esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Model Verification Experiment
#### 3.2.2 Student Model
Combined with the data of the true values and predicted values of various defects in the confusion matrix, the classification accuracy, recall rate, and F1 value of various strip steel defects are calculated by Formula (21). The calculation results of the classification indicators are shown in Table 5.  
Table 5 shows that the pitted surfaces and scratches exhibit excellent performance in terms of the F1 value, both of which have values above 93%. The F1 values of inclusions, patches and cracks have average performance, and the misjudgment rate is high, but the F1 values of the six types of defects are all approximately 90%, with good classification results. To further improve the classification accuracy of the six kinds of defect samples, the teacher model SARSN50 can be replaced by SARSN101 while maintaining the student model.
## 4 Fabrication and Experimental Verification
### Return Loss
After measuring the near-field parameters, we turned to the measurement of the far-field parameters, which started with measuring gain with the help of the substitution method, that is, having two standard horn antennas and the AUT (proposed antenna). The simulated gain of the proposed 1 \(\times\) 2 CP MIMO RDRA antenna at a minimum axial ratio (3.88 GHz) was 6.01 dBic. Correspondingly, for the designed 1 \(\times\) 2 CP wideband MIMO, the measured value of gain at the minimum of the resonance frequency of the axial was ~6.0 dBic. The theoretical and measured average gain readings are depicted in Figure 19. over the entire frequency band of interest. An average gain of ~6.00 dBic has been attained through the cp bandwidth in the simulated and fabricated results. Similarly, the behavior of the desired MIMO RDRA antenna demonstrates through ECC and DG. Information regarding the coupling at the ports illustrates only through isolation parameters (\(S_{12}/S_{21}\)). On the other hand, ECC comprises of all the scattering parameters of the proposed wideband circularly polarized MIMO DRA in order to demonst  
Figure 19: Comparison of the gain of the fabricated and simulated MIMO antenna. Figure 18: Comparison of the axial ratio of the fabricated and simulated MIMO antenna. correlation coefficient. If the value of ECC is Low its means have less correlation among radiating elements; however, having higher values of ECC is not considered to suitable for MIMO applications for a decent level of performance. By using the formula shown in Equation (7), the values of the ECC can be obtained  
\[\rho_{e}=\frac{|S_{11}S_{12}+S_{21}S_{22}|^{2}}{\Big{(}1-|S_{11}|^{2}-|S_{21}|^{ 2}\Big{)}\Big{(}1-|S_{22}|^{2}-|S_{12}|^{2}\Big{)}} \tag{7}\]  
The theoretical values of the ECC lie in the range of 0-0.02, as depicted in Figure 20, throughout the whole frequency band, while in the same frequency band, the experimental values were between 0-0.36 between the two radiators. At minimum of axial ratio (3.88 GHz) the simulated value of ECC is \(1.37\times 10^{-6}\), and in parallel experimental value of ECC at same frequency is 0.02. By exploitation far-field radiation samples can help to determine ECC along with the assistance of Equation (8) [31]  
\[\rho_{e}=\frac{\left|\iint_{4\pi}\left[\stackrel{{\rightarrow}}{{F} }_{1}(\Theta,\Phi)\cdot\stackrel{{\rightarrow}}{{F}}_{2}(\Theta,\Phi)\right]d\Omega\right|^{2}}{\iint_{4\pi}\left|\stackrel{{ \rightarrow}}{{F}}_{1}(\Theta,\Phi)\right|^{2}d\Omega\iint_{4\pi} \left|\stackrel{{\rightarrow}}{{F}}_{2}(\Theta,\Phi)\right|^{2 }d\Omega} \tag{8}\]  
DG is an additional measure utilized to test the implementation of a MIMO antenna (DG). The ideal DG value is 10, although anything greater than 6 is considered desirable in reality, and the measured average DG The suggested broadband CP- RDRA MIMO antenna has a value more than 8.5 dB, as illustrated in Figure 20. The subsequent formula be utilized to compute the defected ground of a desired antenna [31]. Figure 15:0
