esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The potential energy loss of OFDM signals during enhancement processing by stochastic resonance is theoretically analyzed. 5. The enhancement and demodulation system based on SUUBSR is implemented for OFDM signals with different subcarrier modulation schemes. Section 2 of this paper introduces the mathematical expressions for the unsaturated potential function model, the OFDM signal model, and the OFDM signal enhancement and demodulation system model based on SUUBSR. Section 3 theoretically derives the analytical expression for the transient and steady-state responses of the system, and analyzes the factors influencing the performance of the proposed system. Section 4 presents the experimental simulations of the proposed method, applying the proposed system to the enhancement and demodulation of communication signals with different carrier modulation schemes to further verify the feasibility and versatility of the proposed system. Section 5 presents all the conclusions of this study, as well as future work and the reasons behind it. Model of Second-Order Underdamped Unsaturated Bistable Stochastic Resonance System Driven by OFDM Signals  
This section provides a detailed introduction to the mathematical models of each component of the SUUBSR system driven by OFDM signals, serving as the theoretical foundation for the construction of the SUUBSR system proposed in this paper. Additionally, the overall steps of the OFDM signal enhancement and demodulation algorithm presented in this paper, as well as the method for segmenting OFDM signals, are proposed, providing a theoretical basis for subsequent research work.
## 4 Work Method
### Aspects Regarding the Input Data  
#### 4.2.1 Aspects Regarding the Length of the Beam-Type Element  
The starting point of the modelling process is fixing the length of the beam-type element, \(L\). The length of the element is a numerical value that remains constant during the iteration cycles and the kinematic constraints imposed on the joints.  
In order to obtain a beam-type element with optimal shape from a topological point of view, the criteria regarding the variation of the cross-section are a necessary but not sufficient condition. The additional criteria are in close relation to [PERSON]'s critical load, presented in Expression (1). In Expression (1), the second important factor which influences the critical buckling load is the length of the element, a numerical value that must be correlated with the geometric characteristics of the cross-section. Given the length of the element, a factor of inverse proportionality, there are three possible situations to note.  
The possible situations are related to the effective geometric length of the element \(L\) and the critical length of the element \(L_{cr}\), which is an ideal length for which the failure condition is fulfilled and the modelling of a variable cross-section makes sense.  
In case I, the length of the element is less than the ideal length, or \(L<L_{cr}\). In this case, the failure will occur by exceeding the value of the design resistance of the cross-section for uniform compression. The critical buckling force is net superior. In the present case, the modelling of a variable cross-section makes no sense, given the computational effort, the stiffness of the joints, and the technological terms. In this situation, the constant cross-section is an ideal solution.  
In case II, the length of the element is quasi-equal to the ideal length, or \(L=L_{cr}\). This case is considered the ideal case. The failure will occur by exceeding the value of the design resistance of the cross-section for uniform compression. The critical buckling force is superior. Modelling a variable cross-section is necessary in order to achieve a superior value for the critical buckling load beyond the design resistance of the cross-section for uniform compression.  
In case III, the length of the element is greater than the ideal length, or \(L>L_{cr}\). The failure will occur due to the loss of stability. The material is not used rationally.  
For the cases for which the length of the element is a numerical value in the neighborhood of \(L_{cr}\), there are several optimization methods, one of which is proposed in this study is to use a minimum quantity of material that has an optimum distribution on the cross-section.
