esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 4 Experimental Validation and Analysis
### Case I  
(1) Rolling Bearing Datasets: In the datasets, there are mainly eight fault states, namely health (HN), ball fault (BF), inner ring fault (IF), outer ring fault (OF), inner ring, and ball compound fault (IBF), outer ring and ball compound fault (OBF), inner and outer ring compound fault (IOF), and inner and outer ring and ball compound fault (IOBF). The datasets were collected at a motor speed of 3120 rpm with 2 V (Torque: 4.06 N\(\cdot\)m) load. The datasets of rolling bearing failures for eight different failure types are shown in Table 1.  
Figure 4: Experiment preparation. (**a**) Details of the fault arrangement. (**b**) Overlap sampling.  
Figure 5: Specific arrangement of DDS. 1. Speedometer. 2. Converter. 3. Drive motor. 4. Planetary gearbox. 5. Parallel gearbox. 6. Accelerometer. 7. Load. 8. Load controller. 9. Laptop. 10. Acquisition system.  
(2) Analysis of Fault Diagnosis Results in Rolling Bearing Datasets: To show the superiority of the proposed method, a comparison with other DL methods is conducted in this section. The methods used for comparison contain CNN, deep residual shrinkage network with channel-wise thresholds (DRSN-CW) [6], multiscale cascade midpoint residual convolutional neural network (MSC-MPResCNN) [27], and multiscale kernel-based ResCNN (MK-ResCNN) [26]. To verify the effectiveness of the multi-channel strategy and Meta-ACON, adaptive residual shrinkage network with Meta-ACON activation function (ARSN-M) and adaptive multi-channel residual shrinkage network (AMRS) without Meta-ACON are used for comparison.  
In Figure 6, we observe that AMC-RSN has the highest classification accuracy in all methods. We can also find that CNN has the worst diagnosis accuracy. Among these different residual networks, AMC-RSN can obtain the best diagnostic results and the highest time consumption. Compared to AMRS and ARSN-M, AMC-RSN can learn the classifier weights adaptively to improve classification accuracy and extract richer features. The tested bearing is shown in Figure 7. From Table 2, it is obvious that the average classification diagnosis accuracy of AMC-RSN is the highest, i.e., 99.24%, which is better than the remaining methods. In addition, the proposed method has the smallest standard deviation of 0.11%, which reflect AMC-RSN has robustness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Fault Type** & **Training Set** & **Test Set** \\ \hline \multirow{8}{*}{3120 rpm-2V} & HN & 868 & 218 \\  & BF & 868 & 218 \\  & IF & 868 & 218 \\  & OF & 868 & 218 \\  & IBF & 868 & 218 \\  & OBF & 868 & 218 \\  & IOF & 868 & 218 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the rolling bearing datasets.  
Figure 6: Diagnostic results of different models for the bearing datasets.
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
## 3 Results and Discussion
### Cavity Stabilization  
As mentioned previously, the device was mounted on a Peltier control stage in a thermally controlled laboratory environment. Nevertheless, in the free running case, after initializing the system at its optimal working point, the lifetime of operation was in the order of minutes. When the feedback system was enabled there was no observed drift in measured extinction ratio of the bistable switching over an 8 h period of operation. The system was then forced by detuning the laser injection wavelength from the optimal working point in both positive and negative directions from the set-point. Figure 10a shows the switching extinction ratio as a measure of operation in the bistable regime for laser detuning wavelength. Behaviour of the system is shown for both the free running case and where the feedback system has been engaged. For the case with no feedback control the bistable switching exists over only a few picometres of laser detuning as expected. When the control loop is engaged the laser wavelength can be detuned over a 1.5 nm range without affecting the bistable switching operation, limited only by the resistive heater's operation range.  
Finally, the Peltier stage was used to vary the ambient thermal conditions of the device. The temperature values detailed in Figure 10b relate to measurements on the surface of the Peltier stage in contact with the silicon substrate of the device. The lab was kept at a constant temperature of 20 \({}^{\circ}\)C throughout the measurements. A temperature variation of only \(\approx\)1 \({}^{\circ}\)C is sufficient to detune the device from the bistable regime in the free running case. By enabling the feedback control, the system exhibits stable switching over a temperature range of 40 \({}^{\circ}\)C, limited by the Peltier tuning range.  
