esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 4 DNN-Based Guidance (DNNG) Law
### Simulation Results  
This section shows the simulation results of the DNNG law for various initial conditions. The simulation was performed on clean data without considering noise. The energy performance of the DNNG law was evaluated for 100 random initial conditions. The simulation was performed with an integration step of 0.01 s. In addition, DNNG is compared not only with PNG, but also with the reinforcement learning-based guidance (RLG) law presented in the previous paper [16], and this demonstrates its performance through the energy term. Table 2 shows the average value of the simulation of three missile guidance laws for 100 random initial conditions. Here, \(t_{f}\) is the final time, \(R_{f}\) is the final distance and the energy is the value calculated using Equation (12). The DNNG law derives almost the same results as the PNG, although very small errors can occur in numerical calculations. The simulation results show that DNNG uses less energy than RL-based guidance, and is implied to have a sufficient performance to replace PNG.  
Figures 7 and 8 shows the acceleration and the trajectory of the missile, respectively. The initial conditions are as follows: \(R_{0}=28,516\ (\mathrm{m})\), \(\lambda_{0}=4.0915\ (\mathrm{rad})\), \(\psi_{0}=5.1782\ (\mathrm{rad})\), \(V_{m}=230\ (\mathrm{m/s})\). The simulations of the DNNG and PNG laws show almost the same result. The final distance and the energy performance of the PNG law are 1.1356 m and 1302.3000, and those of the DNNG law are 0.2785 m and 1302.3153, respectively. Both guidance laws were successful in hitting the target by entering the 2 m margin. The simulation result suggests that the DNNG law is able to replace the PNG law.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Guidance Law** & \(t_{f}\) **(s)** & \(R_{f}\) **(m)** & **Energy** \\ \hline PNG & 117.2338 & 0.7323 & 1507.7367 \\ DNNG & 116.2438 & 0.6440 & 1507.7142 \\ RLG [16] & 113.0026 & 0.9789 & 1670.2785 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of three guidance laws for 100 random initial conditions.  
Figure 7: Comparison of the acceleration of PNG and DNNG laws.
## 2 Background and Related Work
### Service-Oriented Architectures in Production Automation  
The semantic discovery and context-based orchestration system described in this article builds upon an architectural approach characterized by the concept of service-oriented architectures in production automation. The state of the art of the application of service-oriented architectures in this field is characterized by the technical realization based on web standards. The two outstanding approaches that dealt with the set-up of service-oriented architectures in production automation arise from the EU-founded research projects SOCRADES and PABADIS'PROMISE. In PABADIS'PROMISE, a control architecture was developed that enables a decision responsibility distribution among acting control entities [14]. The ERP system comprises several classes of business functions that are encapsulated as services. The connection to the agent-based MES system is realized through web services. The SOCRADES consortium developed a web service based communication architecture for the industrial automation domain [15]. The focus was the connection of field devices with high-level control systems like MES and ERP systems. Therefore, the communication technologies DPWS and OPC-UA were used.  
The use of the paradigm of service-oriented architectures in the context of industrial automation systems is intended to significantly decrease the effort for integration and programming of automation components. The two basic aspects of this approach are the standardization of communication interfaces and the functional encapsulation of mechatronic functions that enable a high flexibility and an improved control of complexity for automation systems. The basic functions of the manufacturing equipment that execute and monitor the production process are represented as basic services. These basic services can be aggregated to composed services that control the production process.
