esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 4 Applications of AI in Early Fault Diagnosis of Rotating Machinery
### Neural Network  
Neural networks can be used as classifiers in fault diagnosis, which is similar as the KNN and SVM methods. [PERSON] et al. [166] used CWT to extract fault features and a multilayer perceptron network was used for gear fault recognition. [PERSON] et al. [167] extracted fault features by WPT and EMD, and then a BP neural network was used to identify early cracks of rotors. [PERSON] et al. [168] used chaotic behavior features as fault features including the largest Lyapunov exponent, approximate entropy and correlation dimension, and then a neural network was used to identify different faults in rotating machinery.  
Several researchers have proposed improved neural networks methods, which can use the raw signals as the input without preprocessing. [PERSON] et al. [169] put the raw vibration signals into a 1D convolutional neural network for a fast and accurate bearing fault detection. [PERSON] et al. [170] used the raw vibration signals as the input of a multi-layer neural network for gearbox fault diagnosis. The above mentioned methods are summarized in Table 12.
## 2 Materials and Methods
### Protocols  
#### 2.3.1 Measurement Reliability of the Telemedicine System  
In order to exclude major effects of measurement error of our telemedicine system on the evaluation of human pulse contour readings, we explored the repeatability of measurements. To define measurement error by the SCN4 ALL system (combined error of DVP recording, data processing, and analysis), we recorded artificial signals generated by a pulse simulator device (MS100 SpO\({}_{2}\) Simulator, Contec Medical Systems Co., Ltd., Qinhuangdao, China). Besides the generation of high-quality, physiological simulated pulse signals (\"normal\"--SpO\({}_{2}\): 98%, heart rate: 55/min), the simulator offers signals which model frequent signal variants, \"Abnormal 1\" (titled \"geriatric\" in the simulator's software) (SpO\({}_{2}\): 92%, heart rate: 95/min) and \"Abnormal 2\" titled \"weak\" in the software (SpO\({}_{2}\): 90%, heart rate: 95/min) signal settings. The latter simulates a pulse wave in which the detectable signal is of low intensity (Figure 3). We performed five repeated measurements for each signal setting (Normal, Abnormal 1, Abnormal 2) with five different pulse oximeters of the same product release.
# Aeroelastic Optimization Design of the Global Stiffness for a Joined Wing Aircraft
## 4 Results and Discussion  
### Aeroelastic Responses of the Optimized Models  
The constraint conditions of all the methods are the same, including the static aeroelastic responses and flutter constraint. The specific constraint values are shown in Table 1.  
The displacement and torsion angle of the wingtip and the joint are the most concerning static aeroelastic responses, and a comparison of those values is given in Table 3. The flight condition is defined at \(Ma\) = 0.2, a 1.5 g overload and 25,000 m cruise altitude, where the acoustic velocity \(V_{a}\) = 298.4 m/s, the air density \(\rho_{air}\) = 0.04 kg/m\({}^{3}\). The cruise speed \(V_{cr}\) = 59.7 m/s, and the dynamic pressure \(q\) = 71.28 Pa. The aeroelastic deformation of the beam-frame model optimized using method A is shown in Figure 10. The deformation of the other beam-frame model optimized using method B is similar to what is shown in Figure 10, so it is not included to avoid repetition. The aeroelastic deformation of the 3-D model optimized using method C is shown in Figure 11. Figure 12 depicts one of the \(V-g\) and \(V-f\) diagrams obtained based on the \(p-k\) method. The other flutter result diagrams are not included to avoid repetition. All the flutter speeds of the optimized models are larger than the constraint speed. The flutter constraint \(V_{f}\) is larger than 90.0 m/s, which seems very low; however, as the designed cruise speed \(V_{cr}\) is only 59.7 m/s, and \(V_{f}\) is larger than 1.5 times \(V_{cr}\), the constraint speed is reasonable.  
The specific data of the aeroelastic constraints are listed in Table 3. From the table, we can see that compared with the original model, the constraints are satisfied well after optimization by all the methods, with only the vertical deformation of the wingtip reaching the constraint boundary value, and the other constraints having significant margins, so the optimization result is reasonable. It should be noted that the horizontal deformation of the wingtip is not negligible.