Figure 9: Measured extinction ratio of the device as a function of injection wavelength detuning from working point. (i) Injection on the blue side of the resonance; (ii) incomplete bistable switching; (iii) bistable switching; (iv) injection on the red side of the resonator past ‘switch off’ point.
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 2 State of the Art  
### The Origin of the TMA Concept  
The pioneering idea of using the time as an additional degree of freedom to control the radiation pattern of an antenna was proposed by [PERSON] and [PERSON], from the Microwave Laboratory at the Hughes Aircraft Company, Glendale, CA, USA in 1959 [9]. The authors introduced theoretically this entirely new idea in antenna design and suggested several possible physical antenna configurations, such as a square wave switched linear array. However, they employed a common parabolic dish antenna in their first experimental tests and applied a nutation movement to the primary horn with a frequency of \(133\,\mathrm{Hz}\). With this technique, they achieved a multi-pattern operation obtaining simultaneously a sum diagram at the fundamental frequency and a difference diagram at the first sideband harmonic.  
In 1963, [PERSON] et al. --also from Hughes Aircraft Company-- experimentally extended the concept of \"time-modulated antenna\" to antenna arrays [5], thus coining the term TMA. They obtained ultra-low side lobe patterns by inserting a set of RF switches --programmed according to a predetermined periodic time sequence-- in the feed network of an 8-element waveguide slotted linear array working at the X-band. [PERSON] et al. stated that the higher the SLL improvement by applying time modulation, the higher the cost in the system gain. The source of this gain cost is the nonlinear nature of the periodic modulation, which inherently generates sideband radiated loss signals. Consequently, SR had been primarily identified as a phenomenon that severely degrades the TMA gain. As a matter of fact, [PERSON] et al. focused on two representative examples. On the one hand, they selected an initial static Dolph-Tschebyshev pattern with \(-30\,\mathrm{dB}\) SLL improved down to about \(-40\,\mathrm{dB}\) --by applying time modulation implemented by means of RF switches --at the expense of a modest loss in the antenna gain (\(0.3\,\mathrm{dB}\)). On the other hand, they considered a uniform static aperture distribution --with an SLL of \(-13\,\mathrm{dB}\)-- improved down to \(-40\,\mathrm{dB}\) through time modulation with a much higher price to be paid (\(2.4\,\mathrm{dB}\)) in terms of the antenna gain. In this way, [PERSON] et al. have implicitly opened the door to future research that would be focused on minimizing the SR while improving the SLL through the application of the TMA technique.
## 3 Data Collection and Observation Quality Assessment
### Carrier-to-Noise Density Ratio  
The C/N0 is influenced by many factors, such as the antenna gain parameters, the state of the receiver correlator and the multipath effect, which can directly reflect the GNSS observations data quality [30]. The larger the C/N0 means the stronger the signal; vice versa, the higher the probability of anomalous observations. In this assessment, the C/N0 is classified for the elevation of every 5 degrees, and the average value of C/N0 within 5 degrees of elevation is calculated. Figures 3 and 4 display the average value versus the elevation angle of multi-GNSS C/N0 from Huawei Mate40 and P40 at GPS SIC and S5Q, BDS S2I, GLONASS S1C, QZSS S1C and S5Q and Galileo S5Q, respectively, in which different colors represent the C/N0 received by the different satellites. As shown in the figure, the C/N0 of the smartphone shows periodic fluctuations, the positive correlation between C/N0 and elevation is not found, and the C/N0 of some observations is low at a higher elevation [2]. In terms of systems, the C/N0 value of GPS and QZSS is the best, with an average of about 35 dB-Hz; BDS and GLONASS are poorer, as their mean C/N0 is about 33 dB-Hz, and Galileo system presents the worst performance, its mean C/N0 around 30 dB-Hz. Moreover, the degree of dispersion for smartphone C/N0 is larger than for geodetic receivers, which is related to the omnidirectional, passive, linearly polarized and uneven gain built-in GNSS antenna of the smartphone. Compared to the Huawei P40, the C/N0 value received by the Huawei Mate40 is better for GPS, BDS, QZSS and Galileo observations, while for the GLONASS S1C frequency, the Huawei P40 outperforms the Mate40 by approximately 5 dB-Hz.  