# Dombi Aggregation Operators of Neutrosophic Cubic Sets for Multiple Attribute Decision-Making
## 2 Preliminaries
At the same time, the smaller the memberships of indeterminacy and falsity are, the greater the NCN is. As to the accuracy function \(A(x)\), the larger the difference between truth-membership and falsity-membership is, the more effective the statement is. For the certainty function \(C(x)\), if the truth membership is bigger, then the NCN is more certainty. Hence, the score, accuracy and certainty functions are defined as shown above. According to the three functions \(S(x)\), \(A(x)\) and \(C(x)\), the comparison and ranking of two NCNs are defined as following definition. **Definition 6** ([54]). : _Let \(x_{1}=\,\,\left(<\left[T_{1}^{-},\,\,T_{1}^{+}\right]\), \(\left[I_{1}^{-},\,\,I_{1}^{+}\right]\), \(\left[F_{1}^{-},\,\,F_{1}^{+}\right]\)\(>\), \(<\)\(t_{1}\), \(i_{1}\), \(f_{1}\)\(>\)) and \(x_{2}=\,\,\left(<\left[T_{2}^{-},\,\,T_{2}^{+}\right]\), \(\left[I_{2}^{-},\,\,I_{2}^{+}\right]\), \(\left[F_{2}^{-},\,\,F_{2}^{+}\right]\)\(>\), \(<\)\(t_{2}\), \(i_{2}\), \(f_{2}\)\(>\)) be two neutrosophic cubic numbers. Then their ranking method is defined as follows:__If \(S(x_{1})>S(x_{2})\), then \(x_{1}\succ x_{2}\);_  
_If \(S(x_{1})=S(x_{2})\) and \(A(x_{1})>A(x_{2})\), then \(x_{1}\succ x_{2}\);_  
_If \(S(x_{1})=S(x_{2})\), \(A(x_{1})=A(x_{2})\) and \(C(x_{1})>C(x_{2})\), then \(x_{1}\succ x_{2}\);_  
_If \(S(x_{1})=S(x_{2})\), \(A(x_{1})=A(x_{2})\) and \(C(x_{1})=C(x_{2})\), then \(x_{1}\sim x_{2}\)._  
**Example 1. **_Let \(\Psi_{1}\) and \(\Psi_{2}\) be two NCNs._  
_Assume that \(\Psi_{1}\) = (<[0.8, 0.9], [0.1, 0.2], [0.2, 0.3]>, <0.7, 0.1, 0.2>) and \(\Psi_{2}\) = (<[0.5, 0.6], [0.3, 0.4], [0.4, 0.5]>, <0.5, 0.3, 0.4>). Referring to Definition 5, \(S(\Psi_{1})\) = 0.8111, \(S(\Psi_{2})\) = 0.5889, \(A(\Psi_{1})\) = 0.5667, \(A(\Psi_{2})\) = 0.1000, \(C(\Psi_{1})\) = 0.8000, \(C(\Psi_{2})\) = 0.5333. According to Definition 6, \(S(\Psi_{1})>S(\Psi_{2})\), therefore, \(\Psi_{1}\succ\Psi_{2}\)._  
_Assume that \(\Psi_{1}\) = (<[0.5, 0.6], [0.2, 0.3], [0.3, 0.4]>, <0.5, 0.2, 0.3>) and \(\Psi_{2}\) = (<[0.3, 0.4], [0.1, 0.2], [0.2, 0.3]>, <0.3, 0.1, 0.2>). Referring to Definition 5, \(S(\Psi_{1})\) = 0.6556, \(S(\Psi_{2})\) = 0.6556, \(A(\Psi_{1})\) = 0.2000, \(A(\Psi_{2})\) = 0.1000, \(C(\Psi_{1})\) = 0.5333, \(C(\Psi_{2})\) = 0.3333. According to Definition 6, \(S(\Psi_{1})\) = \(S(\Psi_{2})\), \(A(\Psi_{1})>A(\Psi_{2})\), therefore, \(\Psi_{1}\succ\Psi_{2}\)._  
_Assume that \(\Psi_{1}\) = (<[0.5, 0.6], [0.2, 0.3], [0.3, 0.4]>, <0.5, 0.2, 0.3>) and \(\Psi_{2}\) = (<[0.3, 0.4], [0.2, 0.3], [0.1, 0.2]>, <0.3, 0.2, 0.1>). Referring to Definition 5, \(S(\Psi_{1})\) = 0.6556, \(S(\Psi_{2})\) = 0.6556, \(A(\Psi_{1})\) = 0.2000, \(A(\Psi_{2})\) = 0.2000, \(C(\Psi_{1})\) = 0.5333, \(C(\Psi_{2})\) = 0.3333. According to Definition 6, \(S(\Psi_{1})\) = \(S(\Psi_{2})\), \(A(\Psi_{1})\) = \(A(\Psi_{2})\), \(C(\Psi_{1})\) > \(C(\Psi_{2})\) therefore, \(\Psi_{1}\succ\Psi_{2}\)._
## 4 Non-Contact Measurement of CPF via Cough Sounds
### CPF Estimation Model Using Cough Sounds
The cough flow and sound measurement was performed with the participants in a sitting position. The participants wore a face mask with an attached flow sensor. An electret condenser microphone was installed 30 cm from the point where the face mask contacted the face and was attached to the flow sensor. Figure 3: Accuracy of the CPF estimation model using cough sounds. (**a**) Relationship of CPF and CPSL. CPF, cough peak flow; CPSL, cough peak sound pressure level. (**b**) Relationship of CPS and CPF. CPS, cough peak flow predicted via cough sounds. Modified from [PERSON]: Estimation of cough peak flow using cough sounds, Sensors, 2018 [56]. Moreover, the age term improves the CPF estimation accuracy [58]. The sound quality may change with age, but its effect on CPF estimation can be compensated for by adding a proportional age coefficient, as follows:  