## 4 Results
### Drowsiness Detection
The drowsiness recalls of the artificial neural networks (BPNN and CFNN) were higher than that of KNN but lower than that of SVM. However, PCA-SVM had not only the lowest average drowsiness precision and AUC, but also the longest training time of the hybrid models. SVM showed the same performance in single models. This indicated that both PCA-SVM and SVM had high misjudment rates, low classification reliabilities, and a long time delay and were not suitable for application in real driving scenarios. Moreover, the hybrid models performed better than the single models. Figure 6(a) shows the improvement of the hybrid models compared to the single models in each evaluation index. The accuracy, drowsiness recalls, drowsiness precisions, and AUCs of the hybrid models were mostly higher than the single models. In addition, the training times of the hybrid models were shorter than those of the single models. Figure 6(b) takes sample 9 to illustrate the above results in detail. The principal component analysis and artificial neural network hybrid models showed the highest accuracy, drowsiness recall, drowsiness precision, and AUC, with a training time of lower than one second. For the hybrid models, the accuracies, drowsiness precisions, and AUCs of the artificial neural networks (PCA-BPNN and PCA-CFNN) were higher than those of the classic machine learning algorithms (PCA-SVM and PCA-KNN). For single models, the accuracies, drowsiness precisions, and AUCs of the artificial neural networks (BPNN and CFNN) were also higher than those of the classic machine learning algorithms (SVM and KNN). In addition, the hybrid models showed improvements compared to single models in accuracy, drowsiness recall, drowsiness precision, and AUC, and had reductions in training time. This confirmed that the performance of artificial neural networks is better  
Figure 6(c) shows the improvement of the hybrid models compared to single models in each evaluation index. The accuracy, drowsiness recalls, drowsiness precisions, and AUCs of the hybrid models were mostly higher  
than classic machine learning algorithms and hybrid models have better performance than single models. In general, the results of the drowsiness detection stage reflect that (1) the hybrid model with principal component analysis and artificial neural networks performed the best; (2) artificial neural networks obtained a higher accuracy than classic machine learning algorithms; (3) the hybrid with principal component analysis and classification algorithms (artificial neural networks and classic machine learning algorithms) enhanced the accuracy and timeliness of drowsiness detection.
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 2 Theoretical Background and Proposed Method
### Band-Pass Filter for Signal Preprocessing  
A band-pass filter is a commonly used data processing method to filter out clutter. A band-pass filter can retain frequency components in a certain frequency range [19] while filtering out those in other ranges. Its bandwidth is determined by parameter \(\lambda\), following positive correlation.  
Assuming that the input image size is \(N\times N\), its passband bandwidth is given by Equation (13) [19]:  
\[f_{0}=\lambda N \tag{13}\]  
Therefore, the filter function is given by Equation (14), with \(x\) and \(y\) denoting the angular frequency of a two-dimensional filter [20]:  
\[filter(x,y)=\rho(x,y)e^{-(\rho(x,y)/f_{0})^{4}} \tag{14}\]  
where \(\rho(x,y)=\sqrt{x^{2}+y^{2}}\).  
Figure 4 shows the spectrogram of a band-pass filter. It can effectively remove high-frequency noise and low frequency-connected domain parts in the image, retaining the texture features in the image and increasing the suitability of the samples for CNNs to process.
## 4 Verification and Validation
### Thermal-Vacuum Tests  
Thermal-vacuum tests check for the behavior of a satellite in an environment with different conditions of extreme temperature and vacuum. Such tests may take tens of hours to complete, and they require constant monitoring during temperature transitions. Assuming some breaks between the test for the team and for the analysis of interim results, the total time for performing thermal-vacuum tests may take at least one week to finalize.  
The purpose of thermal-vacuum bake-out (TVBO) test is to detect any anomalous behavior in the satellite exposed to high temperature and very low pressure. The TVBO test is commonly used for detecting outgassing materials in the satellite. The SE01 CubeSat remained for 3 h in a bake-out chamber (Figure 14), with a controlled environment at 50 \({}^{\circ}\)C and \(1\times 10^{-5}\) mbar. During that time, the satellite only lost 2 g of its mass. Such a mass loss represents less than 1% of the total mass of the satellite, thus meeting the general requirements for outgassing.  
The satellite must also pass a thermal-vacuum cycling (TVC) test. Unlike TVBO, the TVC test is meant to check for how the satellite behaves under extreme temperature changes in vacuum. The environment to perform a TVC test must be prepared in order to reach both hot and cryogenic temperatures. Figure 15 shows the SE01 CubeSat inside a thermal-cycling chamber through an observation port during the TVC test.  
Figure 14: SE01 in the bake-out chamber.  
Figure 15: SE01 in the thermal-cycling chamber.  