Figure 4: Average C/N0 with elevation for the Huawei P40.  
Figure 3: Average C/N0 with elevation for the Huawei Mate40.
## 4 Experimental Evaluation
### Evaluating the Proposed Approach
#### 4.4.2 Assessing the Execution Times of the Proposed Approach
Using a dynamic load balancing technique did not improve the execution time of the data distribution stage to the same extent. On cluster A, the two pipelines achieved speedups of 1.17\(\times\) and 1.70\(\times\) as compared to an RLE compression and speedups of 1.83\(\times\) and 2.65\(\times\) compared to using no compression. On cluster B, the pipelines achieved speedups of 1.14\(\times\) and 1.58\(\times\) compared to RLE compression and speedups of 1.77\(\times\) and 2.44\(\times\) compared to no compression. Interestingly, the lossless Pipeline 1 was able to achieve better performance in all aspects (the compressed data size and the compression, data transfer, and decompression times) than the other lossless compression methods. This results from the fact that both of the pipelines scale with the utilized compression methods; in this case, using NONE, RLE, and HOMO. This scaling can be seen in Figure 10, where the compressed data size of Pipeline 1 closely follows that of the RLE compression. This behavior should extend to other compression methods as well.
## 3 Results  
### MGRCT Environmental Control in Municipalities  
#### 3.1.1 Annual Generation of Recyclable Waste  
The municipality of Tibasosa is classified as Scenario 1. The trend in the annual generation of recyclable solid waste shows similar percentages for plastic, paper, cardboard and glass and, to a lesser extent, for metal. Moreover, in the municipality of Arbelaez, classified as Scenario 2, there is evidence of a greater generation of plastics, followed by non-iron metal, cardboard, glass, paper and, finally, iron metal.  
The comparison between the annual production of recyclable waste (2021-2030) and its average composition on both municipalities is shown in Figure 3. The total production of recycled waste is higher in Tibasosa than in Arbelaez. Tibasosa produces around 30 t/year of paper, cardboard, plastic glass and non-ferrous metal, while in Arbelaez, the production of all these components is lower than 20 t/year (except for plastic, for which the annual production is higher than 50 t/year).  
The average composition of recycled waste in Tibasosa shows that the amounts of every component are equally distributed between paper (25.41%), paperboard (23.04%), plastic (27.39%) and glass (22.10%). Only a very small amount of metal (2.06%) is found.  
However, in Arbelaez, the amount of the components of waste is not equally distributed. Higher amounts of plastic (39.53%) are found together with non-ferrous metal (18.60%), paperboard (13.95%), glass (11.63%), paper (9.30%) and ferrous metal (6.98%).
## 6 Results and Discussions
### Simulation of the Monitoring and Control of the Parks of a City
#### 6.1.3 Experiment 1  
Number of computers: 1;  
Number of simulators: 1;  
Number of parks per simulator: 1;  
Total number of sensors per park: 11;  
Sampling period per sensor: 9.4 s;  
Total number of actuators per park: 1;  
Number of messages sent to the Broker per sensor: n.  
In this experiment, a single instance of the simulator was run on the same computer with the WSAN model of a single park. At each execution, the number of messages sent by each sensor to the Message Broker was increased by 50. The details are presented in Table 5.  
For example, in execution number 4, each of the sensors sends 50 messages giving a total of 4 \(\times\) (50 messages/sensor) \(\times\) (11 sensors) = 2200 messages. In this case, the simulation lasted 33.42 min.  
There is a directly proportional relationship between the number of messages and the time taken to send the messages to the Message Broker, as shown in Figure 17.  
Slope of the line = \((5500-550)/(83.29-8.47)\) = 66 messages/minute = 1 messages per second (approximately).  
Figure 17: Experiment 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Execution Number**} & \multicolumn{2}{c}{**Number of**} & **Total Number of** & **Average of** \\  & **Messages per Sensor** & **Messages Sent to** & **Execution Time** \\  & **(n)** & **Broker** & **(min)** \\ \hline
1 & 50 & 550 & 8.29 \\
2 & 100 & 1100 & 16.70 \\
3 & 150 & 1650 & 25.18 \\
4 & 200 & 2200 & 33.42 \\
5 & 250 & 2750 & 41.83 \\
6 & 300 & 3300 & 50.18 \\
7 & 350 & 3850 & 58.29 \\
8 & 400 & 4400 & 66.67 \\
9 & 450 & 4950 & 74.99 \\
10 & 500 & 5500 & 83.29 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experiment 1.
