esa-sceva/satcom-chunk-collection huggingface dataset:
# Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders
###### Abstract  
This study explores the combined effect of NaCl and gypsum on the strength of the CaO-activated ground-granulated blast furnace slag (GGBFS) binder system. In the CaO-activated GGBFS system, the incorporation of NaCl without gypsum did not improve the strength of the system. However, with the presence of gypsum, the use of NaCl yielded significantly greater strength than the use of either gypsum or NaCl alone. The presence of NaCl largely increases the solubility of gypsum in a solution, leading to a higher concentration of sulfate ions, which is essential for generating more and faster formations of ettringite in a fresh mixture of paste. The significant strength enhancement of gypsum was likely due to the accelerated and increased formation of ettringite, accompanied by more efficient filling of pores in the system.  
CaO-activation; slag; gypsum; NaCl; cementless binder; construction binder Article
## 4 Case Study of a Suspension Bridge
### System Reliability Evaluation  
According to the first failure searching procedure, the components that are likely to fail were H20 and H44. Since the failure mode of suspenders is brittle failure, the failed cable should be directly removed from the structural system. Subsequently, the structural system was updated and approximated by the DBNs. At the second stage, the components that were likely to fail were stiffening girders associated with G19, G21, G43, and G45. Since the failure mode of girders is plastic failure, a hinge should be added at the node between two girder elements. At the third failure stage, the main cable is the most likely to fail component associated with C18, C20, C22, C42, C44, and C46. Consequently, the three-level fault tree of the bridge structural system is constructed, as shown in Figure 14, where \(\beta_{c}\) is the conditional reliability index of the component corresponding to each failure stage.  
Subsequently, the system reliability of the bridge was estimated through the series-parallel connection criterion. The corresponding series-parallel relationship is presented in Figure 15. It can be observed that the system reliability index was 9.63 by considering the three-level fault tree.  
According to the standard for structural reliability of highway engineering in China, a bridge controlled by brittle failure should have a reliability index greater than 5.2. Therefore, the bridge used in the present study has an adequate safety factor during its lifetime. Nevertheless, fatigue-corrosion damage is the main factor that leads to of bridge safety degradation. In future studies, th cable strength should be determined and updated in the system reliability evaluation.  
Cable damage due to the fatigue-corrosion effect is a critical factor that may lead to service life-shortening, safety threats, and increasing life-cycle cost. Thus, the effect of cable damage on the system reliability of suspension bridge deserves investigation. According to the cable strength degradation model shown in Figure 3, the system reliability of a bridge is adaptively updated. Figure 16  
Figure 14: Fault tree of the suspension bridge.  
shows the degradation curve of the system reliability of the suspension bridge based on the service lifetime of the suspender.  
It can be observed that cable degradation due to fatigue damage leads to a slow reduction of the system reliability index from 9.63 to 7.22. However, the combined effect of fatigue-corrosion damage leads to a relatively sharp decrease of the system reliability index from 9.63 to 4.41. Therefore, cable corrosion is the dominant factor affecting bridge safety. In addition, given the target reliability index of 5.2, the suspension bridge has sufficient safety capacity accounting for cable fatigue damage. On the other hand, considering the fatigue-corrosion damage of suspenders, the maintenance time for cable replacement should be 26 years.
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 1 Introduction
Compared with the traditional SPGD algorithm, this algorithm has a significant improvement in convergence speed and system stability. However, this optimization technique requires prior knowledge of the system's performance metrics, which limits the kinds of applications available. In order to make up for the shortcomings of this algorithm, our laboratory proposed an adaptive stochastic parallel gradient descent (AdaSPGD) optimization algorithm, based on performance index changes in previous practice [42]. Initially, adaptive gain coefficients were first added to the algorithm to compensate phase errors more quickly [43], but this required very complex calculations. Subsequently, it is applied to the adaptive gradient descent (AdaGrad) algorithm [44] and the problem of straight convexity in deep learning [45; 46]. This paper presents an AdaDelSPGD algorithm which can change the adaptive gain coefficient. This algorithm improves the SPGD algorithm to improve the convergence speed of coherent synthesis. Section 2 describes the basic principle of the SPGD algorithm in phase-locked system of fiber laser-phased array, and proposes an AdaDelSPGD algorithm. The convergence speed and convergence effect of the two algorithms in N-channel fiber laser-phased array system are illustrated in detail by simulation. In Section 3, we test the spot contrast and main lobe energy concentration of fiber laser-phased array multiplex communication system after adding two algorithms, and we describe the difference in the optical power value and bit error rate (BER) of the system before and after adding the AdaDelSPGD algorithm. Finally, conclusions are provided in Section 4.