# Prototyping the Symmetry-Based Chaotic Communication System Using Microcontroller Unit
## 2 Materials and Methods
### Chaotic Communication System Architecture  
The choice of hardware often determines the key properties of the designed system. We implemented the testbench for a one-way chaotic master/slave type communication system using two 32-bit microcontroller units, namely Arduino DUE. In the proposed architecture, the transmitting controller generates the chaotic signal using the master system and sends the signal into the communication channel, see Figure 3. The receiver controller acquires a signal from the communication channel, synchronizing it with the chaos generator based on a slave discrete system. To transmit and receive a chaotic signal, we used built-in digital-to-analog and analog-to-digital converter modules. In addition, the \(I^{2}C\) bus was used for communicating the controllers with each other. This allowed us to change the generation parameters in two controllers simultaneously during the experiments. A digital display was added to indicate the current parameter values, and a keyboard was added for input purposes. The toggle control allows for switching the experimental set between the modes of changing parameters and generating a signal. One of the important parts of the research was the investigation of how the noise in the channel affects chaotic communication systems with various types of modulation. We implemented the mixing of white noise to the channel as a simple sum of signals generated by the transmitter and the special noise generator. A summing operation was implemented using a circuit based on two inverting analog adders. One should note that the circuit power is bipolar and equals \(\pm\)15V. By changing the noise level in the noise generator, one can achieve the desired signal-to-noise (SNR) level in the communication channel to investigate the considered modulation types under different noise conditions. Let us describe the experimental setup further.  
Figure 3: Block diagram of the designed chaotic communication system.  
Figure 2: Message transmission using (**a**) PM and (**b**) SCM technique.
## 4 Results and Discussion
### Lining Behavior and Earth Pressure Acting on Lining
#### 4.1.3 Tangential Earth Pressure  
From the tangential earth pressure, \(\tau\), around the lining in Figures 7c and 8c, the following was found:  
1. The shape of the \(\tau\) distribution is an ellipse whose major axis has an angle of 45 degrees against the vertical axis, as the loads acting on the lining and the lining structure are almost biaxially symmetric against the horizontal and vertical direction.
2. As \(k_{\mathrm{t}}\) increases, (1) for load model type 1 (with \(\tau_{0}\)), the distribution shape of \(\tau\) becomes more circular than that of the initial tangential earth pressure, \(\tau_{0}\), which corresponds to \(\tau\) under \(k_{\mathrm{t}}/k_{\mathrm{n}}=0\); and (2) for load model type 0 (no \(\tau_{0}\)), the distribution shape of \(\tau\) becomes more flat than that of \(\tau=0\), which corresponds to \(\tau\) at \(k_{\mathrm{t}}/k_{\mathrm{n}}=0\); and (3) the load model type, that is, the initial tangential earth pressure, \(\tau_{0}\), has more effect on the tangential earth pressure, \(\tau\). This is due to the following: (1) the shear stress magnitude is proportional to relative displacement and \(k_{\mathrm{t}}\). Shear stress is generated in the opposite direction of the relative displacement of the lining against the ground. Under the analysis conditions of this study, the relative displacement direction is horizontally outward, as shown in Figures 7a and 8a. (2) The direction of the initial tangential earth pressure, \(\tau_{0}\), for load type 1 in Figure 2 under \(K_{0}<1\) is also in the horizontal outward direction. (3) The calculated \(\tau\) comes from the superposition of the above shear stresses.
3. As \(k_{\mathrm{n}}\) increases, (1) for \(k_{\mathrm{t}}/k_{\mathrm{n}}=0\), the shape of the \(\tau\) distribution is almost the same, and (2) for \(k_{\mathrm{t}}/k_{\mathrm{n}}\)\(>0\), it becomes more circular. This is because, as \(k_{\mathrm{n}}\) increases, (1) the lining deformation decreases because of the increase in ground reaction force; (2) \(k_{\mathrm{t}}\) increases for \(k_{\mathrm{t}}/k_{\mathrm{n}}>0\); and finally, (3) the relative displacement between the lining and ground decreases, as shown in Figures 7a and 8a.
4. The \(\tau\) of the deep tunnel is larger than that of the shallow tunnel. This is because the overburden load increases, (1) the relative displacement between lining and ground is larger, as shown in Figures 7a and 8a; and (2) for load model type 1, the initial tangential earth pressure, \(\tau_{0}\), is larger.
