esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The lime sand bricks were systematically investigated as potential filter materials for the removal of Cu(II) from aqueous solutions using static batch experiments and fixed bed continuous column experiments. The study provides basic knowledge on using construction wastes bricks as a potential economical medium for removing heavy metals in the aqueous phase. The conclusions are listed as follows:  
(1) The adsorption process revealed that the initial uptake was achieved in 7 h. The isotherm results showed that the amount of Cu(II) adsorbed is large and the maximum adsorption capacity could reach 7.01 mg/g.  
(2) Higher bed depths (20 cm) and a longer residence time (12 min) contributed to a more efficient treatment system in the fixed-bed column.  
(3) The adsorption capacity of lime sand bricks increased with the increase of inlet concentration. When the inlet concentration was 2.0 mg/L, the adsorption capacity reached at 169.59 mg/kg.  
(4) The Box-Behnken design results show that the inlet concentration and residence time in the column experiment have significant effects on the removal of Cu(II) by lime sand bricks. The Adams-Bohart model with a high \(r^{2}\) (0.99) can predict the change of the effluent concentration with time in the Cu(II) removal process.  
(5) The ecotoxicity, bioavailability and mobility of Cu(II) in lime sand bricks depended on their chemical speciation rather than their total concentrations. Sequential extraction showed that the lime sand bricks after adsorption of copper can decrease the ecotoxicity and bioavailability.  
Investigation, K.S.; Methodology, Z.Z.; Resources, C.T.; Validation, J.L. and H.L.; Writing--original draft, S.G.; Writing--review & editing, X.Z.  
This research was supported by the National Natural Science Foundation of the China-Youth Project (51678025); Construction of High Level Teaching Teams in Universities of Beijing-the Youth Top-Notch Talent Cultivation Program (CIT&TCD201804051); the Scientific Research Project of Beijing Educational Committee (SQKM201710016016); the Fundamental Research Funds of Beijing Vocational College of Agriculture; Pyramid Talent Cultivation Project and the Fundamental Research Funds of Beijing University of Civil Engineering and Architecture; Great Scholars Program (CIT&TCD20170313); Beijing Advanced Innovation Center for Future Urban Design: Sponge City Development and Water Quantity & Quality Risk Control (UDC2016040100).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Inlet** & **Exchangeable** & **Carbonates** & **Fe-Mn Oxides** & **Organic Matter** & **Residual** & **Total** & **Recovery** \\
**Concentration** & **Fraction (F1)** & **Fraction (F2)** & **Fraction (F3)** & **Fraction (F4)** & **Fraction (F5)** & **Fraction (F6)** & **(\%)** \\ \hline
0.5 & 3.3 & 14.5 & 11.8 & 27.2 & 9.9 & 69.7 & 95.7 \\
1.0 & 4.9 & 17.2 & 18.8 & 35.2 & 15.6 & 91.5 & 100.2 \\
2.0 & 5.6 & 19.6 & 41.8 & 49.0 & 17.4 & 138.0 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The total concentration and speciation of copper (mg/kg).
## 3 Operational Requirements
### Technical Specifications
#### 3.4.4 Navigation Systems  
GPS and GNSS systems provide high accuracy in determining the robot's position in the field, usually with an accuracy of a few centimeters when using advanced technologies such as GPS RTK (Real-Time Kinematic). This precision is essential for the robot to be able to follow predefined routes and return to specific points of interest accurately [63]. By using GPS/GNSS positioning data, the robot can map and cover the entire field systematically, ensuring that no areas are left unharvested. This is especially important on large farms where full manual coverage would be impractical.  
Odometry sensors are crucial for monitoring the robot's movement, providing information about the distance traveled and direction. These sensors are typically embedded in the wheels or the robot's traction system, recording each rotation of the wheels and translating this data into linear and angular displacement [64]. Odometry allows the robot to make constant corrections in its trajectory, compensating for possible slippage or deviations. This is especially useful when GPS/GNSS experiences temporary signal loss or interference, such as in areas with limited satellite coverage [65].  
