esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
The threshold could be either fixed during the test (in the case of a stationary noise), or floating (in the case of a varying background noise) [76; 77]. In our experiments, the preamplification level was fixed at 40 dB. The detected AE signals have a high signal-to-noise ratio (more than 50 dB). Under these low noisy environment conditions, the AE signals received by the four channels are not affected by a possible saturation (low amplification) or loss in amplitude (they fall below threshold). However, in the case of a noisy environment, raw AE signals can be denoised using a wavelet analysis before performing the hit detection according to a suitable parameterization [78; 79]. Finally,note that if the noise level is unknown, a soft thresholding method can be adopted based on the wavelet coefficients, and by considering a non-white noise model [80].
## 1 Introduction
On the one hand, under the given input-output condition of the structure, the contributions of different modes to the root mean square (RMS) response are distinct and the decrement of these contributions when adding damping is also distinct. On the other hand, the amount of added damping ratio is related to the location of transducers. For example, for a truss-like structure, the damping effect of a mode using resonant shunt depends on the fraction of modal strain energy (MSE) of the rod which is replaced with transducer. For a different mode, the maximum fraction of modal strain energy among all the rods is different, which means that in the independent modal resonant shunt method, a different mode has different controllability. So, in this study, a modal criterion is proposed considering the sensitivity of mode \(H_{2}\) norm with the damping ratio and fraction of MSE. The aim of this study is to apply the independent modal resonant shunt for the random vibration suppression of a truss-cored sandwich panel called a Kagome structure using the proposed modal criterion. This paper is organized as follows: In Section 2, the Kagome structure is introduced and the finite element model of the piezoelectric shunted structure is presented. The independent modal resonant shunt method is summarized in Section 3. In Section 4 the modal criterion is detailed. The validity of the proposed method is demonstrated through the time-domain simulation of the random excitation responses of face sheet. Finally, conclusions are drawn in Section 5.
## 4 Simulation and Discussion  
As mentioned in Section 3, there are two simulation examples in this section. One is for second-order design parameters, and another is for machining tool parameters. Due to the complex coupling relationships between different machining tool parameters, the optimal parameter ranges are usually constricted in a narrow interval, which means a globally optimal solution would hardly be solved. Therefore, this paper conducted the optimal design of control parameters first in a wild range and then optimized machining tool parameters based on former optimal results.  
For the first example, design contact ratio, the radius of the semi-major axis of the contact ellipse, and the amplitude of the meshing transition point are the three control parameters, as well as the optimization variables. However, for the second example, the gear processing parameters remain unchanged while the machining parameters of the pinion are optimized, including the cutter radial settings \(S_{r1}\), the sliding base feed setting \(X_{b1}\), the vertical offset \(E_{m1}\), the increment of machine center to back \(X_{g1}\), the second-order coefficients \(c_{md}\), the third-order coefficients \(c_{me}\), and the fourth-order coefficients \(c_{m\prime\prime}\). Therefore, there are three optimization variables in example 1 and seven optimization variables in example 2.
# Influence of Silane-Modified Coal Gangue Ceramsite on Properties of Ultra-High-Performance Concrete
## 3 Results and Discussion
### Hydrophobic Modification
#### 3.1.2 Water Absorption  
To evaluate the hydrophobic modification effect of CGC, we measured the water absorption change of dry aggregates after reaching a saturated surface following hydrophobic treatment. As illustrated in Figure 5, KH0 represents unmodified aggregates, while KH2.5 and KH5 represent aggregates modified with different SCA concentrations, respectively.  
Figure 5 clearly shows that SCA effectively reduces aggregate water absorption. As the SCA concentration increases, aggregate water absorption decreases accordingly. Compared to unmodified aggregates, those soaked in 2.5% and 5% SCA solutions exhibited a 13.1% and 27.9% reduction in water absorption, respectively. Therefore, it was proved that the hydrophobic modification effect of the aggregate was remarkable.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Hardware Integration  
The system (Figure 1) was designed to provide sensor reports from each monitor once every five minutes.  
Every two seconds, the microprocessor (ATmega32u4) in each monitor collects the analog and digital input data from the four sensors, as well as the serial report from the Teensy microprocessor analyzing the microphone data and passes these data to a WiFi module (Dragino WiFi IoT module HE), which is programmed with a lightweight Unix variant, OpenWrt. The Unix system averages the data over a five-minute interval and transmits the averages to a central server. The noise monitoring is too computationally intense for the Seeeduino microprocessor; instead, the fast Fourier transform of the signal is processed on the Teensy 3.2's 32-bit ARM Cortex-M4 72 MHz processor. A printed circuit was designed and manufactured to incorporate all the devices and microcontrollers into a single package that can easily fit into a compact enclosure (Figure 2).  
