esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Flow-based electrochemical detection
### Other electrochemical detection  
The limitation of the batch-injection analysis concept compared to other flow methods is the lack of possibility of on-line analysis. However, some procedures of on-line sample pretreatment in BIA by using of a solid sorbent are possible [52]. This concept is employed in the voltammetric stripping determinations of trace metals using a bed of commercial chelating resin Chelex-100. The electrochemical preconcentration of analytes in the form of amalgams on the surface of mercury thin film electrodes including additional preconcentration on the packed sorbent can be achieved.  
Flow injection systems with the following amperometric detection modes: sampled-DC, reverse pulse amperometry (RPA), and anodic stripping voltammetry (ASV) for determination of copper(II) have been reported [53]. A robust and fast-response flow adaptor for mercury drop electrodes (MDEs) is described. An L-shaped PTFE tube with an internal diameter of 0.5 mm is fixed on the glass capillary of a MDE with a silicone ring at a distance of about 0.5 mm. The flow of sample will be directly injected onto the mercury drop.  
Another automation system based on the flow injection technique with voltammetric detection using electronic tongues has been reported [54]. This system can be used to characterize wastewaters coming from the paper mill industry. A multielectrode array, consisting of platinum, gold, and rhodium electrodes, was used as the working electrode. The measurements were based on large amplitude pulse voltammetry (LAPV), which consisted of scans of pulses from to 0 to 1.8 V in 0.2 V steps. The system can be used to monitor the quality of used waters in the industrial facilities.  
The conductivity detector usually plays a minor role in flow injection analysis. But, an interesting contactless conductivity detector for the determination of inorganic carbon has been developed [55]. The detectors were tested in water samples by the flow injection analysis combined with a gas diffusion separator. A concentration range from 0.01 to 1 mM NaHCO\({}_{3}\) was tested with a linear dynamic range of up to 0.1 mM. Detection limits of 0.6 and 6 uM with RSD values of 4.1 and 4% were obtained for detectors with the electrodes placed axially and across the tube, respectively. A throughput of 15 samples per hour was attained under an experimental procedure used. Another system based on conductometric detection has been developed [56]. The stopped-flow manifold was used to assay and characterize immobilized tannase (EC 3.1.1.20). The immobilized enzyme reactor was inserted inside the tube-type electrode pair for a real-time conductometric measurement. The activities and kinetic parameters for propyl gallate, methyl gallate, and tannic acid were investigated.
## 3 The Proposed System
### Post-Processing Unit  
In our scheme, the difference between the original image \(x_{i}\) and the output \(\hat{x}_{i}\) of the autoencoder can be used as a clue for defect detection. Since our system depends on the difference of the pixel values, we use the naive thresholding approach for detecting defects. However, defects having small differences and defects only having differences in specific channels are difficult to detect by naive thresholding approach. To solve the problem with defects in specific channels, first, we calculate the Euclidean distance \(L(x_{i},\ \hat{x}_{i})\) as in Equation (3) for each pixel. We use logarithmic transformations with a bias for solving problems with defects having small differences. The results of these methods represent the abnormality of a pixel, \(K(x_{i},\ \hat{x}_{i})\) as:  
\[L(x_{i},\ \hat{x}_{i})=\ \sqrt{\sum_{k}\left(x_{i,k}-\hat{x}_{i,k}\right)^{2}} \tag{3}\]  
\[K(x_{i},\ \hat{x}_{i})=log(L(x_{i},\ \hat{x}_{i})+1) \tag{4}\]  
where \(x_{i}\) and \(\hat{x}_{i}\) mean the pixels in the original image and the output of the autoencoder and \(k\) means index of the channel. Additionally, morphology operations are used to remove noise before determining the defected region using thresholding.
