esa-sceva/satcom-chunk-collection huggingface dataset:
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 2 Background
### Generalized Sidelobe Cancel  
Generalized sidelobe canceler (GSC) belongs to adaptive beamforming, which can be expressed as the following constraint conditions. GSC can maintain the desired signal and suppress the interference and noise signal.  
\[\mathrm{min}\omega^{\mathrm{H}}\mathrm{R}\omega,\text{ subject to }\omega^{ \mathrm{H}}\mathbf{a}=1 \tag{4}\]  
where \(\mathbf{a}\) is the steering vector, which represents the direction of desired signal. For both transmitting and receiving the focused echo signal, \(\mathbf{a}\) is a unit vector. \(\mathbf{R}\) is the interference and noise covariance matrix. The superscript \(\{\cdot\}^{\mathrm{H}}\) denotes the conjugate transpose. Adaptive beamforming GSC separates the linear constraints with an adaptive filter. Therefore, the constrained optimization problem of formula (4) is converted into an unconstrained optimization problem. The apodization weights after GSC can be expressed as [17]:  
\[\mathbf{\omega}=\mathbf{\omega_{q}}-\mathbf{B}\mathbf{\omega_{a}} \tag{5}\]  
\(\mathbf{\omega_{q}}\) and \(\mathbf{\omega_{a}}\) are temporal non-adaptive weight vector and adaptive weight vector, respectively. \(\mathbf{\omega_{q}}\) is determined by received echo signals, \(\mathbf{\omega_{q}}=(\mathbf{a}\mathbf{a}^{\mathrm{H}})^{-1}\mathbf{a}\). \(\mathbf{\omega_{a}}\)  
where \(2\text{K}+1\) represents the number of echo signal used to construct the \(\overset{\wedge}{\mathbf{R}}\). \(\mathbf{X}(\mathbf{k})=[\mathbf{LRI}_{1}(\mathbf{k}),\ \mathbf{LRI}_{2}( \mathbf{k}),\ \cdots,\ \mathbf{LRI}_{N}(\mathbf{k})]^{T}\), which means the time-delayed echo signal. The final echo imaging after GSC can be expressed as:  
\[\mathbf{y}(\mathbf{k})=\boldsymbol{\omega}^{\text{H}}(\mathbf{k})\mathbf{X}( \mathbf{k}) \tag{7}\]
# Study on Preparation and Rheological Properties of 3D Printed Pre-Foaming Concrete
## 1 Introduction
also prepared cement-based three-dimensional printable lightweight foam concrete using protein surfactants, and the resulting 3 DP-LWFC had higher compressive strength than C-LWFC [21]. The mechanical strength of 3 DP-LWFC increased with increasing dry density, which is similar to the C-LWFC. However, traditional foam concrete is difficult to use to meet the printing requirements, which are mainly manifested as higher fluidity and lower yield stress. Previous studies have shown that the rheological properties (yield stress and apparent viscosity) of foamed concrete are closely related to water incorporation [22]. In this experiment, the yield stress of the material is effectively increased by reducing the water-cement ratio. Additionally, [PERSON] et al. provide clear insight into the effects of surfactant on the yield stress of OPC pastes [23]. They showed that the anionic surfactant that becomes adsorbed to the cement grain surface, depending on the concentration, can either increase or reduce the paste's yield stress; i.e., a lower concentration increases the yield stress whereas a higher concentration decreases the yield stress. Therefore, it is bold to guess that different foam content is also the main factor affecting the rheological properties of the \"printing ink\", thereby affecting the printing performance of the material. The development of 3D printed pre-foaming concrete for construction is still at a relatively early stage. Not enough has been reported about the relationship between the composition, structure and properties of this lightweight printed mortar. The purpose of this paper is to investigate the essential properties of 3 DFCs, including rheology, buildability, mechanical strength and thermal conductivity. An in-depth understanding of these problems is conducive to the practical application of lightweight thermal insulation mortar in digital construction.