Our circuit board included the option of amplifying the carbon monoxide and oxidizing gas sensors, signals both initially refined with the manufacture's sensor board. This amplification trades sensor range for precision as the two signals (designated as OP1 and OP2 by the board manufacturer) are scaled to the 5 V maximum input on the microprocessor's analog-to-digital convertor. After considering past measurements in the factory, the gains were set to 1:1 for the carbon monoxide sensors and 1:5 for the oxidizing gas sensors. Consequently, sensor results larger than the expected range were measured at the maximum sensor value.  
Figure 1: A block diagram of the sensor and microprocessor architecture.  
Figure 2: The monitor is encased in a plastic box. (**a**) The open top view reveals the location of the main electronic components and the body of the Sharp DN particle sensor and fan assembly. (**b**) The bottom view reveals the position of the sensors and the black mounting brackets on either end.  
The central microprocessor and sensors rely on a 5 V power supply and typically consume 1 W of power during operation. The monitors were designed to be mounted in the web of an I-beam approximately 2.5 m above the ground (Figure 3) and covered a workspace that measured approximately 252 m \(\times\) 292 m with a ceiling approximately 8.5 m high. A bracket-and-hook system was developed to secure the monitors to the brackets at a fixed location and orientation, while simultaneously allowing for them to be retrieved with minimal effort. To prevent safety concerns, the monitors were designed to allow for a pole to be able to place and retrieve the monitors, negating the need for a ladder or stepping stool.
## 2 Derivation of the Azimuth and the Elevation Error under Frequency Mismatch
### First Condition: \(\Delta\theta=0\)  
In the first case of \(\Delta\theta=0\), \(\Delta\theta=0\) implies that there should be no estimation error in the azimuth. On the other hand, \(\Delta\theta=0\) does not impose any restriction on the elevation estimation error, which implies that, under the first order approximation, the partial derivative of the cost function of an interferometer algorithm with respect to \(\theta\) is zero for \(\Delta\theta=0\), irrespective of \(\Delta\psi\), even in the presence of frequency offset between the frequency of the incident signal and the nominal frequency adopted in calculating the array manifold. As previously stated, since the requirement that the partial derivative of the cost function with respect to \(\theta\) should be zero at \(\theta=\theta^{(0)}+\Delta\theta,\psi=\psi^{(0)}+\Delta\psi\) does not impose any constraint on the elevation error, \(\Delta\psi\), the constraint on the elevation error, \(\Delta\psi\), is imposed from the fact that the partial derivative of the cost function with respect to \(\psi\) should also be zero. Substituting \(\Delta\theta=0\) in the equation resulting from the constraint that the partial derivative of the cost function with respect to \(\psi\) should be zero, this constraint reduces to a quadratic equation or a fourth order equation in \(\Delta\psi\), depending on the approximations employed:  
\[\text{Second order equation in }\Delta\psi \cos(\Delta\psi)\approx\mathbf{1},\sin(\Delta\psi)\approx\Delta\psi\] \[\text{Fourth order equation in }\Delta\psi \cos(\Delta\psi)\approx\mathbf{1}-\frac{\Delta\psi^{2}}{2},\sin( \Delta\psi)\approx\Delta\psi\]  
Out of two roots of the second order equation or out of four roots of the fourth order equation, \(\Delta\psi\) maximizing the cost function is selected. Therefore, \(\Delta\psi\) can easily be obtained from a quadratic formula.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 2 The KLMS Algorithm Based on Random Fourier Features
### Randomized Feature Networks-Based KLMS Algorithm
```
