esa-sceva/satcom-chunk-collection huggingface dataset:
# Traceability of the Micro Scale Pipe Viscometer for Traceable Calibration of Dynamic Viscosity
## 4 Conclusions  
Extensive investigations for the characterization of the pipe viscometer for the traceable inline measurement of dynamic viscosity for flowrates from 1 \(\upmu\)L/min to 150 mL/min and pressure drops up to 10 bar were presented. The glass microtube is the best option and was therefore integrated in the pipe viscometer, leading to the extended uncertainty of the pipe viscometer being 0.90%. The stated measurement uncertainty was validated with measurements of three traceable reference liquids for the dynamic viscosity, where the deviations were either (0.43 \(\pm\) 0.90)% or (0.42 \(\pm\) 0.90)%. Comparison with other pipe viscometers and glass capillary viscosity meters showed consistent results with the reference values within the stated uncertainties, and more details can be found in Reference [2]. Additionally, the glass microtube was characterized by utilizing the \(\upmu\)-CT facility at METAS. These measurements also showed consistent results for the inner diameter of (0.1269 \(\pm\) 0.0010) mm compared to the flow calibration method, where an inner diameter of (0.1267 \(\pm\) 0.0003) mm was determined. These consistent results validate both methods for determining the inner diameter of the glass microtube and the stated measurement uncertainty of the pipe viscometer was validated by the consistent results of the measurements of the dynamic viscosity of the various liquids reported in this paper.  
The determination of the dynamic viscosity with the linear fit method over several measurement data or the single-point method with a single measurement is possible as  
\begin{table}
\begin{tabular}{c c c} \hline
**Facility** & **Liquid C** & **Liquid E** \\ \hline METAS & 1.810 \(\pm\) 0.017 & 1.110 \(\pm\) 0.010 \\ RISE & 1.800 \(\pm\) 0.037 & 1.112 \(\pm\) 0.023 \\ NEL & 1.795 \(\pm\) 0.018 & 1.123 \(\pm\) 0.012 \\ NQIS/EIM & 1.823 \(\pm\) 0.019 & 1.119 \(\pm\) 0.012 \\ KRISS & 1.803 \(\pm\) 0.036 & 1.107 \(\pm\) 0.023 \\ Reference value & 1.809 \(\pm\) 0.009 & 1.116 \(\pm\) 0.006 \\ \hline \end{tabular}
\end{table}
Table 10: Dynamic viscosity of the liquids measured by means of pipe viscometers and glass capillary viscometers at temperature of 22 \({}^{\circ}\)C [2; 23].  
long as the pressure drop is not too small compared to the resolution of the pressure sensors installed.  
Conceptualization, H.B.; methodology, H.B. and B.A.B.; validation, B.A.B., S.N. and H.B.; formal analysis, B.A.B. and S.N.; investigation, B.A.B., S.N. and H.B.; writing--original draft preparation, H.B.; writing--review and editing, H.B., B.A.B. and M.d.H.; supervision, M.d.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. Most of the relevant data are already presented in the tables.  
The authors declare no conflict of interest.
## 3 Proposed Methods
### Defect Detection and Localization
The entire preceding discussion can be summarized the following Algorithm 1:  
```
Input: A defective image \(I_{org}\), well-trained CovNets submodules \(En_{1}\), \(De\) and \(En_{1}\). Output: The final segmentation result \(I_{result}\)  Split the input image \(I_{org}\) into a batch of image patches without overlapping area which can be denoted as \(I_{org}^{r,c}\), \(r=(1,2,\;\ldots,M/m)\) and \(c=(1,2,\;\ldots,N/n)\). Obtain the reconstructed image patches \(I_{rec}^{r,c}\) and two corresponding feature vectors \(f_{i}^{r,c}\) and \(f_{o}^{r,c}\). Rearrange \(I_{rec}^{r,c}\) into a full-sized reconstructed image \(I_{rec}\) and obtain the residual image \(I_{residual}\) through Equation (5). Compare the similarity value between \(f_{i}^{r,c}\) and \(f_{o}^{r,c}\) to generate the mask image patches \(I_{mask}^{r,c}\). Rearrange \(I_{mask}^{r,c}\) and obtain a full-sized mask image \(I_{mask}\) with a threshold value \(\epsilon\) as shown in Equation (6). Synthesize \(I_{residual}\) and \(I_{mask}\) to obtain the final segmentation image \(I_{result}\) through Equation (7). ```  
**Algorithm 1** The defect detection and segmentation.
## 6 Uncertainties Associated with Distances Derived from Point Clouds
### Summary: Uncertainties Associated with Distances Due to the Influence Factors  
In the sections above, we have considered the uncertainty contributions to distances due to a number of effects. In this section we summarise these results, giving typical uncertainty contributions in terms of a small number of statistical parameters.  