## 4 Results and Discussion
### Results Comparison and Analysis  
#### 4.2.1 Evaluation Metrics  
In this study, four evaluation metrics, namely, \(MAE\), \(MAPE\), \(RMSE\) and \(R^{2}\), are adopted to measure the model's performance. In addition, we evaluate the model's  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Ranking** & **Feature** & **Ranking** \\ \hline ESAL & 1 & AC\_THICKNESS & 8 \\ TEMP & 2 & HUMIDITY & 9 \\ FREEZE\_THAW & 3 & DRAINAAGE\_TYPE & 10 \\ FREEZE\_INDEX & 4 & MAINTENANCE & 11 \\ CLOUD\_COVER & 5 & WIND & 12 \\ GB\_THICKNESS & 6 & SUBGRADE\_MATL & 13 \\ PRECIPITATION & 7 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of feature selection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Time Taken** & **Memory Used** & **MSE** & \(R^{2}\) \\ \hline all features & 1.0729 s & 9.34 MB & 0.0801 & 0.7111 \\ selected features & 0.5956 s & 9.72 MB & 0.0778 & 0.7194 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The effectiveness of feature selection.  
complexity and the prediction time. The calculation of the four evaluation metrics is defined as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|(IRI_{i}-I\hat{R}I_{i})| \tag{13}\]  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}|\frac{I\hat{R}I_{i}-IRI_{i}}{IRI_{i}}| \tag{14}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(IRI_{i}-I\hat{R}I_{i})^{2}} \tag{15}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{i}(IRI_{i}-I\hat{R}I_{i})^{2}}{\sum_{i=1}^{n}(IRI_{i} -IRI_{i})^{2}} \tag{16}\]  
where detailed variable definitions are provided in Table A3 in Appendix A.
## 4 Experiments and Results
### Calibration experiments  
In order to determine the internal parameters of the CCD camera, the checkerboard was placed at eighteen random positions and orientations with a large angle between the imaging axis and the  
Figure 6: The flowchart of the process for the whole calibration.  
Figure 7: Setup for the experiments using the DPMD technique.  
board's normal. The checkerboard size along the rows and columns had the same value of 6 mm, as illustrated in Figure 8.  
At each position, the CCD camera captured the checkerboard image. Using the known checkerboard size in the eighteen captured images, the internal parameters of the CCD camera were obtained by the Camera Calibration Toolbox for Matlab [45], the results are shown in Table 1. The radial and tangential distortions of the following captured images were corrected by the obtained internal parameters. The average reprojection error in direction \(x\) and \(y\) was 0.0856 pixel and 0.0668 pixel, respectively, as illustrated in Figure 9.  
After obtaining the internal parameters of the CCD camera, the calibration procedure included the following steps: (1) The camera captured an image of the mirror with the hollow ring marker matrix to calibrate its extrinsic parameters and determine its orientation in the camera coordinate system; (2) Two groups of orthogonal phase-shifted fringe patterns with the optimum three fringe  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Coefficients Focal Length (pixel)** & \multicolumn{2}{c}{**Principal Point**} & \multicolumn{2}{c}{**Radial Distortion**} & \multicolumn{2}{c}{**Tangential**} \\  & \(\mathbf{F_{u}}\) & \(\mathbf{F_{v}}\) & \(\mathbf{P_{u}}\) & \(\mathbf{P_{v}}\) & \(\mathbf{K_{1}}\) & \(\mathbf{K_{2}}\) & \(\mathbf{K_{3}}\) & \(\mathbf{K_{4}}\) \\ \hline Data & 10137.452 & 10137.811 & 1204.783 & 1011.174 & \(-\)0.2712 & \(-\)1.8758 & \(-\)0.0002 & 0.0003 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calibrated internal parameters of the camera.  
Figure 8: The image of the checkerboard.  
Figure 9: Reprojection error of the calibrated camera.  
numbers were displayed on screens LCD\({}_{1}\) and LCD\({}_{2}\). The camera captured these fringe pattern images in sequence. The absolute maps were obtained from the captured fringe patterns using a four-step phase shifting algorithm and the optimum three-fringe number selection method [46; 47]. A matrix with 12 \(\times\) 9 feature points was designed on the LCD screens to enable the corresponding points on the absolute map to be searched for. The orientations of the two screens were determined in the camera coordinate system based on the phase matching and fringe order constraint principle; (3) The two system parameters \(d_{1}\) and \(d_{2}\) were calculated based on the orientations of the two LCD screens and the reference mirror; (4) Using the known distance between neighboring markers, calibration was achieved by determining the relationship between each pixel position and its XY coordinates.