## 3 Results and Discussion
### KPIs: Energy Consumption and Cost of DAC Systems
For the capture system using the APDES-NFC-FD sorbent, the maintenance and repair cost is the most significant element OPEX; hence, the use of another material for equipment could help in cost reduction. The cooling water cost has a great influence on OPEX too. Overall, for processes using amine-functionalized sorbents, it is suggested to use alternative materials of construction and use free air for cooling. For capture systems based on MOF-5 and MOF-177, the cost for the required thermal energy is the most significant, while for the process capturing CO\({}_{2}\) using MIL-101 the cost of cooling water has the highest contribution, although the cost of thermal energy is great too. This suggests that a reduction in costs could be achieved by using air as a cooling medium (as already done in the Antecy Company) and improving the sorbent efficiency of MOFs in terms of loading, which is a critical parameter, as mentioned before. Future research should be focused on improving the efficiency of these new materials, still under study and characterized by unprecedented chemical and structural tunability. Moreover, from the economic analysis we found that the lowest total cost is for the process using the chemisorbent APDES-NFC-FD, while the highest cost is for the process using the physisorbent MIL-101. According to the previous considerations (OPEX with the highest influence on total costs with direct production costs as the main contribution as in Figures S4-S7), we can say that the total costs can range according to the price of significant utilities and raw materials in the market, for the capture system using the silica gel-based sorbent and MOFs. For the process based on the cellulose sorbent, fixed charges have the highest influence on OPEX so we expect that total costs can range \(\pm\)20%. In any case, total costs for amine-based sorbents are in agreement with the order of magnitude estimated by [PERSON] et al. [109] for DAC systems up to 1000 $/tonCO\({}_{2}\). The consideration is for other costs reported in the literature and revised by the work of [PERSON] et al. [53]. These reported costs are of a few hundred dollars per tonne of CO\({}_{2}\) and are based on different assumptions [3,98]. In particular, a few hundred dollars per tonne of captured CO\({}_{2}\) are estimated by [PERSON] and [PERSON] [54], [PERSON] et al. [18], and [PERSON] et al. [55]. However, [PERSON] et al. [110] report in their work that once capital costs are considered, the total capture cost increases significantly. Particular attention should be placed on the use of MOFs, since due to higher operating costs caused by the very low working capacity, these are very expensive for CO\({}_{2}\) capture from air and are not recommended. Future research should be focused on the development of MOFs that ensure higher values of loading. This could be achieved by adding special compounds. This solution could make MOFs suitable for CO\({}_{2}\) capture.
## 2 Stacked Residual Multi-Attention Network Framework
### Attention Fusion Unit  
Inspired by [42], for time series analysis, this study proposes two temporally oriented attention mechanisms, Squeeze-Attention and External-Attention. The specific process is shown in Figure 3, and both methods ensure that the inputs and outputs are consistent, in line with the generic properties of the model. Squeeze-Attention (Figure 3a) is a simplified version of self-attention. \"Squeezing\" and \"Expanding\" refer to the linear down-sampling transformation of the input feature map and the up-sampling of the attention output matrix, respectively. It can be found that Squeeze-Attention is able to add a gating mechanism in the time direction at the input, which can ensure that the input and output remain consistent. The method can be expressed as follows:  
\[\begin{split}& soft\text{max}(x_{i})=\frac{e^{x_{i}}}{\sum_{j}e^{x_{j}}} \\ & A=(\alpha)_{i,j}=softmax(F_{tr}(u_{n})_{1})\\ & F_{out}=F_{\text{exp}}(A\times F_{tr}(u_{n})_{2})\end{split} \tag{11}\]  
where \((\alpha)_{i,j}\) denotes the affinity matrix in the time direction, \(F_{tr}\) denotes a linear transformation, and \(F_{\text{exp}}\) is the up-sampling process.  
However, Squeeze-Attention's focus is on the filtering results of a single filter in the time direction. To this end, this study incorporates the advantages of External-Attention (Figure 3b). External-Attention introduces learnable external memory units capable of expanding attention to all channels and retaining previous training memories. L1-Norm eliminates scale sensitivity of features. The method can be expressed as follows:  
\[\begin{split}\text{F}&=f_{\text{proj}}(u_{n})\\ \text{A}&=\text{Norm}\Big{(}\text{FM}_{\text{k}}^{ \text{T}}\Big{)}\\ \text{F}_{\text{out}}&=\text{AM}_{\text{v}}\end{split} \tag{12}\]  
where \(\text{F}\in\mathbb{R}^{s\times n}\) is the feature map after linear projection, \(\text{M}_{\text{k}}\in\mathbb{R}^{s\times n}\) and \(\text{M}_{\text{v}}\in\mathbb{R}^{s\times n}\) are external memory units. s is the hyperparameter of the memory unit, capable of controlling the memory unit size. To ensure consistent input and output, the same up-sampling strategy needs to be added at the end of the method. The complete training process of the model is summarized in Algorithm 1. The methodology flow chart is provided in Figure 4.  
