esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 DT Model of the Electromagnetic Coupling Process of the BTS
### DT Model of Electromagnetic Coupling Process of BTS  
The three most essential sub-models of the electromagnetic coupling process of BTS are developed in Section 3.2, Sections 3.3 and 3.4, respectively. In this subsection, based on the research in the previous four subsections, the sub-models are fused, and a DT model of the electromagnetic coupling process of BTS is successfully constructed, as shown in Figure 10.  
The mutual inductance model between antennas was involved in constructing the uplink or downlink transmission model. The uplink transmission model needed to calculate the mutual inductance between the onboard receiving antenna and the balise transmitting antenna, as shown in (1). For the downlink transmission model, the antennas of the mutual inductance model became the onboard transmitting antenna and the balise receiving antenna, and the corresponding antenna parameters in (1) needed to be replaced.  
The downlink transmission model could be obtained according to (14). In (14), \(M_{\text{d}}\) refers to mutual inductance between downlink transmission antennas, which can be calculated by referring to (1).  
The equivalent current model of the passive balise was obtained by the black-box test method, as shown in (18). When the passive balise changes, we first used the black-box test method to obtain the test result curve of the input and output characteristics of the balise and then obtained the corresponding equivalent current model according to the fitting result of (15).  
According to the uplink transmission model and the matching circuit model of the onboard receiving antenna, the uplink signal power received by the onboard receiving antenna at any relative position of the OAU and balise can be calculated, as shown in (13). The current \(i_{1}\) in (13) is the uplink transmitting current of balise, which is consistent with \(i_{\phi}\) in (18) for the passive balise.  
Figure 10: DT model of electromagnetic coupling process of BTS.  
The fusion of the above sub-models resulted in a system-level DT model that allowed the analysis of the electromagnetic coupling process of the BTS. The effect of the variation of different system parameters on the uplink signal transmission characteristics could be studied quantitatively using this DT model.
## 4 Numerical Experiments, Results and Discussions
### Compared with Other Six Heuristic Algorithms  
Meanwhile, the results are compared with that obtained by the particle swarm optimization (PSO) [19], the bee colony optimization (BCO) [6], the self-organizing Neural Network (NN) [20], the improved ACO with Pheromone Correction Strategy (ACO+SEE) [14], the generalized chromosome genetic algorithm (GCGA) [25] and the genetic simulated annealing ant colony system with particle swarm optimization techniques (GSAP) [22], shown in Tables 4 and 5. The meanings of the fields in Tables 4 and 5 are same as those in Table 3. More intuitive comparisons are shown in Figures 7 and 8.  
From Table 4, Table 5, Figure 7 and Figure 8, it can be seen that, when compared with the PSO, the BCO, the ACO+SEE, the NN and the GCGA, the proposed algorithm not only found the known optimal solution that others succeeded, but also found that the others failed. In addition, for kroB150, though all the four algorithms (the PSO and the ACO + SEE have not been tested on kroB150 in the literatures) failed to find the known optimal solution, the proposed algorithm obtained a best result. For rl1323 and fl1400, the proposed algorithm shows greater performance than the NN (other four algorithms have not been tested on rl1323 and fl1400 in the literatures). Furthermore, for every instance, the deviation between the average of the results obtained by the proposed algorithm and the known optimal solution is much lower. When compared with the GSAP, for eil76, all the two algorithms found the known optimal solution while the average of the results obtained by the GSAP is slightly better. For eil10
# Domination and Independent Domination in Extended Supergrid Graphs +
## 5 Concluding Remarks  
Here, we first introduce the class of extended supergrid graphs containing grid, triangular grid, supergrid, diagonal supergrid, and triangular supergrid graphs as subclasses. Domination and independent domination problems for grid graphs are known to be NP-complete, and they are NP-complete for extended supergrid graphs as well. However, the complexities of other subclasses of extended supergrid graphs remain unknown. In this paper, we first prove that the domination and independent domination problems on diagonal supergrid graphs are NP-complete. As this result can be immediately applie dto supergrid and triangular supergrid graphs, both problems are NP-complete for them as well. The studied problems on rectangular supergrid graphs and rectangular grid graphs have been solved in linear time. However, their complexities remain unknown for rectangular triangular-supergrid graphs. In this paper, we provide a tight upper bound of the domination and independent domination numbers for rectangular triangular-supergrid graphs. It might be interesting to obtain a lower bound of the domination and independent domination numbers for rectangular triangular-supergrid graphs, which we leave as an open question for future interested readers. Furthermore, the complexities of the domination and independent domination problems for triangular grid graphs remain unknown. We speculate that they are NP-complete. However, we are unable to verify this, and would like to publish it as an open problem for interested readers. Finally, we conclude the status of the complexities of the domination and independent domination problems for extended supergrid graphs in Figure 14.  