## 3 Results and Discussion
Twelve measurements [18] of the threshold pressure taken for vocal tracts of different lengths (Figure 6) are shown in Figure 11a. Since the elastic and damping parameters are the same as in the experiments shown in Figure 9a, one might expect the threshold pressure for \(L_{VT}=19.0\) cm and \(A_{VT}=2.7\) cm\({}^{2}\) to be the same as that for the smallest area of Figure 8a. However, this is not the case. In order to account for the difference, the prephonatory half-width for Figure 11a is adjusted to 0.172 cm to fit the pressure (3061 Pa) at \(L_{VT}=19.0\) cm. This difference in prephonatory half-width threshold pressure may have several causes, including slight differences in how the synthetic vocal folds were manually glued in the mounting device. The SWM calculations presented in Figure 11a produce a very reasonable fit to the trend of the threshold pressure data, but they are not especially accurate for either large or small values of \(L_{VT}\). Frequency measurements with the vocal tract of Figure 6 are shown in Figure 11b. Again, a small adjustment from the parameters of Figure 8 was required. In this case, the  
Figure 10: (Color online) (**a**) Measured and calculated threshold pressures for vocal tracts with different areas for vocal fold model 114. (**b**) Measured and calculated frequencies for vocal tracts with different areas for vocal fold model 114.  
mass of the vocal folds was increased from 0.170 g to 0.184 g in order to decrease the frequency to 129 Hz at \(L_{VT}=19.0\) cm and \(A_{VT}=2.70\) cm. The calculations in Figure 11b reproduce the trends in the data at intermediate and large glottal lengths to a reasonable degree, but do not have the right trend at smaller lengths, where the difference becomes as large as 4% at the smallest length.
## 6 Analysis Results and Verification  
Figure 21a shows the deformation history of the structure during all of the analysis steps. The deformation was purely thermal deformation caused by thermal load, resulting in a range of 1-2 mm during welding, as the downward force of the tool was not considered for analysis. Figure 21b shows the actual deformation in the side frame measured during welding, and the maximum measurement was 1.9 mm. Even though deformation caused by downward force was not considered, the results showed a small margin of errors, and the final deformation after cooling was expected to be within \(\pm\)0.1 mm. Figure 22 shows the final measured and simulated deformation values after cooling for each direction at each point and Table 11 shows the differences between these values.  
Figure 21: _Cont._  
Figure 20: FSW deformation measurement of actual full-frame structural: (**a**) Position of dial indicator; (**b**) measurement direction of deformation.  
Figure 21: FSW deformation: (**a**) Deformation history result; (**b**) deformation measured during welding.  
Figure 22: Cont.  
As shown in Figure 23, residual stresses of around 30 MPa were distributed near the welding center line. This was about 12% of the yield strength of aluminum (250 MPa).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Measured Value** & **Simulated Value** & **Mean Deviation** \\ \hline \multirow{8}{*}{X} & X1 & 0.25 & 0.07 & \multirow{8}{*}{**0.08**} \\  & X2 & 0.15 & 0.07 & \\  & X3 & 0.15 & 0.10 & \\  & X4 & 0.13 & 0.10 & \\  & X5 & 0.08 & 0.02 & \\  & X6 & 0.15 & 0.07 & \\ \hline \multirow{8}{*}{Y} & Y1 & 0.10 & 0.06 & \multirow{8}{*}{**0.05**} \\  & Y2 & 0.11 & 0.08 & \\  & Y3 & 0.20 & 0.07 & \\  & Y4 & 0.18 & 0.10 & \\  & Y5 & 0.05 & 0.05 & \\  & Y6 & 0.06 & 0.05 & \\ \hline \multirow{8}{*}{Z} & Z1 & 0.01 & 0.00 & \multirow{8}{*}{**0.02**} \\  & Z2 & 0.06 & 0.00 & \\  & Z3 & 0.01 & 0.00 & \\  & Z4 & 0.01 & 0.00 & \\  & Z5 & 0.02 & 0.01 & \\  & Z6 & 0.01 & 0.00 & \\  & Z7 & 0.01 & 0.00 & \\  & Z8 & 0.02 & 0.00 & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of variation between measured and experimental values [unit: mm].  