## 4 Vibration Reduction by the MRE-Based ATDVA under Variable-Frequency Excitation
### Building a Current-Frequency Relationship for Stiffness Control  
The MRE-based ATDVA works to reduce vibrations with variable frequencies excited. An electric current should be applied under the condition of frequency excitation so that the MRE's stiffness is variable. Accordingly, the stiffness relationship of the MRE-based ATDVA and the relationship between its stiffness and vibration reduction frequency must be identified experimentally for the real-time detection and reduction of variable frequencies. Further, there is a need to build the relationship between the applied current and the ATDVA's vibration reduction frequency.  
In identifying the relationship between the variable excitation frequency and the stiffness of the MRE-based ATDVA, the shear modulus measuring system proposed by [PERSON] et al. was used to measure the stiffness of MREs with the application of electric current at intervals of 0.5 A from 0 A to 3 A. The MREs presented in chapter 3 were used as MRE specimens. Figure 18 shows a diagram of the experimental setup. The shear modulus of each MRE was measured with 0-20 kHz white noise as excitation. Given as Equation (21), the equilibrium equation of force for the MRE's shear strain was derived from the shear modulus measurements (Figure 20).  
\[G_{MRE}=k_{\tau}^{*}h \tag{21}\]  
Likewise, the stiffness of MREs was measured at intervals of 0.5 A from 0 A to 3 A (Table 5).  
Figure 19: Circuit of current amplification.  
The correlation between vibration reduction frequency and stiffness was determined using the mass and stiffness of the MRE-based ATDVA.  
Figure 21 shows how the vibration reduction frequency varied with the applied electric current. As seen in the figure, the vibration reduction frequency increased with the application of electric current and varied significantly with rapidly changing MRE stiffness between 0 A and 2 A. The vibration reduction frequencies between the currents applied at 0.5-A intervals were estimated using a linear interpolation.
## 3 Results
### Heavy Metal Removal
#### 3.2.4 Copper Removal Rate  
Figure 7 shows the copper removal rates of kaolin and kaolin/nanomaterial samples over 90 days. The initial copper concentration in the leachate was measured as 2.68 mg/L. While the copper removal rate of all kaolin samples was below 35% on the 30 th day, it varied between 26% and 42% on the 60 th day. On the 90 th day, copper concentrations at the reactor outlet increased. The addition of Oltu clay to kaolin showed better performance in copper removal at all times than both kaolin and other kaolin samples with the addition of nanomaterials. The highest copper removal rate was obtained for 5% Oltu-clay-added kaolin. This is related to the fact that Oltu clay provides relatively more numerous active sites for Cu\({}^{+2}\) to be adsorbed than aluminum oxide or iron oxide, respectively, due to its high surface area.  
Figure 6: Zinc removal rate of kaolin and kaolin/nanomaterial samples.
# Unfair and Risky? Profit Allocation in Closed-Loop Supply Chains by Cooperative Game Approaches
## 7 Conclusions and Future Research  
On the basis of a two-echelon CLSC composed of M, L, and F, this research takes L's risk aversion parameter \(\eta\) and F's fairness concern parameter \(\lambda\) into account on five different occasions: a centralized (CC), a decentralized (DC), and three partially allied models (LF, ML, and MF). Analytical comparison of the resulting equilibriums reveals that the more decentralized the CLSC, the less profit it generates. In models LF and MF, L's risk aversion parameter \(\eta\) acts as a functional tool to reallocate profit between L and M. In models DC, LF, and ML where F's fairness concern is effective, the parameter \(\lambda\) plays a role in re-allocating the profits between F and M. Numerical studies are conducted to investigate how L's risk aversion parameter \(\eta\) and F's fairness concern parameter \(\lambda\) affect profit distribution under the five models. Numerical studies confirm that M, L, and F achieve higher optimal profit even when the other two parties form a coalition than when no coalition exists. The impact of risk aversion parameter \(\eta\) and fairness concern parameter \(\lambda\) is dynamic, not always positive or negative. The larger \(\lambda\) does not lead to more profit transferred to F. The larger \(\eta\), the less profit is transferred to L. These research results provide helpful insights for CLSC managers to find out available choices and feasible ways to reach fair profit allocations. CLSC members should make more efforts to maintain cooperation instead of competition.  