Input: Dimension parameter M; learning rate \(\mu\); kernel parameter \(\sigma\); Initialization: Initialize weight vector \(\mathbf{\Omega}(0)\); Sample \(\{\mathbf{w}_{i}\}_{i=1}^{M}\) from the Fourier bases \(p(\mathbf{w})\) of a kernel, which remains the same during the iteration process. For the Gaussian kernel, the directions \(\{w_{i}\}_{i=1}^{M}\) of random mapping are drawn from \(N(0,\sigma^{2})\). While\(\{\mathbf{x}(n),d(n)\}_{n=1}^{N}\) available do (1) Compute the random Fourier feature vector \(\varphi(\mathbf{x}_{n})\) : \(\varphi(\mathbf{x}(n))=[cos(\mathbf{w}_{1}^{T}\mathbf{x}_{n}),...,cos(\mathbf{w}_{M}^{T}\mathbf{x }(n)),sin(\mathbf{w}_{1}^{T}\mathbf{x}(n)),...,sin(\mathbf{w}_{M}^{T}\mathbf{x}(n))]\) ; (2) Compute the output of adaptive filter : \(y(n)=\mathbf{\Omega}(n)^{T}\varphi(\mathbf{x}(n))\); (3) Compute the error : \(e(n)=d(n)-y(n)\); (4) Update weight vector : \(\mathbf{\Omega}(n)=\mathbf{\Omega}(n-1)+\mu e(n)\varphi(\mathbf{x}(n))\); End While;
```  
**Algorithm 1**The KLMS-RFN Algorithm. The main characteristics of the proposed KLMS-RFN algorithm can be described as follows: Firstly, the algorithm can save much of the storage cost by eliminating the dependence on the input samples because the KLMS-RFN algorithm does not need to store the important samples. Secondly, compared with the implicit mapping-based KLMS algorithm introduced in [4], the computation complexity of the KLMS-RFN algorithm is lower than that of the computing kernel matrix when the size of the selected samples is large. Thirdly, in addition, the KLMS-RFN algorithm can be used as an online learning algorithm in the nonstationary environment because it can overcome the constraints of a growing weight network and the complex process of updating the sample set to track the feature changes of the input signal. Fourthly, it is a uniform framework of the kernel adaptive algorithm based on explicit mapping mechanism, and different explicit mapping methods can be incorporated into this framework to obtain the corresponding nonlinear adaptive algorithm.
# Sliding Mode Control of Electro-Hydraulic Position Servo System Based on Adaptive Reaching Law
## 4 Simulation Analyses
Giving a step signal, we used the SMC algorithm based on the exponential reaching law and the SMC algorithm based on the adaptive reaching law to simulate the piston displacement trajectory and piston velocity trajectory. It can be seen from Figure 9 that the SMC algorithm based on the exponential reaching law can make the piston reach a given position, but there is buffeting, the piston displacement buffeting range is \(\pm\)0.0005 mm, and the piston velocity buffeting is \(\pm\)0.0001 m/s. It can be seen from Figure 10 that the chattering range generated by the SMC algorithm based on the adaptive reaching law makes the piston reach the given position at \(\pm\)0.0002 mm, compared with the SMC algorithm based on the exponential reaching law, where the chattering is only two-fifths of that when using the exponential reaching law, and the piston velocity buffeting is \(\pm\)0.0003 m/s, only three-tenths of the velocity buffeting in Figure 9. The convergence velocity of the adaptive reaching law is faster than that of the traditional exponential reaching law. In addition, when the piston reaches the sliding mode surface, the buffeting of the system can be adaptively adjusted according to the system state variables, and finally converges to the equilibrium point of the electro-hydraulic position servo system. It can be seen that the SMC algorithm using the adaptive reaching law can obviously weaken the chattering problem caused by the SMC algorithm using the traditional exponential reaching law, the chattering of the piston displacement and velocity is obviously reduced, and the system still has a good tracking. Figure 8: Displacement tracking curve of exponential reaching law SMC algorithm after disturbed signal.