\[CPF=(\alpha_{0}+\alpha_{1}\cdot age)\left\{exp\left(\beta\cdot CPSL\right)-1\right\}, \tag{2}\]  
where \(\alpha_{0}\), \(\alpha_{1}\) and \(\beta\) are constants, and \(age\) is the participant's age. The CPF estimated by Equation (2) is called CPS to distinguish it from CPF. Although height [56], body weight and BMI can also affect the estimation accuracy [58], these physical characteristics reportedly have minimal effects on the CPF estimation accuracy [58].
# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 3 Results and Discussions
### Surface Plots
#### 3.2.2 Optimum Impact Toughness  
Similar to the experimental results of understudy CFRP tensile strength, the results of impact toughness (Table 2) were transformed into regression Equation (3) and the surface plots (Figure 10). This equation and the surface plot were used to predict impact toughness within the upper and lower limit of curing parameters (\(T_{c}\), \(t_{c}\), and \(P_{au}\)) of experiments.  
It was observed from the surface plots of Figure 10b that MRCC produced an average impact toughness of 10.9 J. However, the maximum impact toughness of 17.4 J was produced at CC4 (140 \({}^{\circ}\)C, 120 min, 3 bar), as shown in Figure 10a. Unlike the result of the maximum tensile strength, the maximum impact toughness was achieved at a different set of curing parameters. The increased void and resin content of the CFRP matrix at lower \(P_{au}\) (3 bar) was the likely reason for the maximum impact toughness. The increased void content, at lower \(P_{au}\) promotes multiple cracking mechanisms, which raises the impact toughness of CFRP [11; 20; 52]. The higher impact toughness is related to a higher Mode I and II interlaminar crack growth resistance due to multiple crack failure mechanisms as a result of increased void contents at lower \(P_{au}\)[9; 11] This improves the structural integrity and damage tolerance of the material, which is a fundamental requirement for material used in aircraft. As we used a smaller cure \(P_{au}\) value of 3 bar, an increase of 59.6% in impact toughness was observed, as compared to the impact toughness at MRCC. A similar set of values of impact toughness (5-13 J) has been observed for CFRP laminate in the literature [53]. The summary of optimal curing parameters along with optimum impact toughness is provided in Table 6.
## 5 RIS Implementation Structures
### Passive and Active RIS  
RISs can be divided as passive-lossy, passive-lossless, or active based on their energy consumption. Their active or passive nature can determine the performance capabilities of RIS. However, RISs cannot be completely passive due to their inherent property of being configurable. A passive RIS is made up of a large number of passive elements, each of which can controllably reflect the incident signal with a phase shift. Each passive RIS element is made up of a reflecting patch connected to a phase-shifting impedance-adjustable circuit. A passive RIS element consumes little direct-current power due to its passive operating mode, and the thermal noise contributed is likewise low. However, passive RISs only yield a minor capacity boost in many circumstances with strong direct links due to the \"multiplicative fading\" effect. This equivalent path loss of the transmitter-RIS receiver reflection link is the product (instead of the sum) of the path losses of the transmitter-RIS link and RIS-receiver link. Therefore, it is thousands of times larger than that of the unobstructed direct link [89]. Figure 5a,b demonstrates the active and passive RIS structures based on energy consumption.  
Figure 4: Reconfigurable Intelligent Surface (Metasurface): A 4-element metasurface based RIS (Each element is a dynamic meta-surface with numerous tightly packed meta-atoms and can apply an arbitrary quasi-continuous phase gradient).
