esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[26] propose a new method based on the wavelet transform for the detection of damages in plate-like and shell-like structures. Diego and Barros [27] employed WPT to analyze the magnitude of the harmonic distortion and the corresponding sub-harmonics in power systems. Pan et al. [28] proposed a novel approach based on improved WPT and support vector data description (SVDD) to assess bearing performance degradation. To obtain the features of the ignition pattern, Vong and Wong [29] used WPT to decompose the engine ignition signal and then employed multi-class least squares SVM to classify the fault types. It is worth pointing out the coefficients of WPT and the corresponding single branch reconstruction signals on all of the decomposing levels, which play a key role in singularity detection and feature extraction. As a novel pattern recognition approach, support vector machine (SVM), was proposed in the 1990s by Cortes and Vapnik, and now, it has been widely used in fault classification [30]. The basic theory for SVM is structural risk minimization (SRM) with small training samples, which are suitable for the classification and regression of the nonlinear and high dimensional problems. However, the lack of faulty training samples has greatly limited the fault classification using SVM in real-world applications. Recently, personalized medicine has become a hot topic to develop a medical procedure that separates patients into different groups, with medical decisions, practices, interventions and/or products being tailored to the individual patient based on his/her predicted response or risk of disease [31]. It is also a new direction for placing the patient at the center of healthcare, and the theoretical basis is molecular dynamics simulation from person to person [32]. Motivated by molecular dynamics simulation-based personalized medicine, the fault detection strategy for mechanical systems might be developed for the personalized diagnosis era. The most probable tools might be numerical simulation, the big data technique or a combination of the two. From the above descriptions, it might be concluded that if the limitation of faulty training samples can be inexpensively overcome by numerical simulation using the dynamic model of mechanical systems, the intelligent methods, such as SVM, GAs and NNs, will be directly applied to detect faults for machines under all kinds of working conditions. In this paper, we present the basic idea of a new personalized fault diagnosis methodology focused on the fault diagnosis of shafts. Based on the present methodology, this paper is arranged as follows: The basic idea and the procedures of the personalized diagnosis methodology are presented in Section 2. In Section 3, finite element (FE) models of shafts are constructed; an example is presented; and the shaft unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance are considered. To further apply the present method to real-world usage, the experimental investigations and further works are discussed in Section 4. Finally, the conclusion of this study is given in Section 5.
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 5 Validation of the Implicit Algorithm of the SBSP-R Model
### Intact Clayey Loess  
Intact clayey loess is hard to be predicted because of the bonding and particle size distribution [33]. Particle size distribution influences the non-unique critical state, and bonding damage affects the strain-softening of intact clayey loess. The critical state and strain-softening of intact clayey loess are predicted using the implicit algorithm of the SBSP-R model (see Table 5).  
The critical state and stress-strain are well captured by the present model with \(r=3.43\) and \(p^{\prime}{}_{s}=47.5\) kPa (see Figure 11), because the spacing ratio \(r=3.43\) is obtained from the triaxial compression test. \(r=3.43\) reflects the real location of the critical state in the \(v\)-ln\(p^{\prime}\) plane. The behaviors are not captured in the case of \(r=2.0\) and \(p^{\prime}{}_{s}=47.5\) kPa, because \(r=2.0\) is just suitable for Cam-clay soils.  
When \(r=2.0\) and \(p^{\prime}{}_{s}=0\) kPa, the present model degenerates to the modified Cam-clay model. When \(r=2.7\) and \(p^{\prime}{}_{s}=0\) kPa, the present model degenerates into the Cam-clay  
Figure 11: Comparisons between model prediction and test results: (**a**) stress-strain and pore pressure \(\sigma^{\prime}{}_{3}=200\) kPa, (**b**) stress-strain and pore pressure \(\sigma^{\prime}{}_{3}=300\) kPa, (**c**) stress-strain and pore pressure \(\sigma^{\prime}{}_{3}=400\) kPa, (**d**) stress paths.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(M\) & \(\lambda\) & \(\kappa\) & \(\mu\) & \(e_{r}\) & \(c_{b}\)/kPa & \(r\) \\ \hline
1.3 & 0.14 & 0.0134 & 0.3 & 0.03 & 30 & 3.43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material parameters for Intact clayey loess.  
model. They are further compared in Figure 12. The experimental data are better predicted by the present model as the real spacing ratio of intact loess (\(r\) = 3.43). This indicates that the structural strength and spacing ratio should be both considered in a constitutive model to capture the critical state, stress-strain, and stress paths of soils with a bonding structure. Besides, the implicit algorithm requires less time and shows high accuracy in predicting the mechanical behaviors of intact claye loess, especially when the spacing ratio \(r\) is larger than 3.0.