Following QB50 mission requirements, the satellites must pass four cycles with peak temperatures of +50 \({}^{\circ}\)C and \(-20\)\({}^{\circ}\)C. A good grip between the CubeSat and the heating table leads to higher heat transfer to the satellite. Hence, a bolted union will make the temperature cycles much faster than placing the CubeSat resting over the table. Figure 16 illustrates the temperature profile of two different points of SE01 and a control temperature during the TVC test. Long periods of time occurred, during which temperature was kept constant at 25 \({}^{\circ}\)C, due to the unavailability of an operator for the chamber overnight. Additionally, the pressure was kept under \(1\times 10^{-5}\) mbar during the entire test. Additionally, the TVC tests require performing functional tests during the one-hour-long plateaus at the highest and lowest temperatures. Hence, it is necessary to fabricate an umbilical cable especially designed to interface all needed satellite connectors through the vacuum chamber. It should be noted that both the materials used for building the satellite as well as the umbilical should have low outgassing properties. Vacuum chamber operators may require a detailed breakdown of all materials used. In conclusion, a TVC test is considered successful if all functional tests are passed and no damage is observed on the satellite after the test.
# Influence of Fatigue Crack Formation and Propagation on Reliability of Steel Members
## 3 Crack Propagation from Surface  
State of stresses (tightness) surrounding crack tip is formulated by the stress-intensity factor (SIF) [31; 32], whose value is depending on the way of stressing, size and shape of a crack and on geometry of a member. In the case of a surface crack, the model of crack propagation is more complicated than in the case of crack propagating from the edge or the embedded crack. SIF is given by Equation, which follows from superposition of tensile and bending stressing  
\[\mathrm{K_{I}}=\sqrt{\pi\cdot a}\cdot\left(\sigma_{m}\cdot\mathrm{F(a)}_{m} \cdot\mathrm{M(a)}_{m}+\sigma_{b}\cdot\mathrm{F(a)}_{b}\cdot\mathrm{M(a)}_{b}\right) \tag{1}\]  
where:  
\(\mathrm{K_{I}}\) is the stress-intensity factor (SIF),  
\(\sigma_{m}\) is the tensile (membrane) stress,  
\(\sigma_{b}\) is the bending stress,  
\(\mathrm{F(a)}_{m}\) is the boundary-correction factor of stress intensity for tension stress,  
\(\mathrm{F(a)}_{b}\) is the boundary-correction factor of stress intensity for bending stress,  
\(\mathrm{M(a)}_{m}\) is the curve-fitting function taking into account the nonlinear stress peak due to the tension effects,  
\(\mathrm{M(a)}_{b}\) is the curve-fitting function taking into account the nonlinear stress peak due to the bending effects. Type of crack (real shape) is substituted (e.g., according to [33]) by the semi elliptical surface crack (Figure 2) with depth of crack \"a\" (length of semi-axis) and half of crack width in plate surface \"c\" (length of the second semi-axis). \"\(\mathrm{T_{cr}}\)\" means center of crack gravity and \"\(\mathrm{e_{cr}}\)\" means distance of center of crack gravity from surface (eccentricity). The change of crack cross-sectional area, depending on number of cycles N, is given by Equation  
\[\mathrm{A_{tr}(N)}=\frac{1}{2}\cdot\pi\cdot a(N)\cdot c(N)\ [m^{2}]. \tag{2}\]  
Distance of the crack center of gravity from the surface is equal to  
\[\mathrm{e_{tr}(N)}=\frac{4\cdot a(N)}{3\cdot\pi}\ [m], \tag{3}\]  
and second moment of area is described by equation  
\[\mathrm{I_{y,tr}(N)}=\left(\frac{\pi}{8}-\frac{8}{9\cdot\pi}\right)\cdot a(N) ^{3}\cdot c(N)\ [m^{4}]. \tag{4}\]  
In the case of the crack propagating from surface, it is not enough to investigate the change of just one crack dimension during crack form changing and its increasing, what is in the case of crack propagating from the edge or from the middle. It is necessary to take into account the change of both dimensions \"a\" and \"c\" in both semi-axes directions. The  
Figure 2: Shape and dimensions of a crack propagating from surface.
## 3 Adaptive In-Transit Co-Processing
### In-Transit Co-Processing  
Blocks transferred to processes on transit nodes might have been reduced or compressed during the in-situ stage. To perform any visualization or analysis of the data, the blocks first need to be decompressed. Blocks are decompressed based on the used compression algorithms. Because blocks on a specific process make up contiguous and convex regions in 3D space they can also easily be aggregated into a single block, which significantly can improve the performance of the co-processing process.  
The content of the co-processing is not part of our proposed approach. Instead, the approach only handles data decompression and data reconstruction on the transit nodes. Analysis and visualization can be performed in a normal fashion according to the researcher's needs, without any need to integrate existing tools and software with our approach.
