esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Simulation 3: Simulation of Algorithm Anti-Noise Performance  
In this simulation, the carrier frequency of each of the two groups of source signals was set to 14 MHz, 20 MHz, and 26 MHz, and the \(\lambda\) was fixed at 40% correspondingly. In the environment of the SNR from 5 to 30 dB, the method in simulation two was used to mix the source signals. The SDR and BER of the separated signals with the increase in the SNR are shown in Figures 15 and 16:  
Figure 14: (**a**) The SDR of different modulation signals with different \(\lambda\); (**b**) The BER of different modulation signals with different \(\lambda\). The 1 st and 2 nd comparison algorithms are given in literatures [8; 10], respectively.  
Figure 15: (**a**) The SDR of three BPSK signals under different SNR; (**b**) The BER of three BPSK signals under different SNR. The 1 st and 2 nd comparison algorithms are given in literatures [8; 10], respectively.  
Figure 13: (**a**) The SDR of three BPSK signals with different \(\lambda\); (**b**) The BER of three BPSK signals with different \(\lambda\). The 1 st and 2 nd comparison algorithms are given in literatures [8; 10], respectively.  
It can be seen from Figures 15 and 16 that, with the SNR increases, the separation performance of the algorithm proposed in this paper and the comparison algorithms have been effectively improved. However, compared with the comparison algorithms, the SDR and BER of the separated signals by the proposed algorithm are higher and lower, respectively, indicating that the signal separation performance is better. Especially for signals with the same BPSK modulation (Figure 15), when the SNR is 10, the BER of the separated signals is lower than \(10^{-2}\). For signals with different modulation (Figure 16), the BER drops below \(10^{-2}\) when the SNR reaches 20 dB.
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods
### The Late Fusion Branch  
#### 2.4.1 Foreground and Background Segmentation  
In order to separate the background and the foreground, we use a simple method to separate the two. First, since rice leaves are green, we use Equation (1) to convert the RGB image to \(I^{\prime}\). \(I^{\prime}\) mainly converts stronger green pixels. Then we use the Otsu thresholding algorithm [36] on \(I^{\prime}\) to get a mask, M. Finally, we use M to cover the thermal image. The segmentation process is shown in Figure 3 and several examples are shown in Figure 4  
\[I^{\prime}(x,y)=2G(x,y)-(R(x,y)+B(x,y)), \tag{1}\]  
Figure 3: Foreground and background segmentation process.  
Figure 2: Overview of proposed framework.  
In Equation (1), \(G(x,y)\), \(R(x,y)\), and \(B(x,y)\) are the intensity values of the three channels of each pixel of the RGB image at the image coordinate \((x,y)\), while \(I^{\prime}(x,y)\) represents the converted intensity value at the image coordinate \((x,y)\). In addition, when \(G(x,y)\gg R(x,y)\) and \(G(x,y)\gg B(x,y)\), \(I^{\prime}(x,y)\) will have a greater intensity; therefore, the pixel in \((x,y)\) is likely to be a leaf. When \(G(x,y)<(R(x,y)+B(x,y))\), the pixel is probably not a leaf, therefore, we regard \(I^{\prime}(x,y)\) as 0.  
We roughly cut out the foreground and background. Although the cutting is not perfect, we believe it is enough for the application in this study. In order to explain why the cutting results are sufficient for our use, as shown in Table 3, compared with the classification results in Section 2.3, the classification ability of the late fusion branch is much higher. We speculate that since the objects we identify are thermal images, if the edges contain errors, they might be weakened by the pooling operation in the CNN. If some leaves are missed, since the temperature difference between the leaves in the whole rice field is usually not large, such slight omissions do not affect the result.
## Materials and Methods
### Displacement Measurement  
The load-displacement hysteresis curve was automatically recorded by the MTS loading system, and the layout of the Linear Variable Differential Transformers (LVDTs) used for the joint displacement measurements is shown in Figure 6; the accuracy of the LVDTs is 0.01 mm. LVDT3 and LVDT4 were used to monitor the rotation of the beam. For specimens with a strong beam, LVDT3 and LVDT4 were located 30 mm away from the column face, as shown in Figure 6a. For specimens with a weak beam, taking into account the local buckling at the root of the beam flange, LVDT1 and LVDT2 were placed  
Figure 4: (**a**) A picture; (**b**) Schematic of the test setup.  
Figure 5: Loading protocol.  
on the beam web, 150 mm away from the column face, as shown in Figure 6b. LVDT1 and LVDT2 were placed at the column end to measure the rotation of the column, and LVDT5 was placed at the beam end to measure the displacement of the loading point.  