# Imaging the Permittivity of Thin Film Materials by Using Scanning Capacitance Microscopy
## 4 Discussion  
1. With relative density (\(\rho\)) -76.8% of MoO\({}_{3}\) dielectric ceramics, the relative permittivity (\(\varepsilon_{r}\)) was \(\sim\)8.31 [19]. The relative permittivity (\(\varepsilon_{r}\)) of 4.50 M NaCl solution was 32.2 [20]. In the present paper, MoO\({}_{3}\) film-doped NaCl was treated with NaCl solution. The average value of \(\varepsilon_{r}\) of MoO\({}_{3}\) film was about 8.0, and that of MoO\({}_{3}\) film-doped NaCl was about 9.5. The experimental result of MoO3 were quantitatively consistent with other experimental results of the same material, the permittivity (\(\varepsilon_{r}\)) of MoO\({}_{3}\)-doped NaCl is significantly different from that of the undoped MoO\({}_{3}\).
2. The packaging specification of the probe is tip radius \(R\leq 10\) nm and cone half angle \(\theta\leq 22^{\circ}\). The capacitance gradient \(\frac{\mathrm{d}\mathcal{C}^{total}_{\mathrm{tot}}}{\mathrm{d}z}\) vs. setting height \(z\) between the tip and the metal ground was measured. Fitted with Equation (17), the tip radius \(R=16.6\) nm and cone half angle \(\theta=20^{\circ}\) were obtained. Since the probe was used for some time and may be blunt, the tip radius \(R=\)16.6 nm, and cone half angle \(\theta=20^{\circ}\) were reasonable.
3. Similar to the chemical intercalation method [21], partial MoO\({}_{3}\) film was treated with NaCl solution. There may be Na intercalation in MoO\({}_{3}\)-film doped NaCl. That was why the thickness and signal of SCM of MoO\({}_{3}\) film was slightly weaker than that of MoO\({}_{3}\) film-doped NaCl.
4. From the topography image of the film, the rise and fall of every point was regarded as the thickness change \(\Delta h\). The thickness \(h\) of the film at every point was set as \(h=\overline{h}+\Delta h=70\)nm \(+\Delta h\). The value of \(\overline{h}\) was obtained from the rise of 70 nm, which corresponded to the change from monocrystalline Si substrate to MoO\({}_{3}\) film. In the topography image of the film, the rise and fall of every point was relative to the previous point, not relative to 70 nm. The value of thickness \(h=\overline{h}+\Delta h=70\)nm \(+\Delta h\) was an approximate one.
5. The monocrystalline silicon substrate was partly coated with MoO\({}_{3}\) film. A point of Si substrate area was chosen. Its measured capacitance gradient \(\frac{\mathrm{d}\mathcal{C}^{Total}_{\mathrm{tot}}}{\mathrm{d}z}\) vs. setting height \(z\) between the tip, and the Si ground was just used to determine the constant \(k^{ground}_{stray}\), since Equation (17) was for metal ground.
6. There was no film on the area of Si substrate in Figure 3a. The results of \(h\) and \(\varepsilon_{r}\) of Si substrate area should be ignored.  
Figure 7: (a) The 2D image of \(\varepsilon_{r}\) and (b) its profile for \(y=56.6\)\(\upmu\)m.
## 5 Experiment and Result Analysis  
### Criteria  
In this paper, accuracy rate, precision rate, recall rate, \(F1\) value and confusion matrix are used as the evaluation criteria of the model:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[Precision=\frac{TP}{TP+FP} \tag{15}\]  
\[Recall=\frac{TP}{TP+FN} \tag{16}\]  
\[F1=2*\frac{Precision*Recall}{Precision+Recall} \tag{17}\]  
In Equations (14)-(17), \(TP\) stands for the number of actual categories predicted as true categories, \(TN\) stands for the number of actual wrong categories predicted as false categories, \(FP\) stands for the number of actual wrong categories predicted as true categories, and \(FN\) stands for the number of actual right categories predicted as false categories.
# A Practical Resource Management Scheme for Cellular Underlaid D2D Networks
## 6 Conclusions  
In this paper, we investigated a cellular underlaid D2D network, which is an enabling technology for mobile caching services. D2D communications are allowed to share radio resources with cellular uplink communications under the assumption that they comply with an interference constraint imposed by a cellular controller such as BS to secure the quality of cellular communications. The performance of D2D communications can be maximized by selecting an optimal combination of D2D pairs to transmit data. However, it causes tremendous complexity of computations and signalling overhead for channel feedbacks to determine an optimal combination complying with the interference constraint. Thus, we proposed a practical resource management scheme for D2D communications. Each D2D pair determines whether to transmit data on its own based on a threshold value and simple bitmap information broadcast by the BS. Thus, the proposed scheme does not require any feedback from the D2D network and does not cause any computational complexity to a BS either. We evaluated the performance of the proposed scheme in terms of average sum-rate and compared it with the optimal scheme. We also derived the optimal threshold values that maximize the average sum-rate of the proposed scheme. Our numerical results showed that the average sum-rates of the proposed scheme approach the optimal sum-rates in low or high SNR regions, despite the tremendous reduction in complexity and signalling overhead. It was also shown that the gap in performance between the proposed and optimal schemes noticeably decreases in the entire SNR region in practical environments where channels are non-_i.i.d_.