```
1:Initialize\(\theta_{M}\) and shuffle the train set \(\{X,Y\}\)
2:for\(t=1,2\dots\dots\), \(T_{\text{max}}\) do
3: Calculate cross entropy loss \(loss_{clf}=\{L(\theta_{M}),x_{k}\}_{k=1}^{K}\)
4: Calculate\(P_{th}\) model gradient \(grad\{\theta_{M},X,p\}=\{\bigtriangledown f\}=\left\{\partial\theta_{M}^{p}/ \partial\left\{x^{i}\right\}_{i=1}^{n}\right\}\)
5: update\(\theta_{M}=\theta_{M}+\gamma\bigtriangledown loss_{clf}\)
6:if\(\%10==0\)then
7: return model parameter \(\theta_{M}\) and \(\{\bigtriangledown f\}\)
8:endif
9:endfor
```  
**Algorithm 1**SRMANet training process  
Figure 3: Attention Fusion Unit. (**a**) Squeeze-Attention. (**b**) External-Attention.
# A Game-Theoretic Analysis for Distributed Honeypots
## 4 Game Theory Analysis
### Bayesian Equilibriums of the Server  
From the perspective of a server, there are four kinds of access strategies of visitors. Among these strategies, \((\pi_{21},\pi_{21})\) is in line with reality. Therefore, taking \((\pi_{21},\pi_{21})\) as an example, we analyze whether a game equilibrium exists or not. Based on the strategy \((\pi_{21},\pi_{21})\), the server knows that opposite players will visit the system. Posteriori probabilities are assumed to be: \(\{P^{\prime}(\theta_{21}|\pi_{21})=p,P^{\prime}(\theta_{20}|\pi_{21})=1-p\}\).  
Based on the posteriori probabilities, payoffs of a honeypot for the strategies \(\pi_{11}\) and \(\pi_{10}\) are denoted as \(\mu_{\theta_{111}(\pi_{11})}\) and \(\mu_{\theta_{111}(\pi_{10})}\) where  
\[\mu_{\theta_{111}(\pi_{11})}=P^{\prime}(\theta_{21}|\pi_{21})(\eta c/N)+P^{ \prime}(\theta_{20}|\pi_{21})\times(0)=p(\eta c/N), \tag{1}\]  
\[\mu_{\theta_{111}(\pi_{10})}=P^{\prime}(\theta_{21}|\pi_{21})\times 0+P^{ \prime}(\theta_{20}|\pi_{21})\times(0)=0. \tag{2}\]  
From Equations (1) and (2), it can be inferred that \(\mu_{\theta_{111}(\pi_{11})}>\mu_{\theta_{111}(\pi_{10})}\), which indicates \(\pi_{11}\) is an absolutely dominant strategy for \(\theta_{111}\). No matter which kind of visitors enters, the honeypot tends to be on.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Attacker** & & & **User** \\ \cline{3-5}  & & \(\pi_{21}\) & \(\pi_{20}\) & \(\pi_{21}\) & \(\pi_{20}\) \\ \hline \multirow{2}{*}{\(\theta_{110}\)} & \(\pi_{11}\) & \((-\gamma a/N,\gamma a/N-b)\) & \((0,0)\) & \((a,a)\) & \((0,0)\) \\  & \(\pi_{10}\) & \((0,-b)\) & \((0,0)\) & \((-a,-a)\) & \((0,0)\) \\ \multirow{2}{*}{\(\theta_{111}\)} & \(\pi_{11}\) & \((\eta c/N,-\eta a/N-b)\) & \((0,0)\) & \((0,-a)\) & \((0,0)\) \\  & \(\pi_{10}\) & \((0,-b)\) & \((0,0)\) & \((0,-a)\) & \((0,0)\) \\ \hline \end{tabular}
\end{table}
Table 7: The simplified payoff matrix.  
Figure 4: Attack-defense game tree.  