The combination of GPS/GNSS data and odometry sensors allows for more accurate and reliable navigation. Sensor fusion integrates the advantages of both systems, using GPS/GNSS for global location and odometry for detailed motion tracking in areas with limited coverage or interference. Sensor fusion also provides redundancy, meaning that if one of the systems fails, the robot can continue to navigate using the data from the other system. This increases operational safety and ensures that collection continues uninterrupted.  
The data collected by the navigation and perception sensors is used to create and update maps of the environment in real time. These maps help the robot better understand its surroundings and make informed decisions about how best to move and gather fruit. Advanced navigation systems allow the robot to adapt to changes in the field environment, such as new plantings or modifications in the arrangement of plant rows. This ensures that the robot can operate effectively in different field conditions and configurations.
# Prediction of Cotton Yarn Quality Based on Attention-GRU
## 4 Conclusions  
In this study, we proposed an improved GRU neural network model with the incorporation of the attention mechanism for predicting the tensile strength of cotton yarn, considering the temporal nature of processing stages and their impact on yarn quality. Through a comparative analysis using real sample data, the following conclusions were drawn:  
(1) Compared to the BP neural network, GRU and LSTM neural networks have demonstrated their capability to effectively capture the temporal nature of the spinning process and the influence of process parameters on yarn quality. Their predictive models exhibit relatively lower training loss values on the training data for yarn production.  
(2) By employing the strategy of adapting the loss change threshold for the proposed Attention-GRU model, the optimal number of iterations and corresponding training loss values for the model have been dynamically determined. This adjustment has resulted in a reduction of the training time from 32.31 s to 24.35 s. While ensuring the model's superior predictive accuracy, this approach further enhances the predictive efficiency of the model.  
(3) The attention mechanism can effectively highlight key information in factors affecting yarn quality. By comparing the predictive performance of various models using test data, the attention GRU model has an overall better predictive performance than GRU, LSTM, and BP neural networks without the attention mechanism. Its average prediction error MAPEs have been reduced by 0.367%, 0.158%, and 0.190%, respectively.  
The proposed model and methodology for predicting cotton yarn tensile strength can serve as a foundation for future research into understanding and managing the impact factors and strategies for quality control in other textile production processes.  
Conceptualization, N.D. and X.H.; methodology, H.J. and K.X.; software, H.J. and K.X.; validation, H.J. and K.X.; formal analysis, N.D. and H.J.; investigation, H.J. and K.X.; resources, X.H. and Y.Y.; data curation, N.D. and H.J.; writing--original draft preparation, H.J. and K.X.; writing--review and editing, N.D., X.H. and W.S.; visualization, H.J. and K.X.; supervision, N.D. and H.J.; project administration, N.D. and H.J.; funding acquisition, N.D. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Zhejiang Provincial Postdoctoral Research Program First Class, China (No. ZJ2021038), the Science and Technology Program of Zhejiang Province, China (No. 2022C01202, No.2022C01065), and the Zhejiang Sci-Tech University Research Start-up Fund, China (No.23242083-Y).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflict of interest.
## 2 The Schmidt Decomposition and Its Recursion Tree Structure
### Singular Value Decomposition (SVD) [25]  
For any \(M\times N\) matrix, \(A\), there exists a factorization of the form:  
\[A=U\Sigma V^{*}=\sum_{i}^{r}\sigma_{i}\mathbf{u}_{i}\mathbf{v}_{i}^{*}, \tag{1}\]  
where \(U\) and \(V\) are \(M\times M\) and \(N\times N\) unitary matrices and \(\Sigma\) is an \(M\times N\) rectangular diagonal matrix with positive diagonal entries \(\sigma_{1}\geq\sigma_{2}\cdots\geq 0\), known as singular values. \(r\), the number of non-zero singular values, defines the rank of the matrix and is the min of \(M\) and \(N\).  