Great opportunities remain for future research. This research discusses a two-echelon CLSC with three agents. It would be meaningful to expand this study into a further complicated CLSC consisting of more agents and more echelons. In addition, this research assumes that one member has fairness concerns, and one other member has risk aversions. It would be very interesting to integrate more agents' risk aversions and fairness concerns into the CLSC and investigate their effects on profit allocations.  
Conceptualization and methodology, T.Z. and T.Y.; data curation, formal analysis, investigation, software, visualization, and writing--original draft, T.Z.; funding acquisition, project administration, resources, supervision, validation, and writing--review and editing, T.Y. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China, grant number 71001010.  
Not applicable.  
The authors declare no conflict of interest.
# Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
## 6 Models for the Latency
### Bluetooth Sniff Low Power Mode  
Taking Table 2 as reference, the latency from end-to-end in Bluetooth multi-hop networks when links are always in sniff low power mode can be modeled as in Equation (4), where some of the time contributions described in [40] are included, namely the amount of hops (N), the inactivity period of the mode (T\({}_{\text{SNIFF}}\)), the ratio of inactivity period affecting the latency (\(\beta\)), and the minimum hop latency (\(\delta_{\min}\)), which summarizes not only the time for processing the data-packets but also other minor contributions. Furthermore, the probability to happen a retransmission in the multi-hop communication is also included in Equation (4): when one retransmission happens (\(p_{1}\)(_retx_)), two (\(p_{2}\)(_retx_)), and more. It happens not only with ping data-packets but also with pingback data-packets.  
\[\delta_{SNIFF}=N\cdot(\beta\cdot T_{SNIFF}+\delta_{min})+p_{1}(retx)\cdot \beta\cdot T_{SNIFF}+p_{2}(2 retx)\cdot 2\cdot\beta\cdot T_{SNIFF}+... \tag{4}\]  
After that, the values for the coefficients in Equation (4) were calculated through linear multiple regression. Since the probability values (\(p_{1}\)(_retx_), \(p_{2}\)(_retx_), etc.) depend on the actual deployment of the home automation application, they have not been included in the regression. Then, the curves that best fit to the results of the tests summarized in Table 2 for the sniff mode were defined.  
The performance in terms of latency of the multi-hop communication network for the ping data-packets when links are in the park mode can be modeled with Equation (5). When the communication is from slave to master, i.e., with pingback data-packets, the performance is similarly modeled with Equation (6).  
\[\delta_{SNIFF}(ping)=N\cdot(0.45\cdot T_{SNIFF}+0.033)+0.202 \tag{5}\]  
\[\delta_{SNIFF}(pingback)=N\cdot(0.46\cdot T_{SNIFF}+0.042)+0.189 \tag{6}\]  
After obtaining the values for the coefficients in Equation (4), the models for the multi-hop communication when working with links in the park mode were validated with R\({}^{2}\) statistical datum. The values of R\({}^{2}\) factor for the results summarized in Table 2 and the models defined by Equations (5) and (6) are 0.9927 when considering ping data-packets, and 0.9910 when considering pingback data-packets. Moreover, the MSE for Equation (5) is 0.023 and for Equation (6) is 0.057.  
As a summary, the latency in asynchronous links of Bluetooth multi-hop networks can be defined with just one equation for each one of the directions, i.e., for ping and pingback data-packets, in park low power mode. Furthermore, the two models were validated with the R\({}^{2}\) statistical parameter and the MSE.
## 3 Experimental Results and Analysis
### Experimental Results for Feature Extraction with Depth Value Calculation
For comparison, we show details for the areas that were not easily observed in the three scenes reconstructed by the different algorithms in Figure 18.  
Figure 18: _Cont._  
As can be seen from Figure 9, since the proposed stereo-matching algorithm and system could detect a large number of feature points and the mismatching rate was very low when stereo matching was performed, the 3D reconstruction based on the good disparity maps formed could produce good 3D models, and they were not inferior to those of the deep learning stereo-matching algorithm. We performed real-time tests with different algorithms and the results are shown in Table 4.  