Figure 14: The complexities of the domination and independent domination problems for the classes of extended supergrid graphs. NP-c = NP-complete, P = Polynomial, * = this paper solved, 7 NP-c = speculated to be NP-complete, and 7P = speculated to be polynomial.  
**Author Contributions:** Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis and investigation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]-S.C. and [PERSON]-[PERSON]; writing--review and editing, [PERSON] and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Science and Technology Council, Taiwan, under grant No. MOST 110-2221-E-324-007-MY3.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank anonymous referees for many useful comments and suggestions which have improved the presentation and correctness of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 1 Introduction
Moreover, artificial neural networks are used by scientists to predict clock offset [12; 16; 24; 25], e.g., WNN (wavelet neural network) and BPNN (back propagation neural network), which has the ability to estimate non-linear time series by sample training. However, the topology structure of the WNN is hard to set up, while the BPNN needs an iterative procedure to determine the appropriate weights which is considerably time-consuming. While most of the abovementioned achievements focus on GPS clock, BDS clock attracts more and more attention. There are also three types of real-time clock products for BDS satellites, namely broadcast ephemeris clock products with the accuracy of about 6 ns, ultra-rapid products about 2-6 ns and real-time estimated corrections updated every 5-10 s [12; 26]. After comparison of these products, it can be concluded that the ultra-rapid product plays an important role in decimeter-level real-time positioning services, which is made available by some analysis centers [12]. However, evident nonlinear system errors, i.e., periodic signals, are detected in ultra-rapid clock product [12; 25]. To meet the requirement of highly precise applications, such as early warning and hydrographic surveying, an improved model is required to enhance its prediction. Regarding the benefit from artificial intelligence, GRNN (generalized regression neural network) is a generation of radial basis function networks and probabilistic neural networks proposed by [PERSON] [27] and has a strong ability of generalization for non-linear and stochastic clocks. In this paper, an improved model based on the SA model and GRNN is developed to deal with BDS-2 satellite clock prediction. In the following sections, the related typical models for BDS-2 satellite clock prediction are summarized in Section 2, including the quadratic polynomial model, spectrum analysis model and GRNN. Then, an improved model for BDS-2 satellite clock prediction is proposed, and satellite- specific optimal parameters are determined by experiments in Section 3. After preprocessing of clock offsets, clock accuracy, static and kinematic precise point positioning (PPP) are validated and analyzed in Section 4. Finally, Section 5 concludes with a discussion.