_Random effects (\(R\))._ Statistical parameter \(\sigma_{R}\).  
\[u_{R}^{2}(d_{ij})=2\sigma_{R}^{2}.\]  
_Probe qualification effects (\(PQ\))._ Statistical parameters \(\sigma_{PQ}\) representing the maximum probe qualification uncertainty. For distance measurements using the same probe, the contribution is zero. Otherwise,  
\[u_{PQ}^{2}(d_{ij})\leq 2\sigma_{PQ}^{2}.\]  
_Scale and squareness effects (\(S\))._ Statistical parameters \(\sigma_{S}\), \(\sigma_{S,a}\) and \(\sigma_{Q}\). For this model, the uncertainty in distance is approximated by \[u_{S}^{2}(d_{ij})\approx\Big{(}\sigma_{S}^{2}+\sigma_{S,a}^{2}+\sigma_{Q}^{2}\Big{)} d_{ij}^{2}.\]  
If the measurements are aligned with an axis, the squareness component, represented by \(\sigma_{Q}\), makes no contribution.  
_Spatially correlated location effects (ET)._ Statistical parameters \(\sigma_{ET}\) and \(\lambda_{ET}\).  
\[u_{ET}^{2}(d_{ij})=2\sigma_{ET}^{2}\Big{(}1-e^{-d_{ij}^{2}/\lambda_{ET}^{2}} \Big{)}\leq 2\sigma_{ET}^{2}.\]  
_Spatially correlated rotation effects (ER)._ Statistical parameters \(\sigma_{ER}\), \(\lambda_{ER}\) and the maximum probe length \(P\).  
\[u_{ER}^{2}(d_{ij})\leq 2\sigma_{ER}^{2}P^{2}\Big{(}1-e^{-d_{ij}^{2}/\lambda_{ ER}^{2}}\Big{)}\leq 2\sigma_{ER}^{2}P^{2}.\]  
_Spatially correlated probing effects (P)._ Statistical parameters \(\sigma_{P_{0}}\), \(\sigma_{P}\), and \(\lambda_{P}\):  
\[u_{P}^{2}(d_{ij})\leq 4\sigma_{P_{0}}^{2}+2\sigma_{P}^{2}.\]  
Putting these formulae together, we derive a summary estimate of the uncertainty \(u(d)\) associated with length measurement given by  
\[u^{2}(d)=\ \sigma_{A}^{2}+\Big{(}\sigma_{S}^{2}+\sigma_{S,a}^{2}+\sigma_{Q}^{2} \Big{)}d^{2}\ +2\sigma_{ET}^{2}\Big{(}1-e^{-d^{2}/\lambda_{ET}^{2}}\Big{)}+2\sigma_{ER}^{2} P^{2}\Big{(}1-e^{-d^{2}/\lambda_{ER}^{2}}\Big{)}, \tag{33}\]  
with  
\[\sigma_{A}^{2}=2\Big{(}\sigma_{R}^{2}+\sigma_{PQ}^{2}+\sigma_{P}^{2}+2\sigma_ {P_{0}}^{2}\Big{)}. \tag{34}\]  
In Equation (33), it is seen that \(u(d)\) is the sum of four components, the first independent of distance, the second directly proportional to distance, while the other two terms depend on the ratio of distance relative to a spatial correlation length.
# Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning
## 3 Results
### State-of-the-Art Comparison  
To validate the efficacy of our proposed model, we compared it with established fine-grained classification approaches, encompassing both strongly supervised (HSnet [38], Mask-CNN [39]) and weakly supervised (RA-CNN [40], ADKD [41]) techniques.  
We evaluated the models' performance on a benchmark dataset. As shown in Table 2, our model achieves a superior accuracy of 91.3%, surpassing HSnet, Mask-CNN, RA-CNN, and ADKD_T by 3.8%, 4.0%, 5.9%, and 1.8%, respectively. This demonstrates the effectiveness of our multi-scale feature fusion, attentional feature enhancement, and contrastive learning strategies in bird classification.  