## 4 Genetic Algorithm
### Genetic Operators
#### 4.5.4 Tournament Selection
For job-3, \(\hat{e}_{3}\) is the minimum of \(\{e_{1,3},\ e_{2,3},\ e_{3,3}\}=e_{1,3}=920.0\), and \(\hat{d}_{3}\) is the maximum of \(\{d_{1,3},\ d_{2,3},\ d_{3,3}\}=d_{2,3}=2603.8\). Therefore, \(\hat{f}_{3}=2603.8-920.0=1683.8\). Similarly, for the forth job, \(\hat{e}_{4}=220.0\), \(\hat{d}_{4}=2583.6\), and \(\hat{f}_{4}=2363.6\). The sublot finish separation time for job-1 is zero since this job has only one sublot, whereas the sublot finish-time separation of job-2 is the difference between (1) the maximum of \(\{d_{1,2},\ d_{2,2},\ d_{3,2}\}=d_{3,2}=\hat{d}_{2}\), and (2) the minimum of \(\{d_{1,2},\ d_{2,2},\ d_{3,2}\}=d_{1,2}=\hat{g}_{2}\), which is evaluated as \(\hat{h}_{2}=\hat{d}_{2}-\hat{g}_{2}=2599.8-2257.4=342.4\). The sublot finish-time separations for the other jobs can be evaluated similarly. The result is summarized in Figure 5:0 In the last row of this table, the objective function components \(\hat{f}_{mas}\), \(\hat{f}_{total}\), \(\hat{h}_{mas}\), and \(\hat{h}_{total}\) are indicated as 2487.5, 9014.7, 1006.1, and 1787, respectively. Table 7 provides the schedule for the prototype problem with respect to the machines. From this table, the workload because of each operation assignment can be evaluated by subtracting SB from PE. For instance, from the first row of this table, the load because of operation-1 of sublot-1 of job-3 is \(l_{m,o,s,j}=l_{1,1,1,3}=1342.0-840.0=502\). Similarly, the workload because of the other four operations on machine-1 can be evaluated as 441.8, 381.4, 291.3, and 147.3, bringing the total workload on this machine to \(1763.8+840=2603.8\), where 840 is the release date of the machine. The utilization (workload/makespan) of this machine is 100% as its workload is the same as the makespan. The workloads and the utilization of the other machines are also evaluated in a similar way as summarized in Figure 5:1 The objective function components \(\hat{l}_{max}\), \(\hat{l}_{total}\), and \(\hat{l}_{max}-\hat{l}_{min}\) are given in the last row of this table as 2603.8, 12,488.4, and 427.7, respectively. The values for all of the objective function components are summarized in Figure 5:2

#### 5.1.2 Optimizing a Single Objective

When we optimize only one objective function term, unaccounted objective function terms can be adversely impacted. This phenomenon asserts the importance of multi-objective optimization. To illustrate this reality, we solve Problem-1 by considering one objective function term at a time. The result is depicted in Figure 5:3 Figure 9 provides the values of the makespan \((Z_{1})\) when \(Z_{1}\) through \(Z_{10}\) are optimized one at a time as a single objective function. As it should be the case, the smallest makespan (about 2608) is achieved when \(Z_{1}\) is considered as the only term in the objective function.
## 2 Coaxiality Measurement Method  
The CE of the APT system is primarily influenced by the error of the angle between the transmitting optical path (beam outgoing path) and the communication optical path (beam receiving path). The point-ahead mirror usually works in an open loop. However, for satellite laser communication, coaxiality is usually constrained at the microradian level; hence, a special on-orbit calibration scheme is needed to reduce the CE. The CE measurement system based on CCR is illustrated in Figure 1. Here, a periscope-type APT is selected as the research target. The CE detection process is as follows. The signal beam is sent out from the laser source, and then the beam is expanded by the lens. Afterward, the beam is reflected by the point-ahead mirror (PAM), Mirror 1, fast-steering mirror (FSM), azimuth folding mirror, and elevation folding mirror sequentially. Then, the beam goes out of the APT system, is reflected by the CCR, and goes back in the same direction. Then, the beam is reflected by the elevation folding mirror, azimuth folding mirror, FSM, and Mirror 2. The blue arrows denote the direction of outgoing light, and the green arrows denote the direction of incoming light.  
Finally, the beam is focused on the surface of the CCD (Charge-coupled Device). The CE can be determined by measuring the displacement deviation between the centroid of the reflected spot and the detector center. Suppose that the beam transmits through the APT system. In such instances, the beam direction will deflect from the original direction when the mirror is error assembled, and the laser spot energy distribution is destroyed by the CCR with a geometrical machining error. Under the joint action of both errors, the spot centroid will deviate from the detector center. It is important to note that the PAA and FSM's position errors are mainly relevant when the APT is powered on. This kind of error can be corrected based on the feedback signal from the position detector, such as the eddy current sensor. So, the assembly error of Mirror 1, Mirror 2, azimuth folding mirror, and elevation folding mirror are under primary consideration.  