## 2 Materials and Methods
### Identification of miRNAs in Wax Gourd  
To obtain clean reads, low-quality reads containing more than one low-quality (Q-value \(\leq\) 20) base or unknown nucleotides(N) were firstly removed. Then, reads without 3'adapters, reads containing 5'adapters, reads containing 3\({}^{\prime}\) and 5\({}^{\prime}\) adapters but no small RNA fragment between them, and reads containing polyA in small RNA fragment were removed. Furthermore, the sequences smaller than 15 nt or longer than 35 nt were removed. All the downstream analyses were based on high-quality clean reads. The clean tags were aligned with small RNAs from the GeneBank database (Release 209.0) and Rfam database (Release 11.0) [34] to identify and remove ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA) and small nucleolar RNA (snoRNA), and other repeats using Bowtie software [35]. All of the clean tags were also aligned to wax gourd reference genome, and those mapped to exons, introns, or repeat sequences were also removed.  
Subsequently, the remaining reads were used to search against the miRBase database (miRBase 22, [[http://www.mirbase.org/](http://www.mirbase.org/)]([http://www.mirbase.org/](http://www.mirbase.org/)), accessed on 9 September 2020) to detect known miRNA. In order to display the difference with the existed 5p and 3p miRNAs, the known miRNA processed from the 5\({}^{\prime}\) arm or 3\({}^{\prime}\) arm of the miRNA precursor is followed by an \"x\" or a \"y\". All of the unannotated tags were aligned with the wax gourd reference genome [33]. According to the criteria of novel miRNAs [36], potential novel miRNAs were predicted using Mireap software ([[http://sourceforge.net/projects/mireap/](http://sourceforge.net/projects/mireap/)]([http://sourceforge.net/projects/mireap/](http://sourceforge.net/projects/mireap/)), accessed on 16 September 2020). Besides, the hairpin structures of novel miRNAs were predicted by Mireap_v0.2 software with default parameters setting.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 3 Results and Discussion
### Calculation of the Wind Profile Lidar's Carrier-to-Noise Ratio  
Figure 11 shows the relationship between the CNR and the detection distance (Baud ratio) drawn according to the above calculation method. As can be seen from the figure, the CNR approximately decreases with increasing detection distance. This shows that the dynamic variation range of the echo signals is large and the amplitude of the echo signal decreases with the increasing detection distance. The wind profile coherent wind measurement Lidar system is a weak signal detection system. Under the condition that the detection distance is as long as possible to ensure the accuracy of extracting the wind field information, the weak signal is always the difficulty in coherent wind measurement Lidar.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## Materials and Methods
### Test Site Description  
The test site is located in the southern part of the Parma University Campus (44\({}^{\circ}\)45\({}^{\prime}\)51\({}^{\prime\prime}\)Nj 10\({}^{\circ}\)18\({}^{\prime}\)33\({}^{\prime\prime}\)F). It encloses an area of about (550 \(\times\) 330) m\({}^{2}\) with buildings (from 6 m to 35 m high), roads, car parks, and meadows (see Figure 2). Trees are mostly located alongside the main road and in car parks.
## 4 CFD Simulation
### CFD Validation
#### 4.3.2 Validation of the Discharge Coefficient for SV Model  
A comparison between the CFD simulation values and the experimental values of the discharge coefficient for SV (\(C_{d}\)) is presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Outdoor Mean** & **Dominant** & **Average Temperature** & & \(C_{d}\) & \\  & **Wind Speed** & **Wind** & **Difference between** & **Simulated** & **Experimental** & **Relative** \\  & **(m/s)** & **Direction** & **Indoor and Outdoor (\({}^{\circ}\)C)** & **Value** & **Value** & **Error** \\ \hline
1–1 & 0.2 & 112.5\({}^{\circ}\) & 3.9 & 0.319 & 0.335 & 4.8\% \\
1–2 & 3.0 & 22.5\({}^{\circ}\) & 3.3 & 0.457 & 0.474 & 3.6\% \\
1–3 & 3.0 & 202.5\({}^{\circ}\) & 1.1 & 0.413 & 0.404 & \(-\)2.2\% \\
1–4 & 2.6 & 337.5\({}^{\circ}\) & 1.4 & 0.270 & 0.261 & \(-\)3.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between simulated and experimental values of the discharge coefficient under CV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Outdoor Mean** & \multicolumn{3}{c}{**Indoor and Outdoor Average Temperature**} & & \multicolumn{2}{c}{\(C_{d}\)\({}^{\prime}\)} \\ \cline{2-6}  & **Wind Speed** & **Difference** & & & \\ \cline{2-6}
**Number** & **(m/s)** & **Simulated** & **Experimental** & **Relative** & **Simulated** & **Experimental** & **Relative** \\  & **Value** & **Value** & **Error** & **Value** & **Value** & **Error** \\ \hline
2–1 & 2.0 & 15.5 & 16.6 & 6.6\% & 0.360 & 0.371 & 3.0\% \\
2–2 & 1.2 & 17.5 & 18.7 & 6.4\% & 0.363 & 0.356 & \(-\)2.0\% \\
2–3 & 1.0 & 18.3 & 19.8 & 7.6\% & 0.4 From Tables 6 and 7, it can be observed that the agreement between the CFD results and the experimental data is acceptable, and the CFD model can be used for subsequent simulations.