## 1 Introduction
In addition to experimental mechanics to understand the bone fracture [19; 20], computer-aided simulation plays an important role to elucidate the knowledge of crack initiation, propagation, and toughening mechanisms in the bone [21]. Most modeling and simulation studies focused on the finite element method (FEM). [PERSON] [22] carried out two-dimensional (2D) cohesive finite element simulations to consider the crack spread into osteons. This study reported that the crack propagation relies on osteon fracture properties. Nevertheless, the modulus of elasticity of osteon had almost no dominance to the crack trajectory. [PERSON] et al. [23] checked the effect of strain rate and porosity on the fracture toughness of human cortical bone, using a cohesive finite element model in 2D. This study showed that a decreased bone resistance to crack propagation could be caused by an increase in strain rate. The eXtended Finite Element Method (X-FEM), in which the crack is not explicitly meshed, was also used to study bone fracture [24]. [PERSON] et al. [25] developed a multi-scale model to study multiple crack growth in a human cortical bone under tension, using the X-FEM and a critical stress intensity factor standard for determining the crack propagation. [PERSON] et al. [26]used X-FEM to simulate the fatigue crack growth in the hip implant. They simulated fatigue crack growth from the initial crack up to the critical point in a 3D model. The dimension of crack initiation was defined from the experience with failures of hip implants. Simultaneously, this investigation approximated the critical point according to the fracture toughness obtained by a hip implant experiment. This study reported that the stress intensity factors of Mode I were much greater than those of Mode II and Mode III. Furthermore, [PERSON] et al. [27] indicated the effect of aging on traits of human cortical bone using X-FEM. This study also considered that failure mechanisms and propagation of cracks in the cortical bone undergo tensile loading. In summary, previous studies on the bone crack and fracture allow advancing the knowledge of these complex physical conditions. Most previous bone fracture simulation studies addressed the effects of micro-cracks on cortical bones. Moreover, femur crack and fracture are the most studied phenomena in the TKR context. In addition, these studies raise many questions about whether the role of the cancellous component's mechanical properties could affect bone fracture analysis, and the limitation about the location of the crack initiation was not considered thoroughly. In particular, the mechanical behaviors of full macro-crack propagation and age-related effects on the tibia bone crack and fracture were not extensively studied. The present study aimed to develop a patient-specific model of the tibia bone and associated TKR implant, using X-FEM to study the tibia crack and fracture in physiological and pathological (i.e., age-related effects) conditions. Both cortical and cancellous bones were taken into consideration.
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
###### Abstract
This method effectively mitigates the impacts of communication interruptions and meets the accuracy requirements of general numerical weather forecasting, but it does not consider the case where the communication interruption time is more than 1 h. In addition, [PERSON] et al. [27] and [PERSON] et al. [28] proposed a real-time water vapor data retrieval method based on the BDS-3 PPP-B2b signal. This method yields a real-time ZTD product with an accuracy of 2.64 cm and a real-time PWV product of less than 4 mm. This method utilizes the high-precision RT-PPP service provided by the BDS-3 PPP-B2b signal without relying on internet communication for data reception, but the disadvantage is that global service is not possible. Notably, [PERSON] et al. [29] and [PERSON] et al. [30] proposed a PPP with the broadcast ephemerides (BE-PPP) method, which provides a new opportunity for real-time PPP to cope with communication outages. In terms of positioning accuracy, BE-PPP performs well. The 3D direction positioning accuracy of the GPS + Galileo static BE-PPP solution is about 10 cm, while it is 8.6 cm for the BDS-3 + GPS + Galileo static BE-PPP solution. To explore the potential of BE-PPP in the retrieval application services of ZTD/PWV, this study investigated water vapor inversion using the GPS/Galileo BE-PPP method to evaluate the bias and stability of real-time ZTD/PWV. The water vapor data from nearby RDS stations and the ERA5 were used in the PWV comparison. The processing strategy of BE-PPP and the ZTD/PWV retrieval algorithm are described in detail in Section 2. The results and a discussion of the ZTD/PWV retrieval experiments are presented in Section 3. The summary and conclusions are presented in Section 4.