## 4 Results  
Table 2 presents a comparison of the estimated universal performance of the six models across three network structures. Enhanced and consensus models outperformed raw models, regardless of network structures and dataset configurations. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network structures. In the multi-dataset scenario, the performance of the enhanced FMs and consensus FMs improved by an average of 3.37% and 3.02% across the network structures, while the raw FMs showed an average increment of 1.55%. Figure 11: Comparison of the image and three types of masks in the guest dataset, CITY-20: (**a**) the image, (**b**) the original mask, (**c**) the supplemented mask, and (**d**) the modified mask. Figure 12: Performance evaluation by splitting the host dataset and merging the cross-validation results on the supplemented masks. In the single-dataset scenario, the consensus model outperformed others for the simple network, showing an improvement of 3.46%. Meanwhile, the enhanced models were the most effective for both U-Net and DeepLabV3, achieving improvements of 2.21% and 2.43%, respectively. In the multi-dataset scenario, we first compared the performance of single-dataset raw models with raw FMs. Then, we compared raw FMs with enhanced and consensus FMs. The raw FMs of the simple network and U-Net outperformed single-dataset raw models by 3.54% and 1.61%, respectively. However, the performance of the raw FM of DeepLabV3 decreased by 0.51% compared to the single-dataset raw model. The enhanced FMs were the most effective for both the simple network and U-Net, showing improvements of 2.19% and 1.97%, compared to the performance of the raw FMs. In contrast, the consensus FM showed the best performance for DeepLabV3, achieving an improvement of 2.76% over the performance of the raw FM. Table 3 compares the performance across different classes. Across all classes in all configurations, the performance of enhanced models improved compared to raw models. Consensus models also improved in almost all cases, except for two notable exceptions: the barren class in the multi-dataset case for the simple network and U-Net. Figure 13 compares the class-wise performance of enhanced and consensus FMs against raw FMs. For both the simple network and U-Net, the enhanced FMs demonstrate more reliable performance compared to the consensus FMs. However, in the case of DeepLabV3, the consensus FM outperforms the enhanced FM.
## 2 An Overview of Published Articles  
Based on the presented contributions, and according to the specified requirements, two main areas of interest may be identified: signal processing techniques for feature extraction, and machine learning-driven diagnostics.  
Within the first group, the proposed methods include texture features from vibration signals (Paper 1), Teager-variational mode decomposition for incipient fault detection (Paper 2), wavelet representation and denoising (Papers 4 and 10), envelope demodulation (Paper 8) and detrended fluctuation analysis (Paper 9). At the same time, machine learning is prominently featured across multiple papers for fault classification and feature extraction. Particular applications involve support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers (Paper 1), K-means clustering, decision tree, random forest, SVM, and AdaBoost algorithm (Paper 4), and neural networks (NN). In particular, generative adversarial networks for augmenting the available dataset and allowing for the training of further diagnostics NNs are proposed in Paper 5, while a depthwise separable convolution-based network such as YAMNet, commonly used for everyday life sound classification, is applied to machine diagnostics through transfer learning in Paper 6.  
Each paper addresses specific applications within industrial contexts, such as induction motors (Paper 1), large rotating metallurgic machinery (Paper 2), turbochargers (Paper 3), bearings (Papers 4, 5, 6, 8, 9), wind turbines' gearboxes (Paper 7), and pumping stations (Paper 10).  
The papers emphasize the practical implications of their findings, such as improved fault detection accuracy and reliability, enhanced maintenance practices and reduced downtime, optimization of operational efficiency in various industrial settings, potential for noise reduction and compliance with environmental regulations and application of advanced data analysis techniques to real-world challenges.  