The relative rotation between the beam and column flange can be obtained from the following expressions:  
\[\theta_{\rm c}=\frac{u_{1}-u_{2}}{1300} \tag{2}\]  
\[\theta_{\rm b}=\frac{u_{3}-u_{4}}{H} \tag{3}\]  
\[\theta=\theta_{\rm b}-\theta_{\rm c} \tag{4}\]  
where \(u_{1}\), \(u_{2}\), \(u_{3}\), and \(u_{4}\) are the displacements measured by LVDT1 through LVDT4, respectively; \(\theta_{\rm b}\) is the rotation of the steel beam; \(\theta_{\rm c}\) is the rotation of the CFST column; and \(H\) is the horizontal distance between LVDT3 and LVDT4, where \(H\) is 350 mm for specimens SJ-1 through SJ-5 and 180 mm for specimens SJ-6 through SJ-8.
To answer this question, we provide the optimization model for multicast traffic throughput maximization in WSNs utilizing time division multiple access (TDMA) to radio channel, where both MCSs and the transmission power of the broadcasting nodes are adjustable. We assume that the MCSs as well as the transmission power used by the broadcasting nodes can be switched from a time slot to a time slot. Two TPC cases are considered in the paper: discrete TPC and continuous TPC. In the former, the transceivers can use only a discrete, usually small, set of power levels (this is common when it comes to low-cost sensor nodes), while, in the latter, the transmissions power applied by the nodes can change continuously. The presented optimization model is the extension of the model that was introduced in [14] (the model was further extended in [17] to include dynamic MCSs assignment) that make it possible to deal with the complex problem of including transmission power control into throughput optimization in WSNs with dynamic MCSs assignment that serve multicast traffic. The provided optimization model allows us to treat the considered problem in a rigid mathematical way, thus it can be used as a benchmark while designing algorithms and protocols to be implemented in the real networks. To the best of our knowledge, such a model does not exist in the literature and, together with the numerical study that illustrates the considerations of the paper, constitute the main contributions of this work. As written above, we consider TDMA-based WSNs. Note that using TDMA is well justified in the given context, since it outperforms other medium access control schemes in terms of traffic throughput. It is also worth mentioning that TDMA is considered to be one of the most promising solutions for energy efficient WSNs [18]. The list of radio technologies supporting TDMA includes WirelessHART, ISA100, and the family of IEEE 802.15.4-based low-rate personal area networks (LR-WPAN). The rest of this paper is organized as follows. In Section 2, we discuss the related work on multicast transmission, MCSs assignment, and TPC in WSNs. Section 3 presents the network description and notations used in this paper. Problem statement and the corresponding mixed-integer programming (MIP) formulations, together with a solution algorithm, are presented in Sections 4-6. Subsequently, numerical results illustrating considerations of this paper are discussed in Section 7. Finally, Section 9 concludes the paper.
## 4 Results and Discussion
### Simulation of the Effects of Rainfall Intensity on Slope Stability  
This section is devoted to the numerical simulation of the contribution of rainfall to the stability of soils of different compositions. The soils under consideration are characteristic of the locality along the R523 roads in the Thulamela Municipality. Simulations entail the estimation of FoS values of the different slope compositions subjected to drained and undrained conditions. The effects of rainfall intensity are tested on silt clay soil, clay soil, and loam soil slopes.
## 2 Materials and Methods
### Capturing Motion Data of Baduanjin, Using IMU  
Undergraduate students with no clinical/mental illness or physical disability from a university in Southwest China were invited to participate in this study. Participants read the information sheet that outlined the purpose and procedure of the study. Those who agreed to participate in the study were given the consent form to sign.  
The IMU used to capture the motion data of Baduanjin was Perception Neuron 2.0, which was developed by Noitom [14]. Perception Neuron 2.0 is a commercial IMU with 17 inertial sense units, and each unit comprises a 3-axis gyroscope, 3-axis accelerometer and 3-axis magnetometer. The output of the captured motion data is in a Biovision Hierarchy (BVH) motion file through the Axis Neuron software developed by Noitom. The effectiveness of Perception Neuron 2.0 in measuring motion accuracy has been experimentally confirmed by [PERSON] et al. [18].  
Before measuring the motion data, participants were required to practice Baduanjin for 30 min to familiarize themselves with the motions and warm up their body. After the practice, the motion data of participants immediately were captured by IMU when they practiced Baduanjin. No feedback was given to participants during practice and the capturing of the motions. The eight standard motions of Baduanjin are shown in Figure 2.
# Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground
###### Abstract  
In order to improve the automation and safety of underground steel belt support in coal mines, a method for the intelligent identification and positioning of steel belt anchor holes in roadway support using inductive sensors is proposed. Using STM32F407 ZGT6 as the main control chip, tasks such as data acquisition and processing, motor motion control, etc., are assigned based on the real-time operating system FreeRTOS. Using the XY mobile platform equipped with inductive sensors to detect steel belts, The collected data includes coordinate values and voltage values. Adaptive threshold generation and correction strategies are used for threshold segmentation and extraction of anchor hole boundary points. The principle of Hough circle transformation is used to fit the extracted boundary points into circles. The results show that this method can perform anchor hole positioning with a positioning error of within 5 mm, meeting the design requirements.  
Keywords:roadway support; steel belt anchor hole; FreeRTOS; threshold segmentation; Hough circle transform; anchor hole positioning +
Footnote †: journal: Applied Sociology
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 4 Viscoelastic Model of Asphalt Concrete
### Determination of Viscoelastic Parameters Based on Generalized Maxwell Model  
The generalized Maxwell model, as shown in Figure 9, is widely used to describe the linear viscoelastic behavior in the finite element software because the relaxation modulus can be expressed by Prony series in the following equation [22]:  
\[E(t)=E_{e}+\sum_{i=1}^{n}E_{i}e^{-t/\tau_{i}} \tag{5}\]  
where \(E(t)\) is the relaxation modulus, \(E_{\text{e}}\) is the long-term equilibrium modulus, \(E_{i}\) is the spring modulus in the \(i^{\text{th}}\) Maxwell model, \(\eta_{i}\) is the dashpot viscosity in the \(i^{\text{th}}\) Maxwell model, and \(\tau_{i}=\eta_{i}/E_{i}\) is the relaxation time.  
Through Fourier transform, the storage modulus and loss modulus of Generalized Maxwell Model can be derived as:  
\[E^{\prime}(\omega)=E_{\text{e}}+\sum_{i=1}^{n}\frac{E_{i}\omega^{2}\tau_{i}^{ 2}}{1+\omega^{2}\tau_{i}^{2}} \tag{6}\]  
\[E^{\prime\prime}(\omega)=\sum_{i=1}^{n}\frac{E_{i}\omega\tau_{i}}{1+\omega^{2 }\tau_{i}^{2}} \tag{7}\]  
in which \(E^{\prime}(\omega)\) is the storage modulus, \(E^{\prime\prime}(\omega)\) is the loss modulus, and \(\omega=2\pi f\) is the angular frequency (rad/s).  
According to the relationship between dynamic modulus, phase angle, storage modulus and loss modulus:  
\[|E^{*}(\omega)|=\sqrt{\big{[}E^{\prime}(\omega)\big{]}^{2}+\big{[}E^{\prime \prime}(\omega)\big{]}^{2}} \tag{8}\]  
Figure 8: Master curve (**a**) Dynamic modulus; (**b**) Phase angle.  
\[\tan\varphi(\omega)=\frac{E^{\prime\prime}(\omega)}{E^{\prime}(\omega)} \tag{9}\]  
then the related parameters in the generalized Maxwell model can be determined by the optimization method through the following objective function:  
\[\text{minimize}\,error=\frac{1}{N}\left\{\sum_{i=1}^{N}\left[1-\frac{\left|E^{ *}(\omega_{i})\right|_{Modelled}}{\left|E^{*}(\omega_{i})\right|_{Mastercurve} }\right]^{2}+\sum_{i=1}^{N}\left[1-\frac{\varphi(\omega_{i})_{Modelled}}{ \varphi(\omega_{i})_{Mastercurve}}\right]^{2}\right\} \tag{10}\]  
where \(N\) is the total number of sample data obtained from the master curve, the subscript modelled means the data is predicted by the generalized Maxwell model, and the subscript Mastercurve means the data is obtained from the master curve.  
Both the dynamic modulus and phase angle are considered in Equation (10) because the overall viscoelastic properties of the material cannot be accurately reflected by only one component, such as dynamic modulus or phase angle [23]. In order to facilitate the solution of the optimization problem, the relaxation time \(\tau_{i}\) is specified in this study, varying from \(10^{-6}\) to \(10^{4}\) s. The viscoelastic parameters from the results of dynamic modulus at the reference temperature of 20 \({}^{\circ}\)C in Figure 8 are listed in Table 3 and can be used as the input parameters of material property for the later finite element analysis.