As for real services, we get the following payoff equations.  
\[\mu_{\theta_{110}(\pi_{11})}=P^{{}^{\prime}}(\theta_{21}|\pi_{21})(-\gamma a/N)+P^ {{}^{\prime}}(\theta_{20}|\pi_{21})a=p(-\gamma a/N)+(1-p)a \tag{3}\]  
\[\mu_{\theta_{110}(\pi_{10})}=P^{{}^{\prime}}(\theta_{21}|\pi_{21})\times 0+P^ {{}^{\prime}}(\theta_{20}|\pi_{21})(-a)=(1-p)(-a) \tag{4}\]  
Solving Equations (3) and (4) simultaneously, we obtain \(2N/(r+2N)=p\). Consider the case when \(p<2N/(r+2N)\). In this case, the dominant strategy is \(\pi_{11}\) for a real server. When \(p>2N/(r+2N)\), \(\pi_{10}\) is the optimal choice. Considering the absolutely dominant strategy \(\pi_{11}\) of a honeypot, we can infer that \((\pi_{11},\pi_{11})\) and \((\pi_{10},\pi_{11})\) respectively acts as the optimal selection for a server under circumstances of \(p<2N/(r+2N)\) and \(p>2N/(r+2N)\), as illustrated in Table 8.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 1 Introduction
Combining multiple platforms to localize important sources can effectively improve the localization accuracy, which is a very effective means in practice. By combining land-based short-wave stations with satellites for localization, a hybrid localization algorithm based on a penalty function was proposed in [5]. But, Ref. [5] does not exploit the elevation information and does not consider the position errors of the satellite. Moreover, the equation constraints in the localization optimization problem are nonconvex, and thus the method can fall into a local optimal solution, which leads to a degradation of the localization accuracy. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for sources such as warships transmitting multiple types of signals, which are localized using land-based short-wave stations and satellites. By introducing auxiliary variables, the DOA-based localization using short-wave stations and the TDOA-based localization using satellites can be modeled as optimization problems with two quadratic equation constraints, respectively. The quadratic nonconvex equation constraints are linearized using the results of the previous iteration [17; 18], which converts the nonconvex constraints to linear constraints and ensures the algorithm can converge to the global optimal solution. Due to the difference in auxiliary variables between the DOA pseudo-linear equation and the TDOA pseudo-linear equation, a unified pseudo-linear equation cannot be established. Therefore, it is proposed in this paper that the localization results of the two methods are fused using Kalman filtering to improve the localization accuracy. The mathematical symbols used in this paper and explanations are shown in Table 1. This paper is organized as follows: Section 2 introduces the localization scenario and the measurement model; Section 3 establishes the corresponding pseudo-linear equations; Section 4 derives the Constrained CRLB (CCRLB); Section 5 gives the proposed localization algorithms and the method for fusion; Section 6 shows the simulation results; and Section 7 is the conclusion.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
###### Abstract  
Abstract:Abstract in the marine environment, underwater targets are often affected by interference from other targets and environmental fluctuations, so traditional target tracking methods are difficult to use for tracking underwater targets stably and accurately. Among the traditional methods, the Kalman filtering method is widely used; however, it only has advantages in solving linear problems and it is difficult to use to realize effective tracking problems when the trajectory of the moving target is nonlinear. Aiming to solve this limitation, an LSTM-Kalman filtering method was proposed, which can efficiently solve the problem of overly large deviations in underwater target tracking. Using this method, we first studied the features of typical underwater targets and, according to these rules, constructed the corresponding target dataset. Second, we built a convolutional neural network (CNN) model to detect the target and determine the tracking value of the moving target. We used a long-term and short-term memory artificial neural network (LSTM-NN) to modify the Kalman filter to predict the azimuth and distance of the target and to update it iteratively. Then, we verified the new method using simulation tests and the measured data from an acoustic sea trial. The results showed that compared to the traditional Kalman filtering method, the relative error of the LSTM-Kalman filtering method was reduced by 60% in the simulation tests and 72.25% in the sea trial and that the estimation variance was only 4.79. These results indicate that the method that is proposed in this paper achieves good prediction results and a high prediction efficiency for underwater target tracking.  