Beyond classification accuracy, we compared model size and execution time. Our model utilizes a ShuffleNetV2 backbone, resulting in a compact size of approximately 193 MB (Table 3). This is significantly smaller than the compared models: 20% of HSnet,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Accuracy (\%)** & **F1 Score** \\ \hline Proposed Model (All Modules) & 91.3 & 90.6 \\ w/o multi-scale feature fusion & 87.4 & 87.1 \\ w/o feature enhancement & 85.2 & 84.8 \\ w/o siamese network training & 86.9 & 86.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiments. We evaluated four model variants: (1) baseline, which includes all three modules; (2) without multi-scale feature fusion; (3) without feature enhancement; and (4) without siamese network training. For each variant, we report the accuracy and F1 score on the test set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Backbone Network** & **Accuracy (\%)** \\ \hline HSnet [31] & GoogLeNet & 87.5 \\ Mask-CNN [36] & VGG-16 & 87.3 \\ RA-CNN [40] & VGG-19 & 85.4 \\ ADKD\_T [41] & Densenet121 & 89.5 \\ FS-NET(Proposed Model) & ShuffleNetV2 & 91.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the accuracy results of different models on the CUB-200-2011 dataset.  
\(11\%\) of Mask-CNN, 1.8% of RA-CNN, and 74% of ADKD_T. Furthermore, our model exhibits faster execution times compared to all benchmarks. It achieves speedups of 7\(\times\), 14\(\times\), 8\(\times\), and 4.8\(\times\) against HSnet, Mask-CNN, RA-CNN, and ADKD_T, respectively. This efficiency makes our model suitable for real-time applications.  
We further investigated the proposed method's performance under conditions with limited training data. The experimental results depicted in Figure 4 demonstrate a clear advantage for our model. When the training data is scarce (only 5.0%), our method achieves a recognition accuracy of 65.2%, which is about 10% higher than the second-best method. This advantage persists even as the training data volume increases. With 25.0% training data, our model still maintains a lead of approximately 5% over the second-best performer.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 4 The Durability Prediction Calculation Methods for Square Rebar Components
### The Time from Corrosion Initiation to Concrete Cover Cracking  
The time from corrosion initiation to concrete cover cracking depends on the critical corrosion depth of the reinforcement and the corrosion rate. The calculation equations of GB/T 51,355 are as follows:  
\[t_{pre}=t_{i}+t_{cr} \tag{7}\]  
\[t_{\sigma}=\frac{\delta_{cr}}{\lambda} \tag{8}\]  
\[\lambda=5.92K_{cl}(0.75+0.0125T)(RH-0.50)^{2/3}c^{-0.675}f_{cv,\epsilon}{}^{- 1.8} \tag{9}\]  
\[\delta_{cr}=0.012c/d+0.00084f_{cv,\epsilon}+0.018 \tag{10}\]  
where \(t_{pre}\) is the durability prediction life (years); \(t_{cr}\) is the time from corrosion initiation to concrete cover cracking (year); \(\delta_{cr}\) is the critical corrosion depth of rebar (mm); \(\lambda\) is the corrosion rate of rebar (mm/year); \(K_{cl}\) is the rebar location coefficient, and \(d\) is the diameter of the round rebar section.  
Equation (7) defines the durability prediction life of one component, while Equation (8) defines the time from corrosion initiation to concrete cover cracking as the critical corrosion depth of rebar divided by the corrosion rate of rebar. Equation (9) expresses that the corrosion rate of rebar is related to the environmental factors and concrete compressive strength, rather than the rebar itself. Only Equation (10) is directly affected by the rebar type in the calculation process. This is because the corrosion-induced cracking models are different for round rebar and square rebar [25]. Thus, Equations (7)-(9) can be kept for square rebar case, while only Equation (10) should be replaced by a dedicated study.
## 3 Design and Development of the Measuring System
### Software Design and Data Algorithm
#### 3.3.4 Data Analysis Module  
With respect to the foregoing introduction of the well-manufactured calibration platform with high precision, it is assumed that the platform is standard and has two parallel and base planes in this work. As a consequence, data analysis could be carried out. Firstly, the displacement data obtained using the sensors are from multiple points, therefore these data need to be unified into a coordinate system during processing. After measurement, the distances between the sensors and the measured metal surfaces could be acquired. And the distance between the sensors and the standard plates of the platform have been obtained during the calibration process.  