Let \(vec(A)\) be the vectorized form of matrix \(A\). The SVD can be used to write this vector into a sum of weight-ordered separable components:  
\[vec(A)=\sum_{i}\sigma_{i}\mathbf{u}_{i}\otimes\mathbf{v}_{i}. \tag{2}\]  
Note that SVD provides the closest low-rank approximations in \(l^{2}\)-norm to the matrix. For instance, a rank \(r_{1}\leq r\) approximation can be obtained by:  
\[A=\sum_{i}^{r_{1}}\sigma_{i}\mathbf{u}_{i}\mathbf{v}_{i}^{*}. \tag{3}\]
## 3 Measurement Setup
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models/Scenarios** & **Methodologies** & **Advantages** & **Important Results** & **References** \\ \hline Standard theoretical & & & & \\ free space (FS) and & Number of beams (or & Strong achieved power & It provides generalized \\ Stanford University & discrete angles) combined at & signal in adaptive array & beam combining model for \\ Interim (SUI) path loss model at 28, 60 and & the Rx, can result in strong & systems & mmWave path loss \\  & received signal power & & & prediction \\  & & & & \\ Urban microcell & Using channel sounder & Characterizes path loss, & PLE for LOS case in 2 and \\ wideband at 28 and & equipped with & shadow fading, delay & for NLOS case in 3 and \\
38 GHz & omnidirectional and & spread, angular spread & similar large- and \\  & steerable directional antenna & and clustering parameters & small-scale parameters \\  & & & achieved at 28 and 38 GHz & \\ \hline Blockage effect causes & Dual-directional path loss & Higher Coverage & Higher BSs and lower \\ low coverage for small cell networks & model incorporates for both LOS and NLOS & probability and better & blockages intensity cause & [60] \\  & transmissions & average achievable rate & lower average & \\ \hline Outdoor propagation & Investigates the effects of & This study helps to prepare & Results shows 9 to 15.6\% \\ path loss models at 60 GHz bands & solar radio emissions for access/backhaul links and D2D communications & appropriate link budgets for deploying 60 GHz for hot and sumy weather & higher PLE values in hot \\  & & & for deploying 60 GHz for \\  & & & & compared to cool night \\  & & & & weather (20–38\({}^{\circ}\)) \\ \hline Measurement at 2 to & & & \\
26 GHz in an urban & Path loss frequency & Dependence is similar in & Larger frequency \\ macro cell & dependence is investigated & LOS areas & dependence is found for \\  & environment & & NLOS environment \\ \hline Spatially consistent & Model is based on large & & & \\ street-by-street path & calibrated raytracing (RT) & Achieve better spatial & Different shadow fading, \\ loss model for the & simulation dataset of 11 BS & consistency & PL slope, variance, \\
28 GHz microcell & locations and over 60,000 PL & & correlation distance, offset \\ scenario & data points & & for each street \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 6 Conclusions  
Sparse arrays composed of UAVs have much larger spacing than half-wavelength. However, they can configure the sub-arrays more flexibly for more efficient power transfer and more extensive coverage, and collaborative jamming in defense applications takes advantage of this to improve maneuverability. When calculating the power distribution in the target region, the waveform superposition algorithm must calculate the jamming waveforms at all the sphere points, then superpose them and integrate them to calculate the jamming power, which is a complicated calculation and reduces the computational efficiency. Therefore, this paper takes the sparse array as a whole, the total equivalent transmitted power is calculated, and the jamming power is estimated based on the array gain and the transmission model. The proposed algorithm dramatically reduces the computational complexity and improves the defense efficiency.  
All authors contributed significantly to the research and development of this manuscript. Y.Z.: Conceptualization, methodology, software, data curation, writing--original draft, visualization. Z.Z.: Supervision, project Administration. Z.H.: Software, data curation, validation. J.Z.: Formal analysis, resources, data curation. J.B.: Writing--review and editing, funding acquisition. L.L.: Formal analysis, investigation. H.H.: Visualization and validation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Youth Foundation of China under Grant No. 62201598.  
The data presented in this study are available upon request from the corresponding author.  
