esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Performance of Foam Concrete Prepared from Decarburized Fly Ash
## 3 Results and Discussions
### Thermal Insulation Performance  
This test measures the thermal insulation performance of foam concrete by testing for thermal conductivity, and the measurement results are shown in Figure 9a. From the figure, it can be seen that with the increase in DFA admixture and water-to-cement ratio, the thermal conductivity of DFA decreases continuously and the thermal insulation performance improves gradually.  
The main reason for such phenomena is the increase in porosity, which we usually consider as decreasing the thermal conductivity of concrete. This is because the thermal conductivity of air is much lower than that of cement paste, and the addition of DFA will increase the porosity and pore size inside the foam concrete. Taking group H2 as an example, the porosity of the foam concrete increased from 75.3% to 81.7% when the DFA admixture increased from 0% to 25%, while the thermal conductivity decreased from 0.0977 W/(m\(\cdot\)k) to 0.0851 W/(m\(\cdot\)k). With the same content of DFA, the increase in the water-to-cement ratio also leads to the formation of more pores inside the dried concrete, increasing the content of internal air. This eventually leads to a decrease in the thermal conductivity of the foam concrete.  
Figure 9b collects the data from some researchers on the thermal conductivity of foam concrete [3, 26, 27, 28, 29] and makes a comparison with the foam concrete prepared in this test. It can be seen from the figure that the thermal conductivity of the foam concrete prepared in this test ranged between 0.0796 and0.1077 W/(m\(\cdot\)k). With the exception of the foam concrete prepared using super-insulating nano-multi-air gel, the thermal conductivity of all other foam concretes was higher than that of the foam concrete prepared in this test. Meanwhile, according to JG/T 266-2011 \"Foam Concrete\", all the 18 groups of foam concrete prepared in this test meet the requirements of A05 grade foam concrete.  
Figure 8: The 28d compressive strength of foam concrete with different matching ratios.
## 6 Challenges and Future Directions
### Deployment Issues  
The deployment of private 5G networks in manufacturing environments requires careful investigation, as traditional 5G deployment approaches may not suit the unique communication requirements of factory floors or processing plants. Hence, revisiting and rethinking base station placement policies is essential to tailor private 5G networks for diverse use cases in smart manufacturing. On the other hand, upgrading to a new WiFi standard may necessitate the replacement or upgrading of old devices to support the new standard. Maintaining backward compatibility with older WiFi protocols can also be difficult. As the number of WiFi devices grows, the available unlicensed spectrum may become congested, resulting in performance loss. Effective spectrum management and coexistence strategies will be critical for WiFi 7 implementation [69]. Interference from other devices or neighboring networks operating on the same or surrounding channels might degrade WiFi signals. For optimal implementation, proper channel allocation and interference mitigation strategies will be required [70]. All of this requires novel research in deployment approaches for nontraditional wireless environments and applications.  
TSN necessitates careful network device configuration and management to achieve accurate time synchronization and prioritization of time-sensitive traffic. This can be difficult, particularly with large-scale installations. TSN is based on standardized protocols and network device settings. During deployment, ensuring interoperability across different vendors' equipment, resource allocation, and scheduling approaches can be difficult. TSN accommodates many forms of traffic on the same network infrastructure, including time-sensitive and standard Ethernet traffic. It can be difficult to achieve efficient network convergence while adhering to rigorous time requirements during deployment. Integrating TSN into existing networks and legacy systems might be difficult since it may necessitate equipment modifications or replacements to support TSN's time-sensitive features. Research to discover novel methods for TSN network configuration and deployment is required.