# Determination of Arsenic Species Distribution
## 4 Results of Geochemical and Geophysical Studies
### Arsenic Species
The experiment and the developed approach to sequential extraction showed its applicability for assessing the content of mobile forms of arsenic in the arsenide tailings, combined with an analytical arsenic speciation. It is shown that the content of directly water-soluble forms of 9.3% is limited by the solubility of the corresponding compounds of magnesium arsenates and arsenites. As previously shown for Khovu-Aksy tailings, a single water extraction does not allow the complete extraction of all potentially water-soluble forms of arsenic. Therefore, the logic of using a phosphate buffer for the extraction of all potentially water-soluble forms of arsenic in this work is confirmed. This made it possible to estimate the total content of potentially water-soluble forms of arsenic (which is 39% of the total As) due to competitive anion exchange. Thus, release arsenic into solutions is limited by the presence of a heterogeneous \"magnesium arsenate-arsenite buffer\". At the same time, the content of arsenic forms sorbed on carbonate minerals is 17%. These forms can also be considered as potentially mobile arsenic. Given that when the pH of the medium changes, arsenic associated with magnesium is washed out, these forms associated with carbonates are also capable of further migration and transport. It is also necessary to consider the different toxicity of As\({}^{\text{V}}\) and As\({}^{\text{III}}\) contained in potentially water-soluble and mobile forms. As a result, it was shown that the percentage of water-soluble forms of arsenic (F1-F2) in the tailings is 39% of the total arsenic content, and the percentage of potentially mobile forms of arsenic (F1-F2-F3) is already 56%. Thus, the content of arsenic in water during the interaction of seasonal precipitation (rain and snow) will be several orders of magnitude higher than the WHO-recommended Permissible Limit of arsenic concentration in groundwater (10 \(\upmu\)g/L) [2]. Moreover, there are significant contents of As\({}^{\text{III}}\), as a more toxic form of arsenic relative to As\({}^{\text{V}}\). In addition to carrying out the sequential extraction procedure, the obtained results were compared with the results of the content of the components in a single aqueous extract prepared in a ratio of 1:10 substance-water (Table 6). According to the results obtained, it can be concluded that for these tailings, the use of single water extracts to assess the content of water-soluble forms of arsenic is not correct because it gives a significant underestimation. The share of water-soluble forms according to a single water extract is 6% of the total content, whereas, according to the data of stepwise leaching, the share of potential water-soluble forms (F1-F2) is already 39%. The difference is almost six times.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Experimental Design
#### 3.4.4 Significance Test  
To further validate the statistical significance of the model parameters, we introduced Student's \(t\)-test using the \(t\)-statistic and the degrees of freedom (\(df\)) to calculate the \(p\)-value through the t-distribution, which is used to assess whether there is a significant difference between the predicted value of the model and the true observation. \(t\) and \(df\) calculations are shown in Equations (14) and (15).  
Original hypothesis (H\({}_{0}\)): There is no significant difference between the predicted and actual values.  
Alternative hypothesis (H\({}_{1}\)): There is a significant difference between the predicted and actual values.  
\[t=\frac{\overline{x}_{1}-\overline{x}_{2}}{\sqrt{\frac{\overline{s}_{1}^{2}}{ n_{1}}+\frac{\overline{s}_{2}^{2}}{n_{2}}}} \tag{14}\]  
\[df=\frac{\left(\frac{\overline{s}_{1}^{2}}{n_{1}}+\frac{\overline{s}_{2}^{2} }{n_{2}}\right)^{2}}{\frac{\left(\frac{\overline{s}_{1}^{2}}{n_{1}}\right)^{ 2}}{n_{1}-1}+\left(\frac{\overline{s}_{2}^{2}}{n_{2}-1}\right)^{2}} \tag{15}\]  
where \(\overline{x}_{1}\) is the sample mean of the true value, \(\overline{x}_{2}\) is the sample mean of the predicted value, \(s_{1}\) is the sample variance of the true value, \(s_{2}\) is the sample variance of the predicted value, and \(n_{1}\) equals \(n_{2}\) to indicate the sample size.
## 3 Multi-Source Subdomain Adaptation Model
### Local Maximum Mean Discrepancy  
Since MMD mainly learns the global alignment of the source domain and the target domain, it ignores the subdomain relationship between the two domains. After the global domain adaptation alignment, the distribution of the two domains is basically the same, but the data distance of the different subdomains is too close, making the network difficult to classify accurately. Therefore, this paper introduces the local maximum mean difference (LMMD) [27].  
In Figure 4, the left side is global domain adaptation and the right side is a subdomain adaptation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Structure** & **Type** & **Receptive Field Size** \\ \hline Input & \(224\times 224\times 3\) & \\ Con1 & Convolution layer & \(7\times 7,64\) \\ Max pool & Maximum pooling layer & \(3\times 3,64\) \\ Conv2\_x & Residual block \(1\times 3\) & \(1\times 1,64\) \\  & & \(3\times 3,64\) \\  & & \(1\times 1,256\) \\ Conv3\_x & Residual block \(1\times 4\) & \(1\times 1,128\) \\  & & \(3\times 3,128\) \\  & & \(1\times 1,512\) \\ Conv4\_x & Residual block \(1\times 6\) & \(1\times 1,256\) \\  & & \(3\times 3,256\) \\  & & \(1\times 1,1024\) \\ Conv5\_x & Residual block \(1\times 3\) & \(1\times 1,512\) \\  & & \(3\times 3,512\) \\  & & \(1\times 1,2048\) \\ Average & Average pooling layer & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ResNet50 network structure.  