# Navigation Scenario Permutation Model for Training of
###### Abstract  
The development of autonomous ships has begun. Artificial intelligence (AI) is expected to be partially responsible for navigation; nevertheless, the importance of human intervention is higher than ever. Human intervention in the control of an autonomous ship via the remote operator requires navigation proficiency. The education method for the remote operators that is presently considered is simulation training. However, the simulation training does not take long enough time for enabling trainees to develop their navigation proficiency equivalent to that of conventional ships navigators. In addition, the simulation training should contain various navigation scenarios to train the traine properly. Therefore, this paper suggests the methods to generate the massive and practical navigation scenarios by extracting navigation elements' distribution from actual ship trajectory data and applying them to the permutation of navigation elements. The results demonstrated the advantages of the proposed methods by comparing the sample navigation scenario and an example of an impractical navigation scenario. In conclusion, it is expected that the massive generation of practical navigation scenarios using the proposed permutation model will positively affect the simulation training of the maritime autonomous surface ship remote operators.  
autonomous ship; remote operator; simulation training; distribution; practical navigation scenario
## 3 Results and Discussion
### Quantitative Analysis
Figure 5: (**a**) Streamwise propagation velocity of the HNS for the input variables and (**b**) max propagation distance in the streamwise direction between the case of the side crack and bottom crack. geometrical effect induced by the ship shape. For the bottom crack, the spilled HNS flows along the bottom surface of the ship. Then, it moves upward and reaches sea level. Figure 7a shows the effect of the current velocity on the averaged HNS mass fraction at sea level, \(w_{s-l}\), according to the HNS density. As expected, \(w_{s-l}\) decreases as the current velocity increases, showing faster propagation. The mass fraction at sea level increases with HNS density. For example, when \(v_{c}\) is 1.028 m/s, \(w_{s-l}\) increases by approximately 30%. This is because a lower HNS density causes faster HNS propagation velocity in the transverse direction, resulting in wider area occupied by the HNS. Thus, the heavier the HNS, the wider the area it occupies at sea level. In addition, the influence of the crack location on the mass fraction becomes negligible as the current velocity increases.
## 2 Materials and Methods
### Dual-Channel Parallel Oscillating Mirror Interferometer  
#### 2.3.1 Basic Principle and Components  
The principle of the parallel oscillating mirror interferometer module is shown in Figure 3, including a collimator L1, a beam splitter BS, three plane reflecting mirrors M1, M2 and M3, two end mirrors M4 and M5, an imaging mirror L2 and a detector D. The upper and lower faces of mirror M2 are reflective and parallel to each other. Mirror M1 and mirror M3 are parallel to mirror M2, and M1 and M3 are symmetric about mirror M2. The part in the dotted line in Figure 3 is the only moving part in the interferometer, which can swing around the rotation axis perpendicular to the paper (z direction). The two end mirrors M4 and M5 are perpendicular to the direction of the incident light.  
The propagation path of light in the interferometer is as follows: the light carrying the measured information is collimated using the collimator L1 and then enters the interferometer as parallel light. The beam splitter BS divides it into transmitted light and reflected light. The reflected light, after being reflected by mirrors M1 and M2, is incident on the end mirror M4, and after being reflected by M4, it returns to the beam splitter BS again. Similarly, the transmitted light is incident on the end mirror M5 after being reflected by the mirrors M3 and M2 and returns to the beam splitter BS after being reflected by M5. The two beams of light merge at the beam splitter and form a beam of coherent light, which converges on the detector D through the imaging mirror L2.  