## 4 Analysis Output  
### Carbonation  
Figure 3 shows the changing phase assemblage as the amount of CO2 increases in 5 g increments. Thermodynamic predictions show the formation of C-S-H, portlandite, ettrinigite, stratlingite, hydrotalcite, and a small amount of calcite in the non-carbonated paste, as seen in previous work by [PERSON] et al. [1] and observed by experimental SEM-EDS and TGA analysis data. The change in solid hydrate volume (separated into major and minor phases for ease of reading) with pH as carbonation continues is shown in Figure 4. As shown, as the pH drops below 13, there is a rapid decrease in C-S-H as portlandite is fully depleted with a corresponding increase in calcite (Figure 4a). There is also a more gradual lowering of ettrinigite abundance, which continues with increasing CO2  
Figure 2: OPC cement predicted hydrate phases (curing temperature = 20 \({}^{\circ}\)C).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Element** & **Cl** & **SO4** & **Na** & **K** & **Mg** & **Ca** \\ \hline Concentration (ppm) & 15,704 & 2254 & 9300 & 360 & 1800 & 490 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Atlantic Ocean seawater analysis used to represent the seawater (taken from [36]).  
concentration and is fully removed at a pH of approximately 10.5. As the decalcification of C-S-H and ettringite continues, the pH drops further and minor amounts of Quartz, M-S-H, gibbsite, and Ferrihydrite are formed at a pH below 12 (Figure 4b) as the calcium in the system is being converted to calcite. The change in C-S-H Ca:Si ratios is presented in Figure 5. The DSPs used here have accounted for the lowering of the Ca:Si ratio. Previous work by the authors [21] has found that a single C-S-H phase would be suitable to model OPC hydration where decalcification did not occur and both portlandite and C-S-H were stable. This further confirms the accuracy of the DSP phase models for C-S-H, and other phases used here, such as M-S-H and siliceous hydrogarnet.  
Previous experimental data [31; 32; 33; 34] from XRD over time during the carbonation of cement pastes have shown the existence of vaterite, which was included in the thermodynamic model using the phase derived above. It does not appear to equilibrate and form a solid phase in the assemblages shown below as calcite is more thermodynamically stable at ambient conditions. We recognize, however, that vaterite is the early, metastable calcium carbonate phase.  
Figure 4: Change in solid phase volume with pH with increasing CO\({}_{2}\) ingress for (**a**) CSH, portlandite, calcite, ettringite and gypsum and (**b**) MSH, ferrihydrite, quartz, straetlingite and gibbsite.  
Figure 3: Phase assemblage with increasing CO\({}_{2}\) (g).
# Investigation of a Gravel-Bed River's Pattern Changes: Insights from Satellite Images
## 2 Materials and Methods
### Methods  
The extraction of information from each image was operated by the help of CAD and QGIS software. According to [PERSON] (1977), in order to minimize the errors in extracting information from the images representing different dates, some control points (such as corner of bridges or of buildings, specific rocks. etc.) clearly distinguishable inside the study area were identified and the images were extracted at the same scale. This was operated also with the help of the AirphotoSE free code for the images' rectification. The existence of some control points enhances significantly Google Earth positional accuracy ([PERSON] et al., 2005). Furthermore, it should be considered that the examined area is around 1 Km\({}^{2}\) and for areas smaller than about 2 km\({}^{2}\), the distortion of the images determines an error usually of the order of a meter or two ([PERSON] et al., 2005). This error is not too far from the error obtained by using other image-based techniques ([PERSON] et al., 2005; [PERSON] et al., 2005).  