## 1 Introduction  
Conditions in modern tunnelling are demanding, as work is often being completed in unfavourable or unpredictable ground conditions along with stringent displacement tolerances and/or settlement requirements. This has driven a need for optimization of the support element and of the overall tunnel support regime. One such support element that is used extensively in underground works [1, 2] and has the potential for further optimization is the fully grouted rebar rock bolt. Understanding of the composite system has been hampered by technological limitations. The use of conventional strain gauge monitoring not only limits spatial resolution of the collected data but also discourages the use of full-scale specimens during testing [3, 4, 5, 6, 7, 8, 9]. Short specimens are often utilized, as it allows researchers to assume the transfer stress to be uniform over the length [10, 11]. There is ample evidence to suggest that there is a shift in transfer stress behaviour with short- to full-length specimens [9], and this can be seen in Figure 1. In order to address this limitation, a solution was further developed by [PERSON] (and associated industrial partners)through an innovative methodology of strain sensing [12]. This methodology uses Rayleigh Optical Frequency Domain Reflectometry (ROFDR), which provides an unprecedented spatial resolution of 0.65 mm for ground support elements without the conformance issue when using strain gauges. This research looks to create a better understanding of the individual and combined effects of ground support, with a focus on rock bolting. This has been achieved using the Distributed Optical Fiber Sensing (DOS) technique and ensuing methodology that has been employed in multiple investigations across a spectrum of scales. The testing has included in situ and laboratory investigations, while also being complemented with numerical modelling (as highlighted in Figure 1).  
This methodology has allowed for various detailed investigations into the parameters which effect rock bolt behaviour. Findings pertaining to the effects of grouting material, embedment length and confining material have previously been conducted [9]. Rib spacing and grout annulus (the thickness of grout between the bar and the confining medium) were selected for investigation, as they have both been seen to influence performance through their effects on mechanical interlock. Both have the potential for additional insight to be gleaned from the use of fiber optic sensor monitoring. Limitations seen in the current body of literature are the lack of full-scale embedment length testing and limited spatial resolution of collected strain data [3; 4; 5; 6; 7; 11; 13; 14]. This is significant as full-scale testing with a 1:1 geometric scale allows for the most reliable research and the closest approximation to field conditions [15]. To address the gap, a robust parametric analysis featuring 24 full-scale specimens was conducted. This was paired with additional investigations, numerical models were constructed and existing analytical models were compared to collected strain data [11; 16].
## 2 Airborne Instruments and Interface with the ATR-42
### Description of the Interfaces with the Research ATR-42 Aircraft  
The research ATR42 has been available for research experiments since early 2006 ([[http://www.safire.fr](http://www.safire.fr)]([http://www.safire.fr](http://www.safire.fr))). It is an original ATR42-320, specifically modified for scientific use. Because of limitations in terms of the existing lower fuselage apertures in the aircraft, _i.e._, only two openings (one forward aperture: \(650\times 450\); one aft aperture: \(700\times 450\) mm), two configurations were proposed for the CAROLS campaign flights, as shown in Figure 6. In the first configuration, providing dual-incidence data, the CAROLS radiometer was operated with two antennas, one pointing to nadir for vertical measurements, and the other pointing to the right side of the aircraft (slant antenna), at an approximately \(30^{\circ}\) incidence angle. The second configuration combines the CAROLS slant antenna, placed at the aft of the aircraft, with the STORM instrument placed at the front (at the location of the nadir antenna used in the first configuration). This second configuration is particularly useful for ocean measurements. The true incidence angles corresponding to each brightness temperature measurement were calculated accounting for the attitude of the aircraft (roll, pitch), with an accuracy of 0.1 degrees.  
Figure 6: **a.** (top) CAROLS instrument with one slant antenna and the STORM instrument, **b.** (bottom) CAROLS instrument with two antennas (one slant and one nadir).