target tracking: LSTM-Kalman filtering; underwater target; active sonar +
Footnote †: journal: Applied Sciences
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 7 Conclusions  
Dam deformation prediction is of great significance, as accurate prediction can effectively evaluate the health status of dams and assist managers in formulating better management strategies. In this paper, a combination model method is proposed to improve the accuracy of dam deformation prediction by improving existing methods and introducing new prediction models. For the single-point scenario, an IEALL-based dam deformation prediction model is proposed to address the issues of low prediction accuracy caused by simple linear addition and parameter random initialization leading to local optima. For the multi-point scenario, a dam deformation prediction algorithm based on spatio-temporal correlation and IEALL (STAGCN-IEALL) is proposed to address the issues of dynamic correlation between different points in the deformation sequence and dynamic dependence on different points. In the comparison experiment of prediction results of various models, STAGCN-IEALL reduced RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%, respectively, compared to ST-GCN. In the prediction results comparison experiment at different time lengths, the STAGCN-IEALL model maintained the lowest RMSE, MAE, and MAPE values as the prediction length increased, and the growth rate was relatively stable. In the ablation analysis experiment, after adding the spatio-temporal feature extraction module, the model's RMSE, MAPE, and MAE were reduced by 7.78%, 5.93%, and 7.91%, respectively. Through various experiments, the proposed combination model can effectively predict the monitoring data of dam deformation and has certain guiding and reference significance for solving practical engineering problems.  
Figure 13: Comparison of model predictions of each variant.  
**Author Contributions:** Conceptualization, G.X. and Z.J.; methodology, Z.J.; software, Y.L.; validation, Y.L., Z.J. and Q.Z.; formal analysis, Z.J.; investigation, Y.L.; resources, Z.J.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, C.W.; visualization, Z.J.; supervision, C.W.; project administration, G.X.; funding acquisition, G.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The Water Resources Science and Technology Projects in Jiangsu Province and The National Key R & D Program of China, grant number 2018 YFC0407106 and 2017065.  
**Data Availability Statement:** Data generated and analyzed during this study are available from the corresponding author by request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Critical and Rare Metals in Greece
### Tellurium (Te)  
Tellurium, a relatively rare metalloid, is used in manufacturing alloys and semiconductors, as well in vulcanizing rubber and in catalysts for petroleum. In alloys it improves properties like ductility and tensile strength or increase the durability and flexibility. It is added to lead to decrease the corrosive action of sulfuric acid in batteries and to improve the lead's strength and hardness. It is also used in making various types of detectors. Compounds made of Te, Cd and Hg, are excellent infrared sensitive semiconductors [25]. The use of high-purity tellurium enhances the effectiveness of the solar cell electric power generations and is very promising for future applications. Tellurium is extensively used as a coloring agent in ceramics and glasses. It is also used as a gasoline additive to reduce engine knocks in automobiles.  
Almost all tellurium currently produced is obtained as a by-product of copper refining. It is estimated that the Cu deposits globally contain approximately 2.2 \(\times\) 10\({}^{-7}\) kg of tellurium reserves worldwide [25]. However, the high-grade ores are being exhausted and this may result in limitations in future tellurium supply.  
In Greece tellurium is a common constituent of Cu-Au epithermal high-intermediate sulfidation type mineralizations (St. Barbara/Kassiteres, St. Demetrios/Sapes, Perama Hill, Mavrokoryfi, Pefka) and spatially associated porphyy Cu-Mo systems (Pagoni Rachi/Kirki, Konos/Sapes), in intrusion-related Au-Bi-Te systems (Palea Kavala, Pangeon Mt) of Oligocene- Miocene age at the Rhodope province, as well as in Limnos and Tinos Islands, Aegegan sea (Fakos porphyy-epithermal- and Panormos intrusion-related Cu-Au-Te prospects respectively) [7; 26; 27; 28]. Tellurium in these prospects is incorporated in various telluride phases, such as hessite, stuzzite, petzite, altaite, sylvanite, melonite, calaverite, coloradotite and tetradymite, as well as in the sulfosalt goldfieldite (tetrahedrite-group). In rare cases native tellurium is present in minor concentrations (Pefka, Tinos, Palea Kavala). The tellurium content in the mineralized samples is variable and is related with the distribution of Te-bearing minerals. According to new chemical analyses, provided here for the first time, the richest samples contain up to 40 ppm in Pagoni Rachi, 45 ppm in Perama Hill, 60 ppm in Pangeon Mt, and \(>\)1000 ppm in Pefka. Comparing with other potential Te-rich ore deposits worldwide (Boliden mining company will produce Te from the Kankberg gold mine, Sweden, with reserves of 2.8 Mt which contains 186 ppm Te in average [29]), the Pefka area is the most promising prospect for future Te exploration and exploitation in Greece.  
The source rocks of the tellurides in northeastern Greek mineralizations are mantle-derived calc-alkaline (Thrace) to shoshonitic (Limnos) subvolcanic intrusions. Magmas were likely enriched in Te as a result of previous subduction of Te-bearing sediments [7].