## 5 Proposed Contour Tracking Control Systems  
### Online System Parameters Identification  
To evaluate the control performances of the distinct control parameters evolved by the IABC algorithm, an accurate dynamic model of the LMs-driven X-Y-Y stage is required in the control system because the control parameters candidates cannot be applied to the control system of the drivers of the LMs-driven X-Y-Y stage, while the optimization is incomplete. Besides, immature control signals may degrade the positioning performance and destroy the stability of the multi-axis contour control system. To solve the above-mentioned problem, a mover positions observer is developed by utilizing a parallel system parameters identification approach, and the model parameters of the LMs-driven X-Y-Y stage shown in (11) and (12) can be identified online. Subsequently, the obtained dynamic model can be utilized to predict the mover positions by adopting the control laws with evolved control parameters, and additionally, the mover positions can further be used for real-time control signals evaluations. On the basis of the investigation from Sections 2-4, an optimal design of the contour tracking controller is further proposed to effectively achieve high-precision tracking in each axis of the LMs-driven X-Y-Y stage, while reducing the contour tracking error with fast convergence rate.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 6 Proposed Approach  
To solve the considered problem, the routing algorithm must take into account both the capacity of each edge and the edge's congestion. Toward this goal we have modified the Dijkstra algorithm for an SDN implementation that requires a global knowledge of the network and is performed centrally at the SDN controller. Being the goal the minimization of UTDs with a fair distribution we refer to the algorithm as _fair unsatisfied demands minimization_ (FUDM).
###### Abstract  
Pumped storage hydropower (PSH) enables the temporary storage of energy, including from intermittent renewable sources, and provides answers to the difficulties related to the mismatch between supply and demand of electrical energy over time. Implementing a PSH station requires two reservoirs at different elevations and with large volumes of water. The idea of using old, flooded open-pit quarries as a lower reservoir has recently emerged. However, quarries cannot be considered as impervious reservoirs, and they are connected to the surrounding aquifers. As a result, PSH activities may entail environmental impacts. The alternation of the pumping-discharge cycles generates rapid and periodic hydraulic head fluctuations in the quarry, which propagate into the surrounding rock media forcing the exchange of water and inducing the aeration of groundwater. This aeration can destabilize the chemical balances between groundwater and minerals in the underground rock media.  
In this study, two numerical groundwater models based on the chalk quarry of Obourg (Belgium) were developed considering realistic pumping-discharge scenarios. The aim of these models was to investigate the hydrodynamic and hydrochemical impact of PSH activities on water inside the quarry and in the surrounding rock media. Results showed that (1) water exchanges between the quarry and the adjacent rock media have a significant influence on the hydraulic head, (2) the frequency of the pump-discharge scenarios influence the potential environmental impacts, and (3), in the case of chalk formations, the expected impact of PSH on the water chemical composition is relatively limited around the quarry. Results highlight that those hydrogeological and hydrochemical concerns should be assessed when developing a project of a PSH installation using a quarry as a lower reservoir, considering all particularities of the proposed sites.  
pumped storage hydropower; energy storage system; hydropower; quarry; open pit; numerical modelling; environmental impacts; hydrochemistry +
Footnote †: journal: Applied Sci. 2021, 11, 4913. [[https://doi.org/10.3390/app1114913](https://doi.org/10.3390/app1114913)]([https://doi.org/10.3390/app1114913](https://doi.org/10.3390/app1114913))
# A High-Precision Active Vibration Isolation Control System: Experimental Study
###### Abstract  
Nowadays, the demand for high-precision devices is becoming more intense, thanks to the growing complexity and sophistication of modern technology and applications, including microscopy, nanomeasurement and analysis instruments. However, challenges arise because environmental factors such as vibration negatively affect their performance. An active vibration isolation system (AVIS) is one of the most recent solutions for that, but the high degree-of-freedom (DoF) nature results in it strongly suffering from unwanted interactions. Hence, in this paper, a robust decoupling controller is designed to handle the mentioned disadvantages. The system model is first presented, followed by the proposed decoupling techniques, and then, a feedback controller is designed by applying the mixed-sensitivity \(H_{\infty}\) control framework. Experimental studies are conducted to investigate the effectiveness of the proposed AVIS and compare it with other systems, namely, a passive vibration isolation system, a proportional-derivative (PD)-controlled AVIS, and a robust controlled AVIS. The robustness and decoupling performance of the proposed controller are guaranteed by suppressing external vibrations and isolating interactions, and, therefore, stabilizing the system.  