## 2 Modeling Approach  
Two common approaches exist for the numerical modeling of multiphase flows. One is Eulerian-Lagrangian method. The other is the Eulerian-Eulerian approach. In the first method, the dispersed phase is considered as an independent discrete phase. In order to solve the dispersed phase, a large number of particles, bubbles, or droplets must be calculated through the computed flow field. Furthermore, momentum, mass, and energy exchanges between the dispersed and fluid phases can be done effectively. On the contrary, the second method considers both phases (fluid and dispersed) as two continuous phases that continuously penetrate to each other. For each phase (not for each one of bubbles, droplets, or particles in the dispersed phase), governing equations are made up of a set of relationships that are uniform in structure for all phases [24]. Since each particle, bubble, or droplet is tracked in a Lagrangian framework, the Eulerian-Lagrangian approach is computationally intensive. In addition, in this approach, owing to the convergence problem at higher hold-ups, the secondary phase should not exceed ten percent of the total volumetric flow rate. In the majority of cases, the volume fraction of the dispersed phase is more than ten percent; therefore, the more common model in CFD is based on the Eulerian-Eulerian approach. In this study, a three- and two-dimensional modeling with considering Eulerian-Eulerian approach is implemented, in which gas phase/liquid phase for 3D and 2D simulation are air/water and CO\({}_{2}\) + air/MEA solution, respectively. The liquid phase is considered as a continuous phase, but the gas phase is regarded as a dispersed fluid phase with a constant bubble diameter (1 mm). The turbulent behavior of the flow was modeled using a standard k-\(\epsilon\) model. Some assumptions are made in this simulation: (1) the flow conditions are isothermal (25 \({}^{\circ}\)C) and (2) the virtual mass force is neglected.
## 3 Synthesis and Analysis of Planar SALT Trajectories
### The Quadrant Rule
So, using simple considerations, we have proven analytically the well-known quadrant rule observed by researchers when numerically designing SALT trajectories. Note that so far we did not assume a Sun-assisted transfer trajectory to be planar. The quadrant rule will be useful for us later for the purpose of analyzing the features of designed SALT trajectories.
# Dynamic Modeling of McKibben Muscle Using Empirical Model and Particle Swarm Optimization Method
## 5 Empirical Modeling of the McKibben Muscle
### Contracting Force Model  
The static modeling of hysteresis was used to configure the contracting force of the McKibben muscle with various pressures and contracting lengths. Several modifications on empirical modeling have been suggested after it was first introduced by [PERSON] in 1988 [7]. The first modification model included an empirical constant \(k\) to account for end deformation of the muscle as suggested by [PERSON] in 1994, as shown in Equations (1) and (2) [8].  
\[F_{s}(P,\ \varepsilon)=\frac{\pi{D_{o}}^{2}P}{4}\Bigg{(}\frac{3(1-k\varepsilon)^{ 2}}{tan^{2}\alpha_{o}}-\frac{1}{sin^{2}\alpha_{o}}\Bigg{)} \tag{1}\]  
\[k=c_{1} \tag{2}\]  
The second modification model included a shape irregularity correction function \(q\) which tunes the slope of the considered static contracting force model as suggested by [PERSON] in 2000, [PERSON] in 2009, and [PERSON] in 2011 [17; 18; 25]. The shape irregularity was defined as an exponential function of input pressure as shown in Equations (3) and (4).  
\[F_{s}(P,\ \varepsilon)=\frac{\pi{D_{o}}^{2}P}{4}\Bigg{(}\frac{3(1-q(P)\varepsilon )^{2}}{tan^{2}\alpha_{o}}-\frac{1}{sin^{2}\alpha_{o}}\Bigg{)} \tag{3}\]  
\[q(P)=c_{1}+c_{2}e^{c_{3}P} \tag{4}\]  
The third modification model included both correction functions of shape irregularity \(q\) and slenderness \(\mu\) as suggested by [PERSON] in 2006 [13]. The slenderness was defined as an exponential function of contracting length as shown in Equations (5)-(7). Its implementation also considers the various length of McKibben muscle; where the contracting force value will significantly increase with increment in McKibben muscle's initial length.  