In Figure 1, the acronyms have the following meanings: CCR refers to the cube corner retro-reflector; _El_ FM refers to the elevation folding mirror; _Az_ FM refers to the azimuth folding mirror; PAM refers to the point ahead mirror; \(M_{1}\) refers to the Mirror 1; and \(M_{2}\) refers to the Mirror 2. O-_xyz_ is the coordinate of the APT system, where axis \(o\)-\(x\) is parallel to the laser, \(o\)-\(z\) is parallel to the azimuth axis, and \(o\)-\(y\) can be defined by the right-hand principle.
# Simulation Analysis and Experimental Verification of the
## 2 L/R Mechanism Design  
During the launch process, the SEC is fixed to the side wall of the space station module through the joints at the top and bottom. The schematic diagram of the MSHML is shown in Figure 2, which is composed of three parts: the main structure of the experiment cabinet, the L/R mechanism, and the MP.  
Figure 1: Conceptual model of the Chinese Space Station.  
The L/R mechanism is a single-drive multioutput lifting mechanism, which is composed of a driving device, a gear transmission device, and a locking device. It is specifically developed and equipped for the MP and provides a higher level of controlled environment for samples and instruments to facilitate fine operation in different experiments. When the L/R mechanism is locking, coaxial gears Z2 and Z3 are driven by the level gear Z1 on the L/R mechanism driving device. Next, Z3 drives the central gear Z4 that drives the four branch gears Z5, Z6, Z7, and Z8 to rotate synchronously so that the four lead screws of the L/R mechanism move forward to tighten the MP. When the end of the lead screw contacts with the groove of the MP, a locking force is generated until the motor reaches a predetermined locking torque, the driving device stops working. At this time, MP is safely locked, and the preload on both sides is \(F\).
## 2 Proposed Prognostic Framework
### Online Real-Time Prognostic Framework  
The overall flow of the proposed online prognostic framework using particle filter algorithm is shown in Figure 2. When a minimum of two new measurement datapoints are available post image segmentation, the prediction algorithm begins to predict the damage evolution due to delamination. The Paris law parameters for both the regions in Figure 1 are represented as (\(m_{1}\), \(C_{1}\)) and (\(m_{2}\), \(C_{2}\)), respectively.  
When a new measurement data is available for prediction, the algorithm evaluates whether the cumulative impact energy, \(N_{k}\), crosses the jump energy so as to choose different Paris law parameters. The slope of the predicted values at every measurement cycle was calculated and compared with the slope of the previous prediction instant. The initial slope value was assumed to be zero. The time instant at which the current slope value was found to be less than the previous time instant slope values was assumed to be the jump energy and can be expressed as:  
\[N^{*}:\rightarrow\ \frac{a_{k}-a_{k-1}}{N_{k}-N_{k-1}}<\frac{a_{k-1}-a_{k-2}}{N _{k-1}-N_{k-2}} \tag{13}\]  
As per Equation (13), the jump energy \(N^{*}\) is deterministic. Once the jump energy is detected, the algorithms identify that the second region of the degradation has kicked in and switches the Paris law parameters from (\(m_{1}\), \(C_{1}\)) to (\(m_{2}\), \(C_{2}\)).  
A particle-filter-based prognostic framework was employed to predict the damage evolution. Based on Ref. [22], the initial distributions for the parameters were assumed to be  
\[m_{1}\sim\text{U (0,4.05), }C_{1}\sim\text{U (--22.33,0)}\]  
\[m_{2}\sim\text{U (0,3.6), }C_{2}\sim\text{U (--22.2,0)}\]  
The initial parameter distribution for the damage stage was assumed to vary online between the current measurement data available for prediction and zero as shown below.  
\[a_{0}\sim\text{ U (}z_{k},0\text{)}\]  
The proposed framework is an extension of the methods adopted in Ref. [22]. For simplicity, we label the method/results used in Ref. [22] as M1. In M1, jump energy, \(N^{\star}\), was found to be 20 J based on the experimental results. Additionally, the damage growth curves of three different GFRP samples were analyzed, and mean and sample variance were used to define the initial parameter distribution for jump energy, which was used in the particle filter algorithm. In our proposed method, since we perform an online prognostic study, the jump energy is deduced on-the-fly based on the predicted damage area. Hence, the proposed method overcomes the dependency on the initial distribution of jump energy compared to M1 and can easily be applied to samples that have a different damage area propagation trend (trace) than Sample S1 used in this work. Based on the initial parameter distribution, the model parameters were estimated using the particle filtering framework and the damage state was projected from the current state till the end of life (EOL) of the sample to estimate the remaining useful life (RUL).