Suppose there are \(m\) samples in source domain \(X^{s}\) and \(n\) samples in target domain \(X^{t}\), where samples \(x_{i}^{s}\) and \(x_{j}^{t}\) belong to each category, with weights \(\omega_{i}^{sc}\) and \(\omega_{j}^{tc}\), and \(y_{ic}\) is the \(c\)th element of the label vector \(y_{i}\) (\(c\) is the number of categories). The formulas for LMMD are shown in (5) and (6).  
\[\omega_{i}^{c}=\frac{y_{ic}}{\sum\limits_{(x_{j},y_{j})\in D}y_{jc}} \tag{5}\]  
\[\hat{d}(X^{s},X^{t})=\frac{1}{C}\sum\limits_{c=1}^{C}\left\|\sum\limits_{i=1} ^{m}\omega_{i}^{sc}\phi(x_{i}^{s})-\sum\limits_{j=1}^{n}\omega_{j}^{tc}\phi(x_ {j}^{t})\right\|_{H}^{2} \tag{6}\]  
Since \(\phi()\) cannot be calculated directly, the above equation is transformed. As shown in Formula (7), \(k(\,\ )\) represents the inner product of the vector.  
\[\begin{array}{c}[\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\omega_{i}^{sc} \omega_{j}^{sc}(x_{i}^{s},x_{j}^{s})\\ loss_{lmmd}=\hat{d}(X^{s},X^{t})=\frac{1}{C}\sum\limits_{c=1}^{C}\begin{array}[ ]{c}+\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\omega_{i}^{tc}\omega_{j}^{tc}( x_{i}^{t},x_{j}^{t})\\ -2\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\omega_{i}^{sc}\omega_{j}^{tc}(x_{i} ^{s},x_{j}^{t})]\end{array} \tag{7}\]
## 2 Literature Review  
[PERSON] et al. [14] present a novel federated DL approach termed DeepFed, for identifying cyber threats against industrial CPSs. Especially, the authors' primary design is a novel DL-based ID method for industrial CPSs, creating utilization of CNN and GRU. Secondary, the authors create a federated learning structure and permit several industrial CPSs to combine a detailed ID method from a privacy-preserving approach. [PERSON] et al. [15]present FID-GAN, a novel fog-based, unsupervised IDS for CPSs employing a generative adversarial network (GAN). The IDS was presented to a fog structure that takes computation resources as near as possible to the end node and so provides for a lesser latency requirement. For achieving superior detection rates, the presented structure estimates a reconstruction loss depending upon the reform of data instances mapped to latent spaces. [PERSON] et al. [16] project a novel AI-enabled multimodal fusion-based IDS (AIMMF-IDS) for CCPS from the Industry 4.0 environments. The presented method primarily executes the data pre-processed approach in two manners such as data conversion and data normalization. Moreover, an improved fish swarm optimization-based FS (IFSO-FS) system is utilized to suitable selective features. The IFSO approach was developed by utilizing a Levy Flight (LF) model as the search process of a typical FSO technique in order to avoid the local optimum problems.  
[PERSON] et al. [17] examine a novel cognitive computing-based IDS approach for achieving security from industrial CPS. The presented method contains pre-processing for discarding the noise which exists from the data. Afterward, the proposed method utilizes a binary bacterial foraging optimization (BBFO) based FS approach for selecting the best subset of features. Additionally, the GRU method was executed for identifying the occurrence of intrusions from the industrial CPS environments. The authors in [18] primarily present a new self-learning spatial distribution technique called Euclidean distance-based between-class learning (EBC learning) that enhances between-class learning by computing the Euclidean distance (ED) amongst KNN of distinct classes. Moreover, a cognitive computing-based ID model termed order-line SMOTE and EBC learning dependent upon RF (BSBC-RF) is also presented as dependent upon EBC learning to industrial CPSs. [PERSON] et al. [19] present a new hybrid technique for intrusion forecast on a CPS's communication network. The authors utilize a bio-simulated hyperparameter searching approach for generating an enhanced DNN infrastructure dependent upon the basic hyperparameters of NNs. In addition, the authors develop a forecasting method dependent upon the enhanced NN infrastructure. Some other methods in the literature are available in [20; 21; 22; 23].