The optical path difference (\(OPD\)) of the reflected and transmitted light varies with time as the mirror swings. The relationship between the \(OPD\) and the distance \(h\) between parallel mirrors and the incident angle \(\alpha\) is shown in Figure 4. The light path distance \(l\) is equal to \(2(OB+BC+CD)\). The vertical line of ray OB crossing point C intersects ray OB at point F, and the extension line of ray OB intersects mirror M4 at point E. Since the incident ray OB is perpendicular to the reflector M4, CD = FE, and the optical path of the system can be written as \(2(OE+FB+BC)\), that is:  
\[l=\frac{2h}{cos\alpha}(1+cos2\alpha)+2 OE=4h\cos\ \alpha+2 OE \tag{7}\]  
If M4 and M5 are symmetric with respect to BS, then the \(OPD\) is:  
\[OPD=4h(cos\alpha_{1}-cos\alpha_{2}) \tag{8}\]  
where \(\alpha_{1}\) and \(\alpha_{2}\) are the incidence angles of the ray incident on M1 and M3, respectively, and vary with the swing angle \(\omega\). The interference intensity that changes with \(OPD\) is collected on the detector. After recording the complete interference data, the spectral data can be restored through Fourier transform.
## 2 Related Works
### 5G Services and Their Economic Value
#### 2.1.1 5G as Optimal Communication Channel for IoT  
5G mobile technology will enable new value chains in many economic sectors. Those using machine-to-machine communications are among them [4]. 5G networks will provide performance enhancements, a high degree of reliability and very low latency communications [33]. The IoT segment with the heaviest economic weight is Smart Home Technologies with a projected market volume of USD 17 billion in 2021 [13]. Smart Home IoT devices are mostly connected to the Internet via Wi-Fi connections with mobile as a redundant backup communication channel. However, IoT devices in remote places and in places with no Wi-Fi coverage make use of mobile links to exchange data with their edge and cloud servers. The arrival of 5G, although at a later stage than, e.g., person-to-person mobile communications [20], will bring better energy efficiency, reliability and performance.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 1 Introduction
Motivated by the observation that the texture is defined as a set of repeated primitives along the whole picture, we propose using a deep learning method that uses a ResNet50 [5] pre-trained model on a massive dataset incorporating different blocks that embed global spatial information to capture channel interdependencies and long-range dependencies to classify clothes pattern into six categories: solid, striped, checkered, dotted, zigzag, and floral. Two methods for voting are evaluated for the final prediction by the combined learners: max voting and entropy voting. Moreover, different models are evaluated in ensemble learning, such as pre-trained DenseNet, EfficientNet, and ResNet152 models. Two popular methods are employed to reduce overfitting: data augmentation and dropout. Figure 1: Examples of six classes of clothes patterns: checkered, floral, dotted, solid, striped, and zigzag. The main contributions of the presented research work are as follows:  
* A deep learning-based ensemble classifier is created that uses CNN models as base learners and entropy voting as a fusion technique to recognize the clothes patterns. To tackle the problem of small datasets and overfitting, the pre-trained CNN models are adopted as base learners, which are fine-tuned to the clothes patterns. * A simple and robust entropy voting-based fusion method is presented that fuses the decisions taken by the base learners, taking into consideration their uncertainties in decision making. * The method is validated thoroughly using benchmark datasets for clothes pattern recognition. The rest of the paper is organized as follows. Section 2 gives an overview of the state-of-the-art method proposed for clothes patterns. The proposed method is presented in Section 3. Experimental results are given in Section 4 and discussed in Section 5. Finally, Section 6 concludes the paper.