The procedure to take information was divided into three steps. In the first step, for each image the external boundary of the \"active\" area of the examined river reach was determined and digitized; thus, the boundary of the largest active area was obtained as the external intersection of the digitized active areas obtained at each time. The second step concerned the extraction of information regarding the active channels and the vegetation patches. To this aim, the study reach was divided into subareas by transients spaced of almost equal longitudinal length. Within each subarea both the flowing channels (i.e., the channels with water present at the image date) and the nonflowing channels (i.e., the channels without water at the image date) and the gravel/sediments islands were identified. Then, according to [PERSON] et al. (1977); [PERSON] et al. (2005), the vegetated areas were determined by aggregating them into islands whose digitized boundaries were identified either by water or by gravel sediments. In the third step, by considering the obtained gravel/sediments islands, the morphological features were identified and their evolution in time was examined. The vegetated islands were classified according to the patch and to the height (_lv_) of the plants, by using the information obtained both from the available images and from the available field photos and from literature ([PERSON] et al., 2005; [PERSON] et al., 2005; [PERSON] et al., 2005). In particular, previous literature works indicated the presence of Populus Nigra as the dominant riparian tree species which strongly resist to unrooting ([PERSON] et al., 2005; [PERSON] et al., 2005) and of abundant willow species (such as alix eleagnos, S. alba, S. purpurea-see ([PERSON] et al., 2005)). Both single bushes and large extent of vegetated islands of trees and piles of woody debris were found by analyzing the field photos.
## 6 Conclusions  
In this study, the expansion force and elastic modulus of polymer grouting materials were tested by means of laboratory materials test. A FE model of high-speed railway train ballastless-track subgrade is established based on ABAQUS. The mechanical characteristics of CRTS III ballastless track under different repair materials, different elevation, and different density of polymer grouting materials are analyzed. The main conclusions are drawn as follows:  
(1) When the cement grouting is used for lifting, the cement slurry layer is in the tension state, and the maximum tensile stress decreases as the height of the lifting increases. However, when the high-polymer grouting is used to repair and lift the settlement of unballasted track, the polymer layer is pressed as a whole, and the maximum compressive stress has no obvious change with the lift height.  
(2) Compared with cement-based mortar materials, polymer materials have good coordinating deformation ability. The stress of the unballasted track structure of CRTSIII high-speed railway after polymer grouting repair is more similar to that of the original unballasted track structure.  
(3) There is no strong correlation between the stress variation of CRTS III ballastless track and the thickness of polymer filling. Within a certain thickness range, increasing the thickness of polymer is beneficial to reducing the difference of stress variation between polymer filling layer and complete pavement. Once beyond this range, the thickness of polymer has little effect on the force variation of CRTS III ballastless track. From the economic point of view, too thick polymer filling layer will increase the cost of disease maintenance.  
Conceptualization, H.F. and F.W.; methodology, Y.S.; software, Y.S.; validation, H.F., Y.S. and X.D.; formal analysis, Y.S.; investigation, B.L.; resources, Y.S.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, X.D.; visualization, X.D.; supervision, X.D.; project administration, H.F.; funding acquisition, H.F.  
This work was supported by the National Key Research and Development Program of China (No.2017 YFC1501204), the National Natural Science Foundation of China (No.51678536, 41404096), the Program for Science and Technology Innovation Talents in Universities of Henan Province (Grant No. 19 HASIT043), the Outstanding Young Talent Research Fund of Zhengzhou University (1621323001) and the Program for Innovative Research Team (in Science and Technology) in University of Henan Province (18 IRTSTHN007. The authors would like to thank for these financial supports.  
The authors declare no conflict of interest.  
Figure 17: Variation of stress at the top of lifting layer under different moduli of elasticity polymer lifting.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
## 2 Hybrid HMM Method
### Hidden Markov Model  
The hidden Markov model is a probabilistic graphical model in which the unobservable (\"hidden\") system state sequence is modeled by Markov chain, and hidden states can be indirectly observed by observation states through some probabilistic distribution. HMM was first proposed by [PERSON] and [PERSON] in 1966 [PERSON] and [PERSON] in 1966 [PERSON] and [PERSON] (1966). HMM and its variants have been widely used in different application areas [PERSON] and [PERSON] (1966).  
A typical HMM model [PERSON] and [PERSON] (1966) can be defined by  
\[\lambda=\{\pi,A,B\}\quad, \tag{1}\]  
where \(\pi\) is the initial probability distribution matrix, \(A\) is the state transition probability matrix and \(B\) is the emission probability matrix or observation probability distribution matrix. \(\pi\), \(A\) and \(B\) are row stochastic matrices.  
In this paper, a long pipeline is divided into several sections and the leakage occurs in a different section that is defined as a different state of the hidden Markov model. The collected stress waves are used to extract the damage indices which serve as observation data. A HMM with three states and three observation states is shown in Figure 1.