Figure 18: Comparison of some details from the 3D models. ( a) Scene one: (l) [62]; (l) [63]; (l) [64]; (IV) ours; (b) scene two: (l) [62]; (l) [63]; (l) [64]; (IV) ours; (c) scene three: (l) [62]; (l) [63]; (l) [64]; (IV) ours. As can be seen, in the stereo-matching stage, the stereo-matching algorithms using deep learning consumed more time because they contained more network structures and modules, and our proposed stereo-matching algorithm was faster than the algorithm from [62], which is known for its speed, on the basis of the good disparity results achieved. In the 3D reconstruction task overall, our proposed algorithms showed better real-time performance and can better meet the needs of engineering applications
## 5 Summary and Conclusions
The difference between the retrieval products became important in the divergence field, where divergence in front of the MCS using UHR-MERRA-2 wind field was replaced by convergence by the UHR-RAP. Additionally, this analysis supports the use of the wind gradient field as a more independent metric to capture surface wind features than the divergence [23]. Additional case studies need to be investigated to draw more robust conclusions on the detection of outflow boundaries using the GF method, especially in the case of smaller convective systems, where the utility of the 7-km product is of primary importance. Ku-band scatterometers have been providing valuable wind measurements over the ocean, however the retrieval process is affected by the sea surface temperature (SST) [74] and to a larger degree by rain [64], due to the wavelength dependence on scattering (\(\lambda^{-4}\)) in comparison to the C-band. Several studies have explored solutions to mitigate these effects [75; 76] and validated the performance of Ku-band against C-band scatterometers [77; 78]. Considering the characteristics of MCSs, Ku-band scatterometers might provide a more accurate depiction of the outflow and precipitation boundary. The extend to which the GF method can be applied to scatterometers operating at higher frequencies needs to explored, especially the physical significance of its thresholds. Conceptualization, T.J.L. ; methodology, T.J.L., G.P., P.G., S.W.N., R.D.L. ; software, G.P. ; validation, G.P., T.J.L. ; formal analysis, G.P. ; investigation, G.P., T.J.L. ; resources, T.J.L., T.C. ; data curation, G.P. ; writing-original draft preparation, G.P. ; writing-review and editing, G.P., T.J.L., P.G., S.W.N., R.D.L. ; visualization, G.P. ; supervision, T.J.L., T.C. ; project administration, T.J.L., T.C. ; funding acquisition, T.J.L., T.C. All authors have read and agreed to the published version of the manuscript. This research was funded by NASA's Ocean Vector Winds Science Team (OVWST) grant number NNH13 ZDA001N-OVWST. The authors would like to credit the Py-Art and Pytroll community. The ASCAT data were gathered from EUMETSAT through the Earth Observation Portal. NEXRAD data were gathered from the NOAA's NCEI and buoy data from the NOAA's NDBC. Winds components were collected from NASA's Earthdata web application that is built on top of NASA's Common Metadata Repository, as part of the Earth Observing System Data and Information System project. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 6 A Real Case Study: The Bossea Cave  
As the procedure described above has given promising results, the same methodology has been exploited for the 3D modeling of three surfaces located inside a real cave: the Bossea Cave. Furthermore, in this case, the OnePlus 5 smartphone was used to capture several videos (about one minute for each area). All the frames, with a resolution equal to \(3840\times 2160\) pixel, were extracted, and many of them were selected to be further processed for the 3D model reconstruction. During the acquisition of the videos, no flash or other additional lights were used, other than those already present in the cave, since, although the lighting conditions were not optimal, we wanted to avoid increasing the reflectivity of the wet surfaces. As we were not able to cross the parapet (for safety reasons), we tried  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Images** & **Avg Acquisition Distance [cm]** & **Avg GSD [mm/px]** & **Overlap [\%]** \\ \hline
50 & 37.7 & 0.0920 & 90 \\
25 & 38.6 & 0.0942 & 85 \\
312 & 43.3 & 0.1057 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 10: GSD estimated by the software AMP.  
to acquire as much information as possible by moving the smartphone to different heights and varying the acquisition angle.  
Starting from the videos, a limited number of frames have been selected in order to guarantee sufficient overlap between them.