# Decision Problem on Imperfect Inspections Combined under Two-Stage Inspection Policy
## 5 Discussion  
The analysis of the proposed model of the two-stage inspection policy with an imperfect second stage confirms some findings from the literature shown in the literature review section. It proves that the two-stage inspection policy is always better than the one-stage policy (Figure 3) if decision variables of the two-stage policy are properly chosen. Moreover, Figure 4 proves that in the presented case study the most profitable results are achieved if the second stage of the inspection is imperfect but cheaper than the perfect one. It shows the possible predominance of the two-stage policy with an imperfect second stage in comparison to the policies considered in the literature, which assume perfection of the inspection's second stage. However, when one wants to apply the two-stage inspection policy in a system, a few questions should be answered: (1) whether it is profitable to use the one- or the two-stage policy, (2) which testing methods should be chosen to apply them alone or in combination, and (3) what should be the length of an inspection period for chosen methods. The optimization process of the proposed complex model is not an easy task and is impossible without applying heuristic methods due to the huge number of possible combinations of policy variables. However, one can use the dependencies shown in the previous paragraph of the paper to search for the most economical solutions of the two-stage inspection policy. To extend the conclusions from the case study, the expressions are given below which allow one to estimate change in expected maintenance costs resulting from applying the two-stage inspection policy instead the one-stage policy. If one compares the one- and two-stage inspection policies, the two-stage policy is more resistant to false positive mistakes than the one-stage policy for a given inspection period if \(\alpha_{n\_II\_1}\cdot\alpha_{n\_II\_2}<\alpha_{n\_II\_1}\). On one hand, its application prevents a system from unnecessary replacement; on the other hand, however, it is the reason for the probability of a system failure during the \(i\)-th inspection period to be higher for the two-stage inspection policy in comparison to the one-stage policy. Assuming \(n\_I\_1=n\_II\_1\), this increase for a given inspection interval \(T\) is equal:  
\[ \begin{array}{l}\Delta F(t_{i},T,n\_I\_1,n\_II\_1,n\_II\_2)=F_{II}(t_{i},T,n \_II\_1,n\_II\_2)-F_{I}(t_{i},T,n\_I\_1)=\\ \sum_{z=1}^{z=i}\int_{t_{z-1}}^{t_{z}}f_{U}(x)\cdot[F_{H}(t_{i}-x)-F_{H}(max \{t_{i-1}-x,0\})]dx\cdot\beta_{n\_I\_1}^{i-z}\cdot\left[(1-\alpha_{n\_II\_1} \cdot\alpha_{n\_II\_2})^{z-1}-(1-\alpha_{n\_I\_1})^{z-1}\right]\end{array}  \tag{20}\]  
Thus, the two-stage inspection policy makes an increase in the expected number of system failures within a renewal cycle, which is equal:  
\[ \begin{array}{l}\Delta NF(T,n\_I\_1,n\_II\_1,n\_II\_2)=NF_{II}(T,n\_II\_1,n \_II\_2)-NF_{I}(T,n\_I\_1)\\ =\sum_{i=1}^{\infty}\Delta F(t_{i},T,n\_I\_1,n\_II\_1,n\_II\_2)\end{array}
## Appendix A Gradient: Divergence, and Curl in Cylindrical Coordinates
### Divergence in Cylindrical Coordinates  
It is similar to analyzing _divergence_ in Cartesian coordinates; the _divergence_ in Cartesian coordinates is carried out by computing the dot product of _nabla_ and vector field \(U(\rho,\phi,z)\) That is,  
\[\begin{split}\
abla\cdot U&=\left(\hat{\rho}\frac{ \partial}{\partial\rho}+\frac{\hat{\phi}}{\rho}\frac{\partial}{\partial\phi}+ \hat{z}\frac{\partial}{\partial z}\right)\left(U_{\mu}\hat{\rho}+U_{\rho}\hat{ \phi}+U_{z}\hat{z}\right)\\ &=\hat{\rho}\left(\hat{\rho}\frac{\partial U_{\rho}}{\partial\rho} +U_{\rho}\frac{\partial\hat{\rho}}{\partial\rho}+\hat{\phi}\frac{\partial U_{ \phi}}{\partial\rho}+U_{\phi}\frac{\partial\hat{\phi}}{\partial\rho}+\hat{z} \frac{\partial U_{z}}{\partial\rho}+U_{z}\frac{\partial\hat{z}}{\partial\rho} \right)\\ &+\frac{\hat{\phi}}{\rho}\left(\hat{\rho}\frac{\partial U_{\rho}} {\partial\phi}+U_{\rho}\frac{\partial\hat{\phi}}{\partial\phi}+\hat{\phi} \frac{\partial U_{\phi}}{\partial\phi}+U_{\phi}\frac{\partial\hat{\phi}}{ \partial\phi}+\hat{z}\frac{\partial U_{z}}{\partial\phi}+U_{z}\frac{\partial \hat{z}}{\partial\phi}\right)\\ &+\hat{z}\left(\hat{\rho}\frac{\partial U_{\rho}}{\partial z} +U_{\rho}\frac{\partial\hat{\rho}}{\partial z}+\hat{\phi}\frac{\partial U_{ \phi}}{\partial z}+U_{\phi}\frac{\partial\hat{\phi}}{\partial z}+\hat{z}\frac{ \partial U_{z}}{\partial z}+U_{z}\frac{\partial\hat{z}}{\partial z}\right) \end{split} \tag{13}\]  