Figure 22: Comparative verification of FSW variant: (**a**) Measured values; (**b**) simulated values.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 6 Conclusions  
This work proposes a two-dimensional discrete data smoothing method based on DFT and zero-padding in order to address the issue of limited test data in engineering practice.  
1. Firstly, the angular spectrum in wavenumber domain is obtained by DFT of the two-dimensional spatial discrete data. Then the angular spectrum is zero-padded and partially adjusted. Finally, IDFT is applied to obtain the smoothing results of two-dimensional discrete data. The proposed method is suitable for even rows or columns of sampling data.
2. In this paper, the 4 \(\times\) 4 two-dimensional discrete random data are smoothed, and the feasibility of the method is verified from the perspectives of numerical value and angular spectrum, respectively. From the numerical point of view, the smoothed two-dimensional discrete data are equal to the original data without changing the value of the data, indicating that the application of smoothing theory can achieve the desired effect. Smoothing results show the feasibility of smoothing theory.
3. This paper also smoothed 16 \(\times\) 8 two-dimensional discrete data twice, which shows that the smoothing theory of two-dimensional discrete data has the function of multiple calculations, and can adjust the data density to the appropriate degree of calculation.  
In fact, the method can be extended to the smoothing of 3D discrete data and other areas related to data processing. Further studies can also focus on the adaptability of zero-padding and its practical application.  
Figure 11: Generated two-dimensional discrete data after second zero-padding.  
**Author Contributions:** Conceptualization, P.Z.; methodology, P.Z. and T.S.; writing--original draft preparation, T.S. and J.X.; writing--review and editing, Y.L., T.L. and L.Z.; funding acquisition, P.Z. and T.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (51805241), Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX22_1048) and Nanjing Institute of Technology Innovation Foundation (CKJB202206) for their strong support.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Currently, the creation of digital copies (digital twins) of various objects by remote sensing methods producing point clouds is becoming commonplace. This might be particularly important for the digital preservation of historical objects. Such clouds are typically primarily acquired as unordered sets of points with regular dense spacing, making the clouds huge in size, which causes such clouds to be difficult to process, store and share. The clouds are, therefore, usually diluted before use, typically through uniform dilution with a set spacing; such dilution can, however, lead to the loss of detail in the resulting cloud (washed-out edges and fine features). In this paper, we present an easy-to-use and computationally inexpensive progressive dilution method preserving detail in highly rugged/curved areas while significantly reducing the number of points in flat areas. This is done on the basis of a newly proposed characteristic \(T\), which is based on the local scattering of the cloud (i.e., on the ruggedness of the local relief). The performance of this algorithm is demonstrated on datasets depicting parts of historic buildings of different characters. The results are evaluated on the basis of (a) root mean square deviation (RMSD) between the original and diluted clouds, (b) of visual evaluation of the differences and (c) of reduction in the point cloud size, demonstrating an excellent performance of the algorithm with a minimum loss of detail while significantly reducing the point clouds (approx. by 47-66% compared to the corresponding uniform dilution for individual datasets).  
point cloud; subsampling; downsampling; dilution; cultural heritage; 3D scanning; SfM photogrammetry; point cloud size; detail 2022
## 4 Hardware Implementation of the RNCS  
In this section, a Relay Node Co-operative System employing SDR platform is presented. A&F and D&F Relay Nodes were implemented, where Frequency Division Duplexing (FDD-LTE), multiple antennas (2 \(\times\) 2 MIMO), and in-band operation--that is, the two links (backhaul and access links) have the same frequencies--are considered. The goal of this paper is to examine the downlink performance of the UE in a real scenario using these architectures.