active vibration isolation system (AVIS); decoupling control; interconnected system; robust control +
Footnote †: journal: Applied Sciences
###### Abstract
In particular, by incorporating DeepESN into the developed AD algorithm, the temporal features of the time series data are extracted in order to predict the occurrence of an anomaly. In short, the main contributions of this work can be summarized as follows:  
* Industrial solution proposal: A modular and commercial IIoT architecture is proposed which combines a Seneca S406 Smart Meter and an embedded device to ensure the repeatability of our solution across all available production lines in a factory scenario and the low latency requirements of future smart manufacturing systems. Specifically, the embedded device at the edge collects information from the IIoT module connected to the production line using the Modbus-TCP/IP protocol and considers all the energy parameters needed to apply the fault detection method. * Anomaly detection modeling: The anomaly prediction problem is identified, experiments are carried out on a non-linear dynamic system, and a DeepESN-based AD method is proposed to solve the prediction problem. * Running at the Edge: Due to the limitations of the computational resources available in industrial plants, a model is proposed that can be trained and tested on devices embedded at the edge. The scheme proposes a deep network based on ESN units which captures complex temporal influences and requires low computational resources. * Results and evaluations: Extensive experiments are conducted in a real scenario to evaluate the performance of the proposed method. The dataset contains the time series values of the 88 variables, which are associated with energy parameters acquired using the industrial smart meter and IIoT sensors already available on the market in order to promote the replication our experiments. The results show that our method presents timelier error detection and achieves a higher prediction accuracy. The remainder of the article is organized as follows: Section 2 shows the works related to machine learning techniques used in the context of anomaly detection with a focus on those used for forecasting outcomes and requiring a few hardware resources. Section 3 describes preliminaries in introducing the two main architectures used for our purposes: ESN and LSTM. The proposed novel DeepESP-based anomaly detection method is introduced in Section 4, while Section 5 presents materials and methods describing the whole architecture from a hardware and software point of view. The experimental analysis, visualization of results, and discussion comparing different techniques are highlighted in Section 7. Finally, the last Section 8 points out the conclusions and future developments.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 6 Results
### MPTCP Throughput with Handover
However, the simulator is able to preserve the throughput trend observed among the congestion control algorithms in the testbed. The difference between the simulator and test results can be explained by examining the instantaneous MPTCP throughput for Wi-Fi and LTE subflows measured by the simulator and testbed during the handover period, as revealed in Figure 16. The figure clearly shows that the hard handover results in an almost zero throughput of the LTE interface during the handover period. The throughput drop takes around 2 s during the X2-based handover in the simulator, whereas the S1-based handover takes around 10 s. This is an anticipated behavior since the S1-based handover involves exchanging signaling messages through the core network (MME/SGW), which, in turn, increases the duration of the handover procedure when compared to the X2-based handover. A drop in the Wi-Fi throughput is also noticed during the X2-based handover for a shorter period than with LTE. In the testbed, the Wi-Fi throughput drops for a significantly shorter period than the simulator. This throughput behavior, as revealed in Figure 16, is observed with OLIA, LIA, and Cubic algorithms. It is attributed to the relatively slow update in the operation of the scheduler that tends to fill first the congestion window of the subflow with shorter RTT (LTE). However, the wVegas algorithm shows different performance with the Wi-Fi subflow during the handover period since the Wi-Fi throughput does not drop to zero or drops very briefly during the handover period, as shown in Figure 17, with the simulator and testbed, respectively. This is attributed to the fast ability of wVegas to adapt to network conditions. Figure 15: Setup 1 MPTCP throughput with handover for different congestion control algorithms. The impact of the presence of a bottleneck link is studied in the handover experiments performed using Setup 2, as presented in Figure 18. The throughput observed in the simulator is in a close match with the testbed throughput results for OLIA, LIA, and Cubic. On the other hand, the throughput difference between the simulator and testbed becomes a bit more noticeable with wVegas. In brief, with no bottleneck link, the MPTCP throughput simulation results are higher than the testbed results for all the studied algorithms when a handover scenario is performed during data transmission. However, the throughput results of the testbed match the simulator ones when the handover is performed in a network configuration with a bottleneck, except with wVegas, where the simulation throughput results are higher. Figure 16: Setup 1 Wi-Fi/LTE subflow throughput variation with time during handover occurrence. Figure 17: Setup 1 Wi-Fi/LTE subflow throughput variation with time for wVegas during handover occurrence. Figure 18: Setup 2 MPTCP throughput with handover for different congestion control algorithms.