Set a coordinate system to obtain the three-dimensional coordinates of the measuring points of each sensor group. For example, set the lower surface of the calibration platform as the z-axis origin, and the no. 6 sensor group (at the center of the plate) as the x-axis and y-axis origin. Then, calculate the flatness of the measured surfaces. Quite a few theories are suitable, in which the least square method [31, 32] is commonly used. Based on the three-dimensional coordinates of each measuring point, firstly solve the linear equation using the matrix method and get an ideal plane equation; and, finally, calculate the distance between each measuring point and the ideal plane. The maximum distance would be defined as the flatness of the measured surface.
# Black Loaded Columns--State of the Art Review
## 3 Numerical Modeling
The edges in cross-sections at small-scaled distances play a significant role in pressure retention, while with increasing scaled distance, the shape influence decreases. An increase in reinforcement in cross-section affects the improvement of the behavior of the columns during the blast load, especially in shapes that retain higher pressures. [PERSON] et al. [75] numerically simulated polymeric foam retrofitted RC columns (35.5 cm \(\times\) 35.5 cm \(\times\) 348 cm) in Abaqus. They used a 10 mm mesh for reinforcement and 20 mm for concrete. Columns coated with 5 mm, 8 mm, and 10 mm thick foam were exposed to 10, 25, and 50 kg of TNT. The greatest reduction in displacement in the middle of the column was obtained under 10 kg of TNT when the column was coated with 10 mm thick foam. [PERSON] et al. [76] compare in LS-Dyna the behavior of a segmental CFST column with monolithic and prestressed monolithic columns. The columns were exposed to 20 and 50 kg of TNT at a standoff distance of 1.5 m. The segmental column showed a smaller residual displacement, and numerical analysis proved that a larger number of segments had a more favorable effect on the behavior of the column. Moreover, increasing the steel thickness had a beneficial effect on reducing concrete damage. [PERSON] et al. [78] and [PERSON] et al. [79] used LS-Dyna for numerical simulations of RC columns retrofitted with CFRP subjected to the close-in explosion. CFRP sheets reduce the deformation and spalling of concrete. The CFRP thickness, wrapping, and dimensions ratio of the charge also had a large impact on the damage of the column and peak pressure. Debonding of CFRP is the most common form of failure, but despite this, CFRP has a role in reducing column damage during the blast load, and it may even have a role in changing the failure mode from shear to flexural deformation. The setting of the CFRP on both sides of the column needs to be further investigated because direct shear is possible due to over-reinforcement.
## 3 Methodology
### Array Layout  
As the ESPAC method was adopted in this experiment, the requirements for array layout were more flexible. However, if the energy of the natural source surface wave was equal in all directions, a linear arrangement could be adopted, which is easier to implement in field work [18]. At the same time, the survey line was located around the village. If the array was arranged in two dimensions, the experimenters needed to enter the village to place instruments, and there are a lot of dangerous buildings in the village. Considering the actual terrain conditions and the safety of the technicians, a linear array was selected for this experiment.  
According to the data provided by the mining party, the burial depth of Coal Seam #2 is roughly 410\(\sim\)420 m, so the exploration effect could be guaranteed if the exploration depth was 500 m. If the exploration depth is \(H\), the number of each array is \(n\) and the spacing between geophones is \(d\), the following formula should be met:  
\[n=[H/(1.5\sim 2.5)d]+1 \tag{8}\]  
We used 11 collection stations in total, so \(n\) is 11. In (8), we can deduce that the geophone spacing \(d\) should be set between approximately 20 and 33 m.  
Due to the small number of acquisition stations in this experiment, in order to maintain a high detection resolution on the basis of meeting the exploration depth, the spacing between the acquisition stations was set as 20 m. The acquisition station could not cover the whole measuring line at one time, so the linear rolling acquisition method was adopted in this experiment [20]. This made use of the characteristics of the linear array and reused the data of the acquisition station on the linear array. It used eight acquisition stations to survey two micro-points each time. After one acquisition, it moved the two stations at the back of the line to the front for the rolling operation, effectively improving the working efficiency by more than double. On this basis, Hefei Guowei Electronics Co., Ltd., Hefei, China, in collaboration with the Seismological Research Institute of the China Seismological Bureau, further improved and perfected the acquisition method. By laying 20 acquisition stations in a straight line and surveying more than 10 micro-points at the same time, the data were reused to a greater extent, and the exploration efficiency was further improved to more than 10 times the original.