\[F_{s}(P,\ \varepsilon)=\mu(\varepsilon)\frac{\pi{D_{o}}^{2}P}{4}\Bigg{(}\frac{3(1 -q(P)\varepsilon)^{2}}{tan^{2}\alpha_{o}}-\frac{1}{sin^{2}\alpha_{o}}\Bigg{)} \tag{5}\]  
\[q(P)=1+c_{1}e^{c_{2}P} \tag{6}\]  
\[\mu(\varepsilon)=c_{3}e^{c_{4}\varepsilon}+c_{5} \tag{7}\]
## 2 Data Augmentation Using GANs  
Training a multilayer CNNs using a limited number of data sets results in overfitting. To avoid overfitting data augmentation is a technique used to increase the amount of data in training processing. Typical data augmentation techniques include cropping, flipping, rotation, and translation, yet those methods are lacking data diversity. GAN, an innovation network proposed in 2014, was applied to generate new data automatically. It trains the generator and discriminator networks simultaneously. The former generates new images, and the latter learns to distinguish the fake images from the input of real and generated data. The two networks continually generate and discriminate tasks and constantly update the parameters of the network. Finally, the training process terminates when the generator network deceives the discriminator network. Figure 1 shows the flowchart of GANs.  
In the present study, DCGAN was applied for data augmentation which the discriminator network uses convolutional stride for downsampling and the generator network uses transposed convolution for upsampling. The details of DCGAN are described as follows.
# Detection of Broken Bars in Induction Motors Using Histogram Analysis of Current Signals
## 1 Introduction
After, a histogram is calculated to highlight its non-stationary frequencies, aiming to obtain the kurtosis. It is used to classify half, one, two BRBs, and healthy conditions with a normal distribution with 100% efficiency. A histogram of oriented gradients (HOG) is an essential approach to fault detection. In [21], three stator currents were transformed into three images, and the HOG was used to extract interesting features of a healthy state, bearing faults, BRB faults, short-circuit faults, and their combinations. A multi-layer artificial neural network (ANN) was trained to classify, achieving 95% accuracy. Similarly, in [22], the stator current was used to extract intensity gradients and edge directions to classify healthy motor and one to four BRB faults in the time domain. The features were calculated through HOG and used to train an ANN, obtaining an accuracy of 96%. It is noticeable that the scientific community has increased its interest in induction motor fault monitoring due to its essential role in the industry. The choice of motor diagnosis through the analysis of current in the time allows for the design of feasible and cheap sensors to acquire the signal of the motor. In this context, this article proposes a low-complexity and statistical approach to identify the operating condition of induction motors by evaluating texture features based on SDHs. The current from the induction motor is used to calculate the SDH and the nine texture attributes evaluating two different sets of displacements, called \(\Delta\). Both displacement sets are used to train and test the decision tree classifier. The classifier is trained to detect one and two BRB and health conditions to find the best displacements that perform best. Based on the classification results, the best \(\Delta\) displacements are used to train and test two more classifiers: the bagged decision tree and the K-nearest neighbor KNN. From this, the texture feature with the highest performance is finally chosen. A polynomial regression curve shows the best trade-off between samples and performance. The rest of the paper is organized in the following order. Section 2 presents the methodology based on histograms of sums and differences and deeply analyses the displacements for texture calculation. Then, three conducted experimental tests of the classification results obtained and a comparison with related works are described in Section 3. Lastly, the discussion and conclusions are presented in Section 4.
## 4 Performance Analysis of the MA-SM
### Performance Analysis of MA-SM with Maximum-Likelihood Receiver
Based on the depicted graphs, we can draw the same conclusion of PSK modulation that the error probability will be dominated by the terms that do not depend on the rotation angles. Therefore, the rotation angles would have negligible effect on the error performance. Table 3 presents a comparison between the value of the cost function in (21) using the optimal rotation angles suggested in [20] and the conventional system without rotation for 16 and 64-QAM schemes. For both scenarios, the optimal rotation angles minimize the cost function.