## 2 Materials and Methods  
### Transmon Fabrication  
The device was fabricated at the Technology Innovation Institute in Masdar City, Abu Dhabi. A high resistivity (>20 k\(\Omega\)\(\cdot\)cm) (100) FZ silicon was used. The wafer was diced into square pieces of 22 mm \(\times\) 22 mm with half cuts (from the backside of the wafer) of 2 mm \(\times\) 14 mm as the cavity slot dimensions. Those half-cuts allow manual cleaving post junction fabrication, avoiding exposing the junction for a protective resist layer if dicing was to be performed afterwards. Dies were sonicated in Acetone and IPA for 5 min.  
A bilayer stack of resist was spun using a SCS 6808 spin coater (Indianapolis, IN, USA). In total, 500 nm of Kayaku PMGI SF9 (Westborough, MA, USA) was used for the bottom layer and 200 nm of Alfresist GmbH AR-P 6200.9 (CSAR) (Strausberg, Germany) was used for the top layer. Both the shunting capacitors and the junctions were written using a Raith eLINE Plus electron beam lithography system. In total, 20 keV, an aperture of 60 \(\upmu\)m, and a dose of 185 \(\upmu\)C/cm\({}^{2}\) was used for the capacitors, while the junctions were written with a smaller 15 \(\upmu\)m aperture and a dose of 112.5 \(\upmu\)C/cm\({}^{2}\). Post exposure, the samples were developed using Allresist AR 600-546 and Kayaku diluted 101A for the top and bottom layers, respectively.  
Subsequently, the dies were loaded into a Plassys MEB 550s (Marolles-en-Hurepoix, Freance) e-beam evaporation system. The system was allowed to pump to below 9 \(\times\) 10\({}^{-8}\) mbar of pressure. The evaporation was performed at a 55\({}^{\circ}\) tilt angle using the Manhattan approach. In total, 35 nm of Al were deposited at a rate of 0.6 nm/s, followed by a static oxidation at 0.625 mbar for 25 min, and a second Al evaporation of 55 nm.  
Littoff was performed in a bath of N-Methylpyrrolidone at 70 \({}^{\circ}\)C. A test structure on the same die was used to measure room temperature resistance using a S-1160 manual probe station from Signatone (Gilroy, CA, USA) and a 2450 SMU from Keithley (Cleveland, OH, USA). In total, 4849 \(\pm\) 115 \(\Omega\) was measured for the device used in this work. An optical image of the transmon chip within the resonator and of the device is shown in Figure 1. The junction area is roughly 200 \(\times\) 200 nm\({}^{2}\) and the two antenna pads separated by 20 \(\upmu\)m are 556 \(\upmu\)m long and 144 \(\upmu\)m wide.  
The resonant cavity is made of Al alloy 6061 with a rectangular parallelepiped shape of dimensions \(L_{x}\times L_{y}\times L_{z}=26\times 36\times 8\) mm\({}^{3}\). The silicon chip with the qubits is hosted in the middle of the \(x-y\) plane with pads parallel to the \(z\) axis (see Section 4) to couple to the mode TE110. Two holes allow for the insertion of the antennas for control and readout of the qubit state.