Since \(\hat{\rho}\), \(\hat{\phi}\), and \(\hat{z}\) are orthogonal unit vectors, the inner product between each two is 0 (self-inner product equals to 1). Together with Equations (10) and (11), we have  
\[\begin{split}\
abla\cdot U&=\hat{\rho}\left(\hat{ \rho}\frac{\partial U_{\rho}}{\partial\rho}+0+\hat{\phi}\frac{\partial U_{ \phi}}{\partial\rho}+0+\hat{z}\frac{\partial U_{z}}{\partial\rho}+0\right)\\ &+\frac{\hat{\phi}}{\rho}\left(\hat{\rho}\frac{\partial U_{\rho}} {\partial\phi}+U_{\rho}\hat{\phi}+\hat{\phi}\frac{\partial U_{\phi}}{\partial \phi}-U_{\phi}\hat{\rho}+\hat{z}\frac{\partial U_{z}}{\partial\phi}+0\right) \\ &+\hat{z}\left(\hat{\rho}\frac{\partial U_{\rho}}{\partial\phi}+0 +\hat{\phi}\frac{\partial U_{\phi}}{\partial z}+0+2\frac{\partial U_{z}}{ \partial z}+0\right)\\ &=\left(\frac{\partial U_{\rho}}{\partial\rho}+\frac{U_{\rho}}{ \rho}\right)+\frac{1}{\rho}\frac{\partial U_{\phi}}{\partial\phi}+\frac{ \partial U_{z}}{\partial z}\\ &=\frac{1}{\rho}\frac{\partial}{\partial\rho}\left(U_{\rho}\cdot \rho\right)+\frac{1}{\rho}\frac{\partial U_{\phi}}{\partial\phi}+\frac{ \partial U_{z}}{\partial z}\end{split} \tag{14}\]
## 1 Introduction
Thus, high gains can be maintained for highly-mobile UAVs. Furthermore, instead of requiring a shorter (\(t_{p2}-t_{p1}\)), the proposed scheme can simply generate more predictions to maintain accurate DoA information, resulting in low training overhead. Simulation experiments carried out to verify the proposed method are presented. Compared with the conventional DoA-based beamforming scheme using an improved variant of the MUSIC algorithm [17], the proposed method yields more accurate DoA estimations and higher gains. The performance margin is especially remarkable when the UAV is flying fast or the DoA estimation interval is large. Besides, simulation experiments also suggests that applying the proposed scheme can reduce up to 100 pilot signal transmissions per second. The rest of the paper is organized as follows: Section 2 describes the sources of DoA errors and their impact on beamforming methods. Section 3 introduces the proposed algorithm and the method for the real-time steering vector update beamforming scheme. Simulation experiments and results are given in Section 4 to verify their performance. Section 5 concludes this paper. Notations: We employ boldface for vectors and bold capital letters for matrices; the superscript \((\cdot)^{\text{T}}\) denotes the transpose of a vector or matrix; the superscript \((\cdot)^{\text{H}}\) denotes the Hermitian transpose of a vector or matrix; the superscript \((\cdot)^{-1}\) denotes the inverse of a matrix; **I** is the identity matrix; \(\exp(x)\) denotes exponentia; \(|\cdot|\) denotes absolute value; the superscript \((\cdot)^{\circ}\) denotes angle in degrees. The acronym list is shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Explanation** \\ \hline UAV & unmanned aerial vehicle \\ DoA & direction of arrival \\ BS & base station \\ aUE & aerial user equipment \\ Lo6 & line-of-sight \\ CSI & channel state information \\ SNR & signal-to-noise ratio \\ SINR & signal-to-interference-plus-noise ratio \\ SIR & signal-to-interference ratio \\ MUSIC & multiple signal classification \\ FSI & flight status information \\ DL & diagonal loading \\ LCMV & linearly constrained minimum variance \\ GPS & global positioning system \\ IMU & inertial measurement unit \\ QoS & quality of service \\ NED & north-east-down \\ ECEF & earth-centered-earth-fixed \\
2-D & 2-dimensional \\
3-D & 3-dimensional \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronym list.