## 4 Dual-Band Passive Metasurface Based Beam Steering Techniques  
The antenna systems utilizing metasurfaces or metamaterials discussed in the literature can be broadly classified as RAs, TAs, and NFMS. Dual-band beam steering with RAs has mostly been achieved by utilizing reconfigurable RAs or a combination of RAs and phased arrays. On the other hand, the other two techniques use strategically designed metasurfaces to achieve dual-band beam steering capability. Beam steering in TAs is implemented either by feed translation/rotation or by rotating the transmitarrays (metasurfaces). In the case of NFMS systems, two PGMs are independently rotated synchronously or asynchronously parallel to the antenna axis and placed in the near-field of the antenna to achieve beam steering. This section briefly describes these three popular techniques along with state-of-the-art antenna systems. The inherent challenges associated with each approach are also discussed subsequently.
## 3 Results and Discussion  
### Microstructural Parameters of the Alloy Studied  
The alloy studied contained the aluminum-based solid solution as a matrix phase and the secondary particles of the Fe- and Ni-enriched phase (Figure 1a,b), a small fraction of the Mg\({}_{2}\)Si phase owing to the residual Si and the L1\({}_{2}\)-strucutred phase (Figure 1c,d). The coarse Ni- and Fe-bearing particles belonged to the Al\({}_{9}\)FeNi phase of crystallization (eutectic) origin [20,57,58]. A mean size of the coarse particles was 0.8 \(\pm\) 0.1 \(\upmu\)m, and their volume fraction was 8 \(\pm\) 1%. The L1\({}_{2}\)-structured dispersoids belonged to the Al\({}_{3}\)(Sc,Zr) phase which precipitated during thermomechanical treatment [59, 60, 61]. The L1\({}_{2}\) precipitates demonstrated the superlattice reflexes in SAED and the Ashby-Brown contrast owing to the precipitates coherency with the Al matrix (Figure 1c) [59,62,63]. The mean size of the L1\({}_{2}\) precipitates was 11 \(\pm\) 1 nm. Fine and coarse precipitates were near-uniformly distributed in the aluminum matrix. Due to the nanoscale dispersions, the sheets demonstrated a predominantly non-recrystallized grain structure before the start of the superplastic deformation in a temperature range of 420-540 \({}^{\circ}\)C (Figure 2). The TEM study confirmed the formation of subgrains after annealing of the samples at 500 \({}^{\circ}\)C (Figure 1c,d).  
Figure 1: Microstructures for the Al-Mg-Fe-Ni-Zr-Sc alloy studied before the start of the superplastic deformation at 500 \({}^{\circ}\)C; (**a**) SEM image (**b**) SEM-EDS element distribution maps for the alloying elements (**c**,**d**) TEM images; insert: SAED for (**c**) image.  
Figure 2: Grain structure of the investigated Al-Mg-Fe-Ni-Zr-Sc alloy (**a**,**b**) before the start of the superplastic deformation at (**a**) 420 \({}^{\circ}\)C, (**b**) 540 \({}^{\circ}\)C, and (**c**,**d**) after the superplastic deformation (logarithmic strain of 1.5) at a temperature of 460 \({}^{\circ}\)C with strain rates of (**c**) 0.008 and (**d**) 0.06 s\({}^{-1}\).  
The recrystallized grains were formed during the superplastic deformation. The grain structures after the strain of 1.5 at a temperature of 460 \({}^{\circ}\)C with strain rates of 0.008 and 0.06 s\({}^{-1}\) are presented in Figure 2c,d. The mean grain size of 4.6 \(\pm\) 0.5 and 3.5 \(\pm\) 0.4 \(\upmu\)m at 0.008 and 0.06 s\({}^{-1}\) was formed respectively; therefore, a higher strain rate of superplastic deformation provided a finer grain structure.
## 2 Materials and Methods
### Data Reduction Equations
#### 2.2.3 Uncertainty Analysis  
The calculation of the uncertainty analysis was based on the statistical methods (standard deviation) introduced by [PERSON], 2006 [38]. The representative values of uncertainty analysis included the individual uncertainties of the measured values.  