## 4 Time Continuous Compactness Based _Rwta_
### Time Continuous Compactness (TCC)  
We define \(C_{\varphi}^{pw}\) as the TCC of wavelength \(w\) on path \(p\) in \([t_{s},t_{e}]\) in \(\varphi\) state, as shown in Equation (1). \(\varphi\) denotes the state of network, \(K_{\varphi}^{pw}\) indicates the number of available timeslot segments at wavelength \(w\) on path \(p\) in \(\varphi\) state, and \(N_{\varphi}^{pwj}\) represents the number of timeslots on available timeslot segment \(j\) at wavelength \(w\) on path \(p\) in \(\varphi\) state. From the definition, we can see that the larger \(C_{\varphi}^{pw}\) is, the better connectivity of wavelength-timeslots on the path will be.  
\[C_{\varphi}^{pw}=\ \frac{\sum_{j=1}^{K_{\varphi}^{pw}}N_{\varphi}^{pwj}}{(t_{e }-t_{s})+1}\frac{1}{K_{\varphi}^{pw}} \tag{1}\]  
Let \(W\) denote the set of wavelengths on path \(p\). In \(\varphi\) state, TCC of path \(p\) in period \([t_{s},t_{e}]\) is represented by \(C_{\varphi}^{p}\), which is the sum of TCC of all wavelengths on \(p\), as defined in Equation (2). Small \(C_{\varphi}^{p}\) means that the path has poor wavelength-timeslots connectivity during this period, possibly due to heavy traffic carried on the path or large number of wavelength-timeslot fragments on the path. Under such situation, if new service requests arrive on path \(p\), the probability of being blocked will be relatively high.  
\[C_{\varphi}^{p}=\ \sum_{w\in W}C_{\varphi}^{wp} \tag{2}\]  
Figure 4 gives an illustrating example of TCC calculation. In the figure, the grids with color represent the occupancy of timeslots on path \(p\) in \(\varphi\) state, and the grids without color indicate that they are available. Timeslots enclosed in dotted rectangles indicate that they are connective on the path. By calculating TCC of two wavelengths in the timeslot segment, TCC of \(w1\) is larger than that of \(w2\). Obviously, the connectivity of timeslots for \(w1\) is better than \(w2\) on path \(p\).
# I-Shaped Metamaterial Using SRR for Multi-Band Wireless Communication
###### Abstract  
A novel I-shaped metamaterial (ISMeTM) using split-ring resonator (SRR) for multi-band wireless communication is presented in this paper. The proposed ISMeTM unit cell structure is designed using the three-square split-ring resonators (SSRRs) and I-shaped copper strip at the center. The size of the proposed ISMeTM is 10 \(\times\) 10 \(\times\) 1.6 mm\({}^{3}\) while utilizing the FR-4 dielectric substrate material. The analysis of various array arrangements, variation in the ring gap, variation in strip length, and the variation in strip width is performed to achieve the optimum results for multi-band operation. The effective permittivity, permeability, and refractive index of the unit cell have been analyzed. The design and simulation of the ISMeTM unit cell and arrays are performed using the Computer Simulation Technology (CST) Studio Suite and MATLAB. The equivalent circuit of the ISMeTM is designed using the Advanced Design System (ADS) software. The split ring's inner loop's gap functions as a capacitor, while the metallic ring itself functions as an inductor. Electric resonance is created by the interaction between the split ring and the electric field. The interaction of magnetic fields with metallic loops during EM propagation in the structure causes the magnetic resonance. The variation in dimensions of the structure causes the variation in the inductance and capacitance, which causes the variation in resonant frequency. The proposed design is optimized after several parametric analyses. A comprehensive analysis of 1 \(\times\) 2, 2 \(\times\) 2, and 2 \(\times\) 4 array is also investigated. The results confirm the multi-band operation of the proposed ISMeTM. The proposed ISMeTM is suitable for the multi-band C/X/Ku-band microwave applications.  
metamaterial; SSRR; permittivity; permeability; multi-band; C-band; X-band; Ku-band +
Footnote †: journal: _crystals_