## 4 Results and Discussion
### Laboratory Modelling of Biological Groundwater Treatment
For nonspecific denitrification bacteria of the genera _Rhodococcus_ and _Rhodobacter_, nitrate removal was insufficient; however, a decrease in the rate of NO3- reduction led to lower NO2 concentrations to avoid nitrite shock. We stress again that not only the amount of nitrates but also the microbial community diversity causes retardation in the nitrate reduction process. It should be added that in conditions of high pollution, the development of microbial biofilms on rocks can play a key role in contributing to pollutant concentration gradients and the protection of cells from toxic stress [39]. Microbial biofilm development can likely play an important role in formation conditions with high contamination. Therefore, we studied this process in detail. \begin{table}
\begin{tabular}{c c c} \hline
**Taxon** & **Sample 1** & **Sample 3** \\ \hline Acidovorax & 10.32 & 0 \\ Acinetobacter & 0.60 & 0 \\ Actinobacteria & 0.74 & 0 \\ Bacillus & 0 & 2.0 \\ Burkholderiaceae & 4.69 & 0 \\ Candidatus Accumulibacter & 0.55 & 0 \\ Clostrtiales & 1.12 & 0 \\ Comamonas & 0.55 & 0 \\ Dechloromonas & 1.91 & 0 \\ Delftia & 1.15 & 0 \\ Desulfofbulaceae & 1.56 & 0 \\ (Desulfobacterium) & 0.68 & 0 \\ Desulfuromonas & 0.63 & 0 \\ Gallionellaceae & 1.94 & 0 \\ Hydrogenophaga & 9.06 & 0 \\ Limnohabitans & 1.64 & 0 \\ Methylophilaceae & 0.57 & 0 \\ Nitrosomandaceae & 0.87 & 0 \\ Nitrospira & 1.47 & 0 \\ Other & 9.03 & 0 \\ Parcubacteria & 0.1 & 0 \\ Planctomycetes & 0.66 & 0 \\ Polaromonas & 1.01 & 0 \\ Pseudomonas & 2.29 & 0 \\ Pseudorhodobacter & 0.63 & 0 \\ Rhodobacter & 0 & 10.30 \\ Rhodobacter & 2.31 & 0 \\ Rhodococcus & 0.03 & 87.7 \\ Rhodocyclaceae & 4.31 & 0 \\ Sideroxydans & 1.36 & 0 \\ Simplicispira & 3.19 & 0 \\ Sulfuritalea & 4.18 & 0 \\ Thauera & 27.21 & 0 \\ Vulcanibacillus & 1.77 & 0 \\ Sum & 100 & 100 \\ \hline \end{tabular}
\end{table} 
Table 5: Analysis of 16S rRNA genes of the microbial community in the samples with added whey (part 1). (% of the total number of OTUs).
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Temperature Compensation  
The effect of temperature on the electrical resistance \(R_{a}\) of the sensor \(S_{a}\) must be compensated to obtain an accurate force or displacement measurements.  
The following temperature compensation approach has been developed for this purpose: the compensated resistance \(R_{c}\) can be obtained as:  
\[R_{c}=R_{a}-f(G) \tag{1}\]  
where \(G\) is a reference variable (e.g., a measured temperature or the resistance of the dummy sensor) and \(f(G)=\sum_{i=0}^{N}c_{i}G^{i}\) is a polynomial of degree \(N=3\), since preliminary tests showed a nonlinear relationship. In some cases, the degree of the polynomial can be reduced to \(N=1\) or \(2\) if the results are comparable.  
\(f(G)\) describes the relationship between \(R_{a}\) and the reference variable \(G\) when \(S_{a}\) is not stressed (i.e., only considering no-load steps), allowing to separate and compensate for the effect of temperature on \(R_{a}\). Indeed, the coefficients \(c_{i}\) are obtained with Ordinary Least Squares (OLS) fitting of \(f(G)\) over \(R_{a}\) measured when force and displacement are zero.  
Each temperature sensor and the dummy sensor have been considered in turn as reference variables \(G\), and the repeatability \(r\) defined in Section 2.4.2 has been applied to \(R_{c}\) and has been used to choose the reference variable \(G\) and the polynomial order.  
Then, a second polynomial (up to the 3 rd degree, which can be reduced to the 1 st or 2 nd degree as for Equation (1)), is used for fitting the relationship  
\[F=f_{F}(R_{c})\ \text{or}\ P=f_{P}(R_{c}) \tag{2}\]  
between the measured applied force \(F\) (or the displacement \(P\) applied by the robot) and \(R_{c}\), by considering all performed cycles, the results are evaluated through the repeatability \(r\) and the RMSE of the fitting.