Uncertainty of Humidity Ratio (\(X\))  
The humidity ratio was given by  
\[X=0.62198\frac{RH.P_{s}}{P-RH.P_{s}} \tag{9}\]  
The uncertainty analysis was expressed as  
\[u^{2}(X)=\left(\frac{\partial X}{\partial RH}\right)^{2}\!\!u^{2}(RH)+\left( \frac{\partial X}{\partial P}\right)^{2}\!\!u^{2}(P)+\left(\frac{\partial X} {\partial P_{s}}\right)^{2}\!\!u^{2}(P_{s}) \tag{10}\]  
where \(RH\) is the relative humidity of the air, \(P\) is the atmospheric pressure, and \(P_{s}\) is the saturation water vapor pressure.  
Uncertainty of Dehumidification Index (\(D_{i}\))  
The dehumidification index was calculated by Equations (2) and (3). Therefore, the uncertainty analysis for the dehumidification index was  
\[u^{2}(D_{i})=\left(\frac{\partial D_{i}}{\partial X_{in}}\right)^{2}\!\!u^{2 }(X_{in})+\left(\frac{\partial D_{i}}{\partial X_{out}}\right)^{2}\!\!u^{2}(X _{out}) \tag{11}\]  
Uncertainty of Latent Effectiveness (\(\varepsilon\))  
The latent effectiveness uncertainty evaluation, based on the parameters in Equation (5), was calculated as  
\[u^{2}(\varepsilon)=\left(\frac{\partial\varepsilon}{\partial X_{ads,\ in}} \right)^{2}\!\!u^{2}\!\left(X_{ads,\ in}\right)+\left(\frac{\partial \varepsilon}{\partial X_{ads,\ out}}\right)^{2}\!\!u^{2}\!\left(X_{ads,\ out}\right)+\left(\frac{\partial \varepsilon}{\partial X_{ideal}}\right)^{2}\!\!u^{2}(X_{ideal}) \tag{12}\]  
Uncertainty of Latent Heat Ratio (\(LHR\))  
For latent heat ratio, rearranging Equations (6)-(8) formed  
\[LHR\ =\!\!\frac{Q_{lat}}{Q_{lat}\!\!+\!Q_{sen}} \tag{13}\]\[=\frac{\dot{m}_{a}(h_{in}-h_{out})}{\dot{m}_{a}(h_{in}-h_{out})+\dot{m} _{a}c_{p}\Big{(}T_{des,in}-T_{des,out}\Big{)}} \tag{14}\] \[=\frac{(h_{in}-h_{out})}{(h_{in}-h_{out})+c_{p}\Big{(}T_{des,in}-T_ {des,out}\Big{)}} \tag{15}\]  
The uncertainty of \(LHR\) was  
\[\begin{split} u^{2}(LHR)=&\Big{(}\frac{\partial LHR }{\partial l_{in}}\Big{)}^{2}u^{2}(h_{in})+\Big{(}\frac{\partial LHR}{\partial l _{out}}\Big{)}^{2}u^{2}(h_{out})+\Big{(}\frac{\partial LHR}{\partial c_{p}} \Big{)}^{2}u^{2}\big{(}c_{p}\Big{)}\\ +&\Big{(}\frac{\partial LHR}{\partial l_{des,in}} \Big{)}^{2}u^{2}\Big{(}T_{des,in}\Big{)}+\Big{(}\frac{\partial LHR}{\partial l _{des,out}}\Big{)}^{2}u^{2}\big{(}T_{des,out}\Big{)}\end{split} \tag{16}\]  
Uncertainty of Reynolds number (\(Re\))  
Reynolds number uncertainty was given as  
\[u^{2}(Re)=\left(\frac{\partial Re}{\partial\rho}\right)^{2}u^{2}(\rho)+\left( \frac{\partial Re}{\partial U_{a}}\right)^{2}u^{2}(U_{a})+\left(\frac{\partial Re }{\partial D_{h}}\right)^{2}u^{2}(D_{h})+\left(\frac{\partial Re}{\partial \mu}\right)^{2}u^{2}(\mu) \tag{17}\]  
Table 3 collects the uncertainty values for the humidity ratio, Reynolds number (\(Re\)), dehumidification index, latent effectiveness, and latent heat ratio.