These common themes and main arguments highlight the diversity of approaches and applications in fault detection and machinery health monitoring, showcasing advancements in both methodology and application across different industrial sectors. Also, it appears clear that the most recent artificial intelligence (AI) and deep learning papers are not just focused on classification effectiveness but also tries to solve the well-known issues related to the need of large training datasets with labeled data, which are often scarce in practical industrial applications and may lead to overfitting and generalization issues. In this regard, transfer learning (Paper 6) efficiently utilizes pre-trained models to reduce data requirements and training time, while generative networks (Paper 5) can augment and balance datasets, enhancing model performance and generalization.
# Equivalent Linearization Methods for a Control System with Clutching Inerter Damper
## 6 Analysis of the Accuracy of ELMs  
In this section an analysis of the accuracy of the ELMs proposed in Section 3, Section 4, and Section 5 was conducted using a numerical study. The equivalent linearization parameters for all methods are calculated as shown in Table 2, and the parameters of the SDOF for the numerical study are set as \(m=5\) kg, \(\omega=2\pi\), \(\xi=0.05\).  
Figure 6 shows the results of the equivalent linearization parameters intuitively. As can be seen, the equivalent inertance-mass ratio is almost linearly related to the inertance-mass ratio, and so is the equivalent damping ratio; the difference is that the equivalent damping ratio obtained from ELM3 is larger than that from ELM1 and ELM2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Soil Type** & \(\omega_{g}(rad/s)\) & \(\xi_{g}\) & \(\omega_{f}(rad/s)\) & \(\xi_{f}\) \\ \hline Firm & 15.0 & 0.6 & 1.5 & 0.6 \\ Medium & 10.0 & 0.4 & 1.0 & 0.6 \\ Soft & 5.0 & 0.2 & 0.5 & 0.6 \\ \hline \end{tabular}
\end{table}
Table 1: Filter parameters for different soil conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**ELM1**} & \multicolumn{4}{c}{**ELM2**} & \multicolumn{4}{c}{**ELM3**} \\ \cline{2-13} \(\mu\) & \(\mu_{eq}\) & \(b_{eq}\) & \(c_{eq}\) & \(\xi_{eq}\) & \(\mu_{eq}\) & \(b_{eq}\) & \(c_{eq}\) & \(\xi_{eq}\) & \(\mu_{eq}\) & \(b_{eq}\) & \(c_{eq}\) & \(\xi_{eq}\) \\ \hline
0.1 & 0.05 & 0.26 & 0.98 & 0.06 & 0.05 & 0.25 & 0.98 & 0.06 & 0.05 & 0.26 & 1.14 & 0.07 \\
0.2 & 0.11 & 0.53 & 1.92 & 0.08 & 0.10 & 0.49 & 1.91 & 0.08 & 0.10 & 0.52 & 2.31 & 0.08 \\
0.3 & 0.16 & 0.78 & 2.82 & 0.09 & 0.15 & 0.73 & 2.80 & 0.09 & 0.16 & 0.80 & 3.49 & 0.10 \\
0.4 & 0.21 & 1.04 & 3.69 & 0.10 & 0.19 & 0.96 & 3.66 & 0.10 & 0.22 & 1.08 & 4.69 & 0.11 \\
0.5 & 0.26 & 1.32 & 4.55 & 0.11 & 0.24 & 1.19 & 4.49 & 0.11 & 0.28 & 1.38 & 5.90 & 0.13 \\
0.6 & 0.32 & 1.58 & 5.37 & 0.12 & 0.28 & 1.41 & 5.30 & 0.12 & 0.34 & 1.68 & 7.12 & 0.14 \\
0.7 & 0.37 & 1.83 & 6.16 & 0.13 & 0.33 & 1.63 & 6.08 & 0.13 & 0.40 & 1.98 & 8.35 & 0.15 \\
0.8 & 0.41 & 2.06 & 6.91 & 0.13 & 0.37 & 1.85 & 6.83 & 0.14 & 0.46 & 2.29 & 9.58 & 0.17 \\
0.9 & 0.47 & 2.37 & 7.70 & 0.14 & 0.41 & 2.07 & 7.57 & 0.14 & 0.52 & 2.61 & 10.81 & 0.18 \\
1.0 & 0.53 & 2.63 & 8.46 & 0.15 & 0.46 & 2.29 & 8.28 & 0.15 & 0.59 & 2.93 & 12.05 & 0.19 \\ \hline \end{tabular}
\end{table}
Table 2: Equivalent linearization parameters for all methods.