The current research is limited to UAVs' electromagnetic countermeasures, which is beneficial to the development of low-altitude defense in collaborative jamming and does not pose a threat to public health or national security. The authors acknowledge the dual-use potential of research involving UAVs equipped with jamming nodes and confirm that all necessary precautions have been taken to prevent potential misuse. As an ethical responsibility, the authors strictly adhere to relevant national and international laws about DURC. The authors advocate for responsible deployment, ethical considerations, regulatory compliance, and transparent reporting to mitigate misuse risks and foster beneficial outcomes.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results and Discussions
### HCF and VHCF Behavior
The average element size is set to 1 \(\upmu\)m; whereas, a finer discretization is performed in areas where the crack is expected to propagate. Simulations are carried out with a cyclic displacement driven test. The increment of the amplitude is chosen to be 0.0001 \(\upmu\)m/cycle. Displacements are applied on the right edge of the sample, whereas the displacements on the left edge are constrained. The different stages of crack propagation are presented in Figure 11. The cracks initiate at the surface of the holes at the applied displacements of 4.384 \(\upmu\)m and thereafter propagate rapidly. The sample collapses at the prescribed displacements of 4.396 \(\upmu\)m. The evolution of the damage parameter \(d\), the \(\epsilon_{11}^{\mathrm{P}}\) component of the plastic strain tensor and the \(\sigma_{11}\) component of the stress tensor are presented in Figure 12.  
Figure 11: Crack propagation in the sample with two circular pores. The results show the crack propagation for the prescribed displacements: (**a**) 4.384 \(\upmu\)m; (**b**) 4.390 \(\upmu\)m; and, (**c**) 4.396 \(\upmu\)m. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Young’s Modulus** & **Poisson’s Ratio** & **Yield Limit** & **Isotropic Hardening** \\ \hline \(E\) [GPa] & \(\upsilon\) & \(k_{\gamma 0}\) [MPa] & \(H\) [GPa] \\
70 & 0.3 & 218 & 123.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Material parameters used in the simulation for AlSi12. Figure 12: Evolution of: (**a**)the damage parameter \(d\); (**b**) the first component of the plastic strain tensor \(\epsilon_{11}^{\mathrm{P}}\); and, (**c**) the first component of the stress tensor \(\sigma_{11}\). The values correspond to a node at the top of the right hole where one of the cracks initiates. The following conclusions can be drawn here. The rate of the stress decreases after the initiation of the plastic strain. The damage parameter rapidly increases once a certain value is exceeded. The kink of the stress/cycle curve goes back to the fact that hardening in the current model is not coupled with the damage.
## 2 Materials and Methods
### Autofocus Using Sparse Matrix  
In addition to image fusion, autofocusing using sparse matrix method process was also put forward in this study. The sparse feature of sparse matrix was mainly used to test the focus stripe correlation generated from the testing bench. Since low-rank matrix had the main components of focus stripe, it had the same signficiance pointed out in Equation (3) where sparse matrix was the orignal image subtracted by low-rank matrix. Hence, the correpsonding frame information in sparse matrix can be used as a reference for sharpness. This concept was applied in the acutal practice. First, 110 images of different focal distances were compiled into \(\mathbf{D}\) matrix according to the arrangement diagram in Figures 6 and 7. As shown in Equations (1) through (4), they were decomposed into low-rank matrix \(\mathbf{A}\) and sparse matrix \(\mathbf{E}\). Then, \(\mathbf{{}^{E}I^{R}_{i}}\), \(i\) = 1,2,3 \(\ldots\)\(t\) corrsponding to different focal distances in \(E\) matrix were directly used to calculate the images corresponding to single \(\mathbf{{}^{E}I^{R}_{i}}\), to tally the results. An image frame corresponding to the lowest value was the sharpest frame. In particular, the lowest point in Figure 4 was the frame with the best sharpness. The calculation can be simplified into Equation (9).  