## 3 Fuzzy-Logic Topology Control (FTC)
### WSNs Topology Control Using Fuzzy-Logic
#### 3.1.3 \(Prob_{0}\) and K  
Since \(ND_{ref}\) is characterized by the probability, it is necessary to adjust the node degree if a node does not reach \(ND_{ref}\). For instance, if \(CR=25\) m can not lead to actual \(ND=ND_{ref}\), then next step is to adjust \(Prob\) to a higher value according to the node degree error \(e_{ND}\). As shown in Figure 1, there is an integral controller outside the fuzzy control to adaptively change \(Prob\). From the control theory point of view, the system properties (e.g., steady state) are controlled by parameter \(Prob_{0}\) and \(K\). If \(e_{ND}\) is less than 0, \(K\) is configured to be half of its initial value. As a result, \(CR\) tends to increase faster than decrease. In Section 4.2, we will further discuss the impact of \(Prob_{0}\) and \(K\) by simulation.
## 3 Enhanced Frequency Response Strategy of a DFIG
### Local Stability Analysis of the Enhanced Frequency Response Strategy  
As shown in Section 3.2, the DFIG supports the system frequency using a constant \(\Delta P\) after detecting a disturbance. Figure 6 shows a simplified diagram of a DFIG and a synchronous generator (SG) for the local stability analysis.  
As in [24], \(G_{WTG}\) in Figure 6 is expressed as:  
\[G_{WTG}(s)=\frac{1}{1+sT_{WTG}}, \tag{11}\]  
where \(G_{WTG}\) is a constant and set to 0.2 s.  
Figure 5: Power trajectory of the enhanced frequency response of the DFIG-based over-speed de-loaded curve.  
Figure 6: Simplified diagram of a DFIG and a synchronous generator for the local stability analysis.  
As in [25], the transfer function of the SG in Figure 6 can be represented by:  
\[G_{SG}(s)=\frac{G_{\text{SG3}}(s)}{1+G_{\text{SG1}}(s)G_{\text{SG2}}(s)G_{\text{ SG3}}(s)\frac{1}{R}}, \tag{12}\]  
where \(G_{\text{SG1}}(s)\), \(G_{\text{SG2}}(s)\), and \(G_{\text{SG3}}(s)\) represent the transfer functions of the governor, prime mover, and rotating mass of the SG, respectively; \(R\) is the droop gain of the SG and is set to 5%.  
The transfer functions of \(G_{\text{SG1}}(s)\), \(G_{\text{SG2}}(s)\), and \(G_{\text{SG3}}(s)\) are represented by:  
\[G_{\text{SG1}}(s)=\frac{1}{1+sT_{\text{G}}}, \tag{13}\]  
\[G_{\text{SG2}}(s)=\frac{1}{1+sT_{\text{P}}}, \tag{14}\]  
\[G_{\text{SG3}}(s)=\frac{1}{\alpha s+\beta}, \tag{15}\]  
where \(T_{G}\) is the time constant of the governor and is set to 0.1 s, and \(T_{P}\) is the time constant of the prime mover and is set to 0.3 s [26]; \(\alpha\) and \(\beta\) are the angular momentum and damping coefficient of the SG and are set to 9.7 p.u. and 0 p.u., respectively [27].  
Thus, the transfer function from \(\Delta P\) to \(\Delta f\) can be expressed as:  
\[G_{\text{TF}}(s)=\frac{KG_{\text{SG3}}(s)G_{\text{WTG}}(s)}{1+G_{\text{SG1}}( s)G_{\text{SG2}}(s)G_{\text{SG3}}(s)\frac{1}{R}}, \tag{16}\]  
Figure 7 shows the root locus of (16). Equation (16) contains four poles, which are \(-10.8\), \(-4.99\), \(-1.24\pm\j 2.19\), respectively. Note that the poles of Equation (16) are located in the left half of the axis, therefore, we can deduce that the proposed enhanced frequency response strategy ensures the stable operation of the power system.
## 2 Test Method
### Variation of Anode Potential Difference
#### 3.6.1 Influence of Low Temperature on Anode Potential Difference  
The moisture content of soft clay was 35-55%. The change of anode potential difference is shown in Figure 16 when there was no grouting, when we filled with water or FeCl\({}_{3}\) solution near the anode at normal temperature, and when there was no grouting and FeCl\({}_{3}\) solution was filled at low temperature.  