## 4 Predictions Using ML Algorithms
### Predictions for Uneven Settlement  
Figure 16 compares the training and testing results of the uneven settlement predictions by RFR, MLP, and XGBoost, respectively. Compared with the MLP model, both the XGBoost and RF models give better predictions. From Figure 10, it is obvious that the data points produced by the RFR and XGBoost models fit well with the reference line. This indicates the ability of these two algorithms to predict the maximum settlement, especially for larger values. As can be seen in Table 3, similar to the prediction results for settlement, the best prediction model is RF, followed by XGBoost. In this study, the data are based on monitoring data from one section, which shows less noise. As a reliable tree-based tool, XGBoost and RFR methods can strike a balance between predictive accuracy and robustness.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**R\({}^{2}\)**} & \multicolumn{3}{c}{**MAE**} & \multicolumn{3}{c}{**RMSE**} \\ \cline{2-7}  & & **T****iangle** & **Testing** & **T****iangle** & **Testing** & **T****iangle** & **Testing** \\ \hline \multirow{3}{*}{ESD-L} & RFR & 99.2\% & 99.7\% & 2.42 & 2.30 & 6.43 & 3.11 \\  & MLP & 98.9\% & 99.4\% & 4.63 & 3.46 & 7.24 & 4.46 \\  & XGBoost & 99.8\% & 98.5\% & 1.08 & 5.82 & 2.23 & 7.04 \\  & RFR & 99.5\% & 99.5\% & 1.93 & 2.72 & 5.42 & 4.01 \\  & MLP & 95.5\% & 97.9\% & 9.30 & 7.45 & 15.82 & 8.18 \\  & XGBoost & 99.9\% & 98.4\% & 1.25 & 5.79 & 2.80 & 7.21 \\  & RFR & 98.4\% & 99.7\% & 2.50 & 2.04 & 8.37 & 2.78 \\  & MLP & 94.2\% & 97.3\% & 8.82 & 6.38 & 15.76 & 7.71 \\  & XGBoost & 99.8\% & 96.8\% & 1.14 & 6 & 2.68 & 8.42 \\  & RFR & 99\% & 99.4\% & 2.64 & 3.15 & 7.17 & 4.05 \\  & MLP & 95.8\% & 98\% & 7.88 & 6.02 & 14.33 & 7.58 \\  & XGBoost & 99.8\% & 98\% & 1.15 & 5.87 & 2.58 & 7.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance indicators of models in predicting settlement.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 4 Subsystems Analysis
### Propulsion System  
The micro-propulsion system is usually the largest and the most demanding in terms of mass and volume, thus it is seen as one of the driving subsystems for this mission (MR7 in Table 1). Moreover, commercial launches are more frequent than deep-space ones (respectively around five per year and one per year), so they can be used as a rideshare more easily [34]. For this reason an Earth sphere-of-influence (SOI) escape scenario is considered,  
Figure 2: Ground Sampling Distance (GSD) as a function of the fly-by altitude, for off-the-shelf visible cameras, together with mass and volume characteristics.  
with the goal of achieving a transfer able to fly-by an asteroid. The Earth escape problem has been investigated in previous work and solutions have been proposed--chemical propulsion followed by electric low-thrust trajectory [34], low-thrust escape trajectory [35], and micro-propulsion staging [36]. However, all of these solutions consider customized propulsion systems, which are not in line with the off-the-shelf approach used in this mission concept.  
Both chemical and electric micro propulsion systems are evaluated, following respectively two simplified approaches for high-thrust and low-thrust applications.
## 3 Experimental Results
### Dynamic Behavior of PV System  
The effective practice of recommended PV pumping system was proved under varying sun insolation levels. In this experiment, solar irradiance level is varied from 300 W/m\({}^{2}\) to 1000 W/m\({}^{2}\). According to variation in sun irradiance level, corresponding changes in PV current, DC link voltage, BLDC stator current and motor torque have been verified (Figure 10) and PV pumping is running without any interruption. The duty cycle for BLDC-PV pump control is generated with variation in sun insolation accordingly and outstanding motion control has been comprehended.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 1 Introduction  
### Amorphous Metals: Structure and Properties  
Amorphous metals, also known as metallic glasses or bulk metallic glasses (BMGs), are a class of materials that have been researched for the past few decades due to their unique non-crystalline atomic structure. Unlike the conventional crystal structure of a metal, amorphous metals lack long-range atomic order, resulting in a disordered, glassy structure [1]. These materials possess remarkable properties that make them highly attractive for various applications.  
One of the main advantages of amorphous metals is their superior mechanical strength. In contrast to crystalline metals, which can deform through dislocation movement along specific crystallographic planes, amorphous metals are far superior as their long-range order does not exist and therefore they do not possess any specific slip planes [2]. As a result, they can achieve higher strengths [3], often exceeding those of their crystalline counterparts by orders of magnitude. The yield strength, combined with their high elastic limit and plastic strain [4], makes amorphous metals suitable for applications where a high degree of mechanical performance is needed, such as in sports equipment [5], aerospace components [6], and sensing instruments [7].  
Another important property of amorphous metals is their excellent resistance to corrosion [8]. Due to their lack of grain boundaries and the absence of crystalline defects,amorphous metals exhibit superior resistance to chemical attacks and corrosion compared to their structural counterparts within a certain composition range [9]. This property makes them attractive for use in harsh environments, such as in marine applications [10] and medical implants [11].  
These materials also possess unique magnetic properties. Amorphous alloys, like those based on Fe [12] and Co [13], exhibit soft magnetic behavior, making them suitable for applications in transformers, electrical motors, and magnetic shielding [14]. Other amorphous alloys, such as Nd-based materials, exhibit hard magnetic behavior, finding applications in permanent magnets and magnetic recording media [15; 16].