\[\begin{array}{rcl}FP&=&\underset{i\in[1,\ t]}{\operatorname{argmin}}\\ &=&\underset{i\in[1,\ t]}{\operatorname{argmin}}\\ \end{array}\left(\sum\limits_{k=1}^{N}\mathbf{{}^{E}I^{R}_{i}}(k)\right)\\ &=&\underset{i\in[1,\ t]}{\operatorname{argmin}}\\ \end{array} \tag{9}\]  
where \(FP\) is focus position and \(N=m\times n\) is image size.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 2 Related Work
### NeQuick Model  
The NeQuick-G model uses empirical and climate-based parameters to represent the ionosphere. It is adopted by Galileo satellites and is designed to remove 70% of ionospheric errors. The NeQuick model has a 3D representation of the electron density inside the ionosphere, and it is able to predict the ionospheric error based on the user's latitude, longitude, and altitude. This prediction is also based on the coefficients \(a_{i1}\), \(a_{i2}\), and \(a_{i3}\) that are broadcast within the Galileo satellite's navigational message. The NeQuick modelis more computationally demanding than other models, such as Klobuchar, but it is also more accurate for Galileo signals [41]. More information about the NeQuick model can be found in [41; 42; 43]. As our experimental data focus on GPS signals (not on Galileo ones), we have not included the NeQuick model in our benchmark algorithms; addressing Galileo signals remains a topic of future research.
## 3 Smart Metering in Germany
### Secure Bidirectional Communication for Value-Added Services
A requirement regarding encrypted communication is that SMGW (for both HAN and WAN interface), aEMP, and CLS have TLS certificates from the SM-PKI and corresponding private keys. **HAN Communication Scenario HKS5:**: In the HKS5 the SMGW initiates the channel. The initiation is triggered by time or another event. Once triggered, the SMGW establishes two TLS channels, one to the CLS and one to the aEMP, according to the respective communication profiles. Here, again, the SMGW connects to the CLS and the aEMP as a TLS client. Hence, the data tunnel is established analogously to HKS4. Figure 2: The three relevant HAN communication scenarios for integrating a decentralized energy management system (EMS), based on [6]. The major difference lies in who initiates the secured communication via the transparent tunnel.
## 5 Discussion
### Limitations and Future Work  
The current study was subject to several limitations. First, the sample size and experimental context are relatively limited. As noted earlier, the constrained spatial range of the virtual environment may have diminished the significance of spatial relationship features. Additionally, the characteristics of social interactions may vary across diverse social groups [77], while the participants in this study were predominantly university students, representing a relatively homogeneous demographic. Future research should aim to include more diverse populations and broader interaction scenarios to enhance the generalizability of the findings.  
Secondly, this study prioritized features that are easily detectable by automated systems, which introduces certain limitations in examining influencing factors. For example, personality traits, which have been shown to significantly impact interaction behaviors [78; 79], were not included in this study. Future research could consider incorporating additional features, such as personality traits, using pre-experiment questionnaires to evaluate the risk of turn-taking failures more comprehensively.
# Optimization Method for Improving Efficiency of Thermal Field Reconstruction in Concrete Dam
## 4 Case Study  
### Project Overview  
The WDD hydropower station is on the Jinshajiang River, located in Luquan County, Yunnan Province, China (Figure 15). The WDD concrete dam is a double curvature arch with a maximum height of 270 m and a thickness-height ratio of 0.19. The river valley is dry and hot with large temperature differences of 14 \({}^{\circ}\)C during the day and night. The power station is installed with 12 hydroelectric generating units with a total installed capacity of 10,200 MW. The concrete volume of the WDD dam is about 2.8 million m\({}^{3}\). The dam consists of fifteen concrete monoliths with no longitudinal joints [22].  
Therefore, temperature control during concrete pouring is challenging. To acquire the evolution process of the dam concrete thermal field and avoid temperature cracks, the optical fibers were buried in the 7# river bed dam monolith and the 12# bank slope dam monolith of the WDD dam. The internal temperature of the concrete was monitored using the DTS system, with the \"Z\"-type fiber layout scheme. Based on the method proposed in this study, real-time thermal fields of the concrete were obtained, and the results were used to guide the formulation of temperature control measures at the construction site.