As can be seen from Figure 16,  
1. Except for the water content of 35%, the anodic potential difference at low temperature was significantly higher than that at normal temperature without grouting, with an increment of 0.15-0.40 V and an increase of 26.7-70.2%, indicating that low temperature is not conducive to electro-osmosis. When the water content was 35%, the reason why the anode potential difference at normal temperature was higher than that at low temperature requires further analysis.
2. Whether at normal temperature or low temperature, the anode filled with FeCl\({}_{3}\) solution can effectively reduce the anode potential difference, and the reduction of anode electro-osmosis difference was 0.20-1.40 V, which effectively improved the electro-osmosis efficiency. When the moisture content was 35%, the decrease of anodic potential difference of FeCl\({}_{3}\) solution was the largest, with a decrease of 1.40 V at normal temperature and a decrease of 75.7%, and a decrease of 0.80 V at  
Figure 16: Variation diagram of anode potential difference with water content under different working conditions.  
low temperature and a decrease of 54.1%, indicating that the anode filled with FeCl\({}_{3}\) solution was the optimal choice when the moisture content was 35%.
3. After the anode was filled with FeCl\({}_{3}\) solution, in general, the anode potential difference at normal temperature was higher than that at low temperature, but there was little difference between them, which indicates that the anode filled with FeCl\({}_{3}\) solution can effectively reduce the influence of low temperature on the anode potential difference.
4. The anode filled with water can also reduce the potential difference of anode to a certain extent, because the water used was tap water and it contained certain conductive ions, but the effect of an anode filled with water was far less than that of an anode filled with FeCl\({}_{3}\) solution.
The data transmissions among neighbors, therefore, require channel switching and a sophisticated MAC scheme to find a rendezvous time for the sender and receiver. As a result, such protocols require fine-grained time synchronization among the nodes. To minimize the channel switching and to use multiple channels when necessary, a recent paper proposes a dynamic channel allocation policy based on control theory (hereafter referred to as DM-MAC) [10]. Because the nodes in DM-MAC change channels in a distributed manner for multihop communications, the nodes still need to switch channels. In order to completely avoid channel switching, a static channel allocation policy is proposed in TMCP [11]. TMCP divides the network into a number of sink-rooted sub-trees, where each sub-tree uses an orthogonal channel. However, the sub-tree creation requires a costly initialization phase. However, the sub-tree creation requires a costly initialization phase. In this paper, we design a multi-channel communications system for WSNs. LPMC dynamically adds or removes channels based on the active network load, and uses multiple channels whenever (when the network load is higher than the capacity) and wherever (the part of the network with a high load) it is necessary. While LPMC has a similar flavor in terms of channel switching to DM-MAC [10], and in terms of channel allocation to TMCP [11], it differs considerably in the following ways:  
* Unlike TMCP, LPMC does not need any initialization, such that the overhead is reduced. LPMC assigns channels dynamically instead of the static allocation of TMCP. TMCP divides the network into sub-trees by considering the equal data rate of the nodes. Due to the dynamic channel allocation, LPMC is transparent to data rates. * Unlike DM-MAC, LPMC adds or removes channels based on the overall network load. Furthermore, the sink controls the channel changing instead of the sensor nodes, since it has a more comprehensive view of the overall network traffic. The main contributions of this paper can be summarized as follows: (i) We propose a multi-channel communication systems for WSNs that keeps the protocol functionalities out of the sensor nodes as much as possible. (ii) LPMC dynamically identifies the network load and adds channel(s) to the mostly heavily loaded part. No initialization steps are required for LPMC, and the overhead for channel assignment is minimal. Nodes do not need to switch channels to receive or forward packets. (iii) LPMC dynamically adds paths with non-interfering channels to a set of nodes to meet the traffic demands. (iv) The performance of LPMC is evaluated through extensive simulations, and the results demonstrate that LPMC performs better than the existing schemes in terms of packet delivery ratio, network throughput, end-to-end delays, and energy consumption. The rest of the paper is organized as follows. In Section 2, we explain the existing multi-channel mechanisms for WSNs. We present the proposed mechanism in detail in Section 3. Section 4 demonstrates the performance evaluation of the LPMC. Finally, we conclude in Section 5 with a direction to the future works.