###### Abstract  
Integrated Access and Backhaul (IAB) is an enabling technology for efficient 5G millimeter wave (mmWave) New Radio (NR) deployment. The key feature of IAB is multi-hop wireless backhauling, allowing utilizing relaying IAB-nodes to provide cost-efficient access network densification and alleviate the problem of blockages. One of the critical performance measures in such systems is the latency of packet delivery over the multi-hop paths. The paper aims at assessing the impact of multi-hop transmission on the end-to-end delay in an IAB radio access network, taking into account the half-duplex constraint. We build a detailed queuing theory model for latency assessment in time-division-multiplexing (TDM)-based IAB deployments and evaluate the delay due to queuing in the network nodes for several cell topologies and under different time allocation strategies between access and backhaul. The paper considers a practical Manhattan-style urban deployment, which is characteristically impaired by the blockage of buildings. The numerical results show that balancing the access and backhaul micro phases is crucial for reducing the end-to-end packet delay, at least in the uplink, while increasing the number of network hops yields a linear increase in the total packet delay for both the uplink and downlink. The numerical results were obtained via simulation using the open-source software OMNeT++.  
5G; New Radio; millimeter wave; Integrated Access and Backhaul; latency +
Footnote †: journal:  
_Future Internet_**2022**, _14_, 345. [[https://doi.org/10.3390/fi4110345](https://doi.org/10.3390/fi4110345)]([https://doi.org/10.3390/fi4110345](https://doi.org/10.3390/fi4110345))
## 4 Calibration and Characterization
### Radiometric Stability  
Once the calibration is performed, the radiometric stability of the receiving chain has been studied. The radiometric stability is the parameter that provides information about how the calibration parameters will drift with time. The radiometric stability depends on several parameters such as temperature drifts, or instability of amplifiers gain, among others [36].  
The radiometric stability is calculated as the Allan variance of the retrieved power for each band and polarization [36]. Moreover, it also determines the optimum integration time to achieve the best radiometric sensitivity.  
The Allan deviation measurements (square root of the variance) for all the bands are shown in Figure 12. In addition, the optimum integration time, and its corresponding radiometric resolution are obtained as the minimum of the Allan deviation. These results are shown in Table 3.  
Results show that radiometric stability depends on the band under assessment. However, as a general trend, it can be observed that radiometric stability is higher for lower frequency bands since they have larger optimum integration times and lower Allan deviation values.  
Figure 12: Radiometric stability obtained as the Allan variance for each frequency band and polarization (H **left** and V **right**). Filled markers represent the minimum value for each case, i.e., the optimal integration time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Horizontal Polarization**} & \multicolumn{2}{c}{**Vertical Polarization**} \\ \hline
**Frequency** & **Integration** & **Allan** & **Integration** & **Allan** \\
**Band** & **Time (s)** & **Deviation** (\(\times 10^{-3}\)) & **Time (s)** & **Deviation** (\(\times 10^{-3}\)) \\ \hline L & 193 & 1.73 & 142 & 1.44 \\ S & 35 & 2.77 & 49 & 2.30 \\ C & 55 & 2.49 & 16 & 3.83 \\ X & 89 & 1.93 & 34 & 3.18 \\ K\({}^{\prime}\) & 29 & 3.62 & 13 & 4.89 \\ K\({}^{\prime\prime}\) & 55 & 2.48 & 77 & 2.55 \\ Ka & 50 & 2.79 & 6 & 6.29 \\ W & 13 & 7.14 & 17 & 4.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal integration time and Allan deviation for each the frequency band and polarization.