## 3 Results
### Investigation in Field Measurement
The distribution of signals in areas \(U\), \(W\), \(X\), \(Y\) (Figure 14) is consistent with the corresponding areas \(U1\), \(W1\), \(X1\), \(Y1\) (Figure 15), while signals in lower part of area \(V\) are not as intensive as the area \(V1\). It is noticeable that comparing with the pre-processed profile (Figure 11), the noisy signals in area \(Y\) nearly disappear, which implies the high surrounding rock quality in this area. Although \(W\) and \(X\) are concentrated areas of fragmented rocks, the lower part of \(W\) presents higher rock quality after EEMD processing and the area \(X\) is not as cracked as shown in Figure 11. These results indicate that the developed approach has the potential to discover the subterranean joint fissures to further identify the surrounding rock stability. Figure 13: (**a**) IMFs and residue of Line 1 signals from the cavern detection decomposed by EEMD. (**b**) IMFs and residue of Line 2 signals from the cavern detection decomposed by EEMD. All the mode features are marked in dotted circles.
## 4 Experimental Study of the CAFB Using a Model Bridge  
In Sections 2 and 3 of this paper, a CAFB was developed as the SHM of large construction structures, designed optimally using the El-Centro earthquake waveform and a wireless sensing system comprised of embedding it within the IDAQ system. In this section, the response performance of the civil structure is evaluated using the CAFB-based IDAQ system developed previously. To do that, a model bridge of the Seohae Grand Bridge in Korea, at a scale of 1/20, was designed and produced so that the targeted three low-level bending modes became less than 10 Hz to reflect the flexible dynamic characteristics of the bridge. To secure the relevance of the test value prior to the response analysis of the bridge, detailed FE (finite element) modeling and analysis were performed, and during the response test, the accuracy of the wireless response was evaluated using an IDAQ system based on the response acquired by an existing wired sensing system.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 4 Performance Evaluation
### Carrier Phase Error/Code Phase Error Step Scene  
In test scene A, the \(C/N_{0}\) of the GNSS signal was set to 45 dB-Hz. There was a step input for carrier phase error at 1500 milliseconds (ms), and for code phase error at 2000 ms. \(T_{\text{coh}}\) was set to 1 ms, \(d\) set to 1 chip. This test was used to evaluate the performance of the ATAN/ATAN2/NC-EMLE discriminator and pre-filter in normal condition.  
Figure 11 shows the tracking error estimation results in test scene A. The black line represents the real tracking error when we simulated I/Q correlation groups. As expected, the coherent/non-coherent pre-filter were able to estimate the carrier phase error and code phase error accurately. The convergence time of pre-filter was less than 200 ms when step-input occurred. The estimations of pre-filter were less noisy than that of the ATAN/ATAN2/NC-EMLE discriminator. The estimation accuracy of the coherent pre-filter for tracking error was slightly higher than that of the non-coherent pre-filter, as shown in Table 2.  
Figure 11: (**a**) The carrier phase error estimation in test scene A; (**b**) The code phase error estimation in test scene A.
# Liquid Crystal Tunable Dielectric Metamaterial Absorber in the Terahertz Range
Institute for Smart Liquid Crystals, JITRI, Changshu 215500, China 1  
Zhixiong Shen  
1 Key Laboratory of Intelligent Optical Sensing and Manipulation, College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China; [EMAIL_ADDRESS] (Z.  
metadevices have been demonstrated, such as active filters [15; 16], polarization converters [17], and phase shifters [18]. Here, both the cell gap and the operating voltage are drastically decreased. Through precisely designing the geometry of metadevices and optimizing the matching between the refractive index change of LC and electromagnetic resonant boundary condition, a dynamic tuning with large modulation depth can be reached. Here, we propose a tunable THz dielectric MMA, which is composed of a silicon pillar array embedded in an LC layer driven by graphene electrodes. The function of this absorber relies on the overlapping between electric and magnetic dipole resonances of the silicon pillar array. We precisely set the radius of the pillar to satisfy the boundary condition at the bias saturated state, thus it behaves as a perfect absorber. While turning off the bias, the overlapped resonances are broken due to the refractive index change of LC. As a result, the absorption peak is split and its amplitude drops to half of the original one.
