.5–3.9** & **3.9–4.2** & **4.2–4.5** & **4.5–5.2** \\ \hline \(\overline{\beta}_{1}\) & 0.99 & 0.81 & 0.63 & 0.50 & 0.41 & 0.16 \\ \(\overline{\beta}_{2}\) & 0.01 & 0.19 & 0.37 & 0.50 & 0.59 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean values of \(\overline{\beta}_{1}\) and \(\overline{\beta}_{2}\) for the six groups of \(x=\log(\text{T}_{\text{T}})\) within Z9.  
Figure 6: The six groups of road stretches (different colors) for the actual implementation of Dynamap. In the inset we report the intervals of \(x\) values within each group.  
uncorrelated random processes, the total variance of their sum, \(\sigma_{N}^{2}\), is given by the sum of the individual variances. In our case, we have identified four processes whose fluctuations contribute to the total statistical error, \(\sigma_{T}^{2}\), yielding the result  
\[\sigma_{T}^{2}=\ \sigma_{pred}^{2}+\ \sigma_{stat}^{2}+\ \sigma_{comp}^{2}+\ \sigma_{sample}^{2} \tag{5}\]  
corresponding to the intrinsic prediction error of the method, \(\sigma_{pred}^{2}\), the statistical variance of the equivalent noise levels measured by the monitoring stations, \(\sigma_{stat}^{2}\), the different cluster compositions for different time intervals, \(\sigma_{comp}^{2}\), and the variance due to stratified sampling, \(\sigma_{sample}^{2}\). In what follows, we discuss each term in Equation (5) separately.
## 4 Results and Discussion  
The research findings will be delineated across three principal sections. The initial section will elucidate the dispersion of PlanetQ's ROE across both spatial and temporal dimensions. Subsequently, the second section will delineate the disparities between PlanetQ observations and those derived from COSMIC, with a meticulous examination of their statistical significance via appropriate tests. Finally, the third section will scrutinize the disparities vis-a-vis the NWP models, specifically GFS and ECMWF, while rigorously evaluating the statistical significance of these variances.
## 2 MMA Design  
The proposed metamaterial absorber comprises three layers configuration (metal-dielectric-metal), shown in Figure 1. The perspective views of 3 \(\times\) 3 metamaterial absorbers are displayed in Figure 1a. The design parameters are illustrated in Figure 1a,b. The top and bottom layers are Nickel (Ni) metal, and Aluminium Nitride (AlN) is used as the dielectric substrate with a dielectric constant of 8.6 and an electrical loss tangent of 0.0003.  
The AlN material is chosen over other substrate materials due to its attractive high thermal conductivity (200 W/K/m) and since it does not attenuate during high temperatures. Moreover, AlN is a semiconductor with a stable wurtzite (WZ) structure, which is a very significant property for optoelectronic applications in a large spectral region. The unit cell upper layer consists of six concentric hexagonal-shaped ultrathin resonators. As indicated in Figure 1, P = 66 nm is the of the unit cell optimized boundary length. The four equivalent hexagonal rings have side-arm lengths of a1 = 30 nm, a2 = 25 nm, and a3 = 20 nm, a4 = 15 nm, respectively. The widths of the hexagonal shape ring are Wd = 3 nm. In addition, tn, ts and tw represent the bottom ground, middle dielectric substrate and top patch thicknesses, respectively, where the values of tn, ts and tw are 20 nm, 12 nm and 3 nm, individually. The CST Microwave Studio has been used to design and simulated the proposed metamaterial absorber [33]. The unit cell boundary conditions have been applied along the x and y-axis. The negative z-axis is the direction of wave propagation, where open add-space boundary conditions have been defined. The total absorption (A) of the resonators was calculated by using the simplified Equation (1) [34]:  
\[\mathrm{A(\lambda)}=1-\mathrm{T(\lambda)-R(\lambda)} \tag{1}\]  
where A(\(\lambda\)), T(\(\lambda\)) and R(\(\lambda\)) are total absorbances, transmittance and reflectance, respectively. Figure 2 illustrates the simulation result of the MMA, which demonstrates an above 92% absorption rate from a wavelength range of 380-2500 nm, which covers the entire visible and infrared optical window. The equation can also be correlated with the S parameters as \(\mathrm{A=1-\left|S_{11}\right|^{2}-\left|S_{21}\right|^{2}\)[6], where S\({}_{11}\) and S\({}_{21}\) are the reflection and transmission coefficients of the absorber, respectively. Figure 2 presents the proposed design's transmittance, reflectance and absorption curve, where above 90% absorption appeared from 380-2500 nm wavelength with an average absorption of 96.64%.
## 8 Conclusions
### Summary of Key Findings  
Key findings from this review include the following:  
Nanosensors, such as carbon nanotube-based, graphene-based, nanowire-based, and quantum dot-based sensors, offer unique sensing capabilities for bridge maintenance, including strain monitoring, crack detection, corrosion sensing, and environmental monitoring.  
Top-down fabrication techniques, such as lithography-based fabrication and chemical etching methods, provide precise control over sensor dimensions and structures, while bottom-up approaches, such as chemical vapor deposition and molecular self-assembly, enable the growth or assembly of nanosensors from atomic or molecular building blocks.  
Integration strategies for nanosensors in bridge maintenance encompass sensor placement and distribution, connection and communication infrastructure, and power supply considerations. These strategies ensure effective data collection, analysis, and monitoring of bridge structures.  
Challenges and limitations of nanosensor deployment include signal processing and data interpretation, calibration and sensor reliability, as well as environmental factors and durability. Overcoming these challenges requires advanced data analytics, calibration protocols, and robust sensor design.  
Case studies and performance evaluations have demonstrated the effectiveness of nanosensors in bridge monitoring systems and their reliability and accuracy in real-world scenarios. Nanosensors have shown potential in detecting structural anomalies, optimizing maintenance strategies, and improving bridge safety and longevity.
## 3 Experimental Results
### Hysteresis and Backbone Curves  
#### Hysteresis Curves  
Figure 17 shows the hysteresis curves of the CP-2, CP-5, PC-6, RP-8 and HP-9 specimens and the backbone curves of all specimens [28] that are used to evaluate their energy dissipation and lateral load-bearing capacities.  
Figure 17: Hysteresis and backbone curves. (**a**) Hysteresis loops of CP-2 specimen. (**b**) Hysteresis loops of CP-5 specimen. (**c**) Hysteresis loops of PC-6 specimen. (**d**) Hysteresis loops of RP-8 specimen. (**e**) Hysteresis loops of HP-9 specimen. (**f**) Backbone curves of all specimens.  
The energy dissipation capacity of the HP-9 steel specimen was better than the CP and PC concrete specimens, as it had larger hysteresis loops. The CP-5 specimen with larger buried pile depth had a better energy dissipation capacity as compared to the CP-2 and RP-8 specimens. It also found that the RP-8 specimen had a better energy dissipation capacity as compared to the CP-2 specimen due to its larger cross-section that increased the soil-pile interaction.  
As Figure 17f shows, the lateral bearing capacity of the HP-9 specimen was the largest, followed by RP-8 specimen. The maximum bearing capacity of the HP-9 specimen was 54% larger than the RP-8 specimen. The maximum bearing capacity of the CP-5, PC-7 and RP-8 specimens was 7.6%, 27% and 78.7% larger than CP-2 specimen, respectively. The lateral bearing capacity and ductility of piles were improved with the increase of the reinforcement ratio, prestressing level and cross-section size.