## 5 Proposed Simulated Annealing-Based Procedure  
Due to its computational complexity, the MALSP is strongly NP-hard, and the mathematical programming model previously described in Section 4 can solve only small instances of the problem. In contrast, medium- and large-size scales of the MALSP need heuristic-based procedures to be handled. Several meta-heuristic procedures could be adopted to define a feasible and good solution to this optimization model. Among the most promising ones, it is necessary to mention ant colony, genetic, and simulated annealing algorithms. All these possibilities are distinguished by strengths and weaknesses that should be carefully assessed before deciding which to adopt. Considering the features of the developed MALBP, it is of major importance that the selected meta-heuristic algorithm offers the opportunity to quickly converge to feasible and good solutions for the targeted problem. A remarkable advantage of simulated annealing (SA) in terms of its usage to solve this MALBP is represented by the fact that all the intermediate solutions defined in its following steps of the SA procedure are feasible [38], e.g., represent tasks to stations assignment that fulfill all the problem constraints. This feature is of extreme importance to eliminate time-consuming procedures required to assess the feasibility of intermediate solutions, eliminate unfeasible ones and eventually identify alternative configurations. Defining a specific SA deals with setting the encoding of alternatives and the first solution, along with the way to obtain a feasible synchronization configuration, the neighborhood generation, and the objective function adopted to evaluate the solutions found. Those elements are described in detail in the following of this section.
# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Previous Research
#### 2.1.4 False News Detection Based on User Interaction  
[PERSON] [33] proposed an idea regarding hoax and false news detection, where the nature of a Facebook post could be determined by the users who \"like\" the posts. The baseline for their research was that a user who \"likes\" a post determined as a hoax, is anticipated to \"like\" even more hoax posts. They would analyze a post according to the users who \"liked\" the post, and if there was a high enough amount of users who had previously \"liked\" several posts determined as hoaxes, the current post being analyzed would be determined to be a hoax as well. For their experiments, they used two different classification techniques: logistic regression and boolean label crowdsourcing (BLC). Both of their techniques achieved very high results in detecting whether a Facebook post could be determined as a hoax or a non-hoax, suggesting that analyzing the users interacting with Facebook posts can accurately determine the nature of the Facebook posts. WhileTacchini's [33] approach effectively employs user interaction to detect hoax posts with high accuracy, its reliance on user behavior, which could be influenced by various factors unrelated to the post's authenticity, may limit its robustness in reality.  
[PERSON] [34] used linguistic analysis and user detection to detect false news. They proposed a model that would generate representations of social media users based on the language they use and the news they spread, which would then be used to detect false news. The model was created by using convolutional neural networks (CNNs), which are ideally suited for text classification. In their study, they analyzed the language commonly used by those who share false news. The study concluded that the language used by users who spread false news is consistent, which, in turn, makes it easier to detect news based on the people who share them, like in [PERSON]'s [33] research. However, [PERSON]'s [34] approach may struggle to account for the variety and developing tactics of misinformation across different social media contexts.
# Numerical Analysis on Spanwise Correlation of Vortex-Induced Force of Split Double-Box Beam
## 3 Overview of Numerical Simulation of Split Double-Box Beam
### Setup of Numerical Simulation  
#### 3.4.1 Demarcation of Grids  
The grids are divided by the principle of 'rigid motion area + moving grid area + static grid area'.  
When demarcating the 2D grids, the rigid motion area is divided by structured grids, and adhered body quadrilateral grids were adopted on the wall surface of the bridge model. The grids in the rigid area will not be deformed with the motion of the rectangular section, when vortex-induced vibration occurs in the main beam which can ensure the accuracy of the calculation results. The dynamic grid area is divided using a non-structural grid triangular grid, and the grids in this area are continuously updated with the motion of the rigid body.  
When demarcating the 3D grids, the rigid motion area is divided by hexahedral structured grids, the dynamic grid area is divided by unstructured grid tetrahedral cells, and the static grid area is divided by hexahedral structured grids, which are relatively sparse and change smoothly from inside to outside. The grids at the interface of each area are basically the same size to ensure the stability and accuracy of the calculation.  
To ensure that Y+ \(<\) 1, the distance between the center of the body fitted grid elements on the first layer and the wall is set as 0.05, and the growth rate of the grid is set as 1.05 to ensure the smoothness of grid transition [18]. The demarcation of grids is shown in Figure 5a-c.