esa-sceva/satcom-chunk-collection huggingface dataset:
# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 4 Experiment Result and Analysis
### Time and Frequency Analysis  
We took the gear of the gearbox's low speed shaft as the research object. The acoustical signal collected from the normal gear, gear with tooth fracture, gear with pitting, and gear with wear under two different load conditions.  
The left panel of Figure 7 shows the four types of gears in dataset A representing a no-load condition and the right panel shows the same type in dataset B representing a load condition. From the time domain signal, we can see that the waveform in three different types of gears, including tooth fracture, normal, and pitting, seemed to be obviously different in both datasets. The normal gears had a higher amplitude than the gears with a tooth fracture and the amplitude of pitting gears at certain time was 1 or \(-1\), which was stronger than that of normal gears. This indicates that the time domain contained sufficient information to distinguish these three types of gears. As for the type of pitting and wear in gears, the differences were too subtle to be seen from the time domain signal. However, from the frequency domain signal, we found that the magnitude and distribution of the spectrum amplitude between different types of fault signals were different, especially in the range of 0 to 1000 Hz in dataset A and 0 to 800 Hz in dataset B, which meant the four different types of gears in a meshing condition had an obvious difference in meshing frequency. The types of pitting and wear in gears could be easily distinguished according to the magnitude of the spectrum amplitude in the range of 0-1000 Hz and 3000 Hz in dataset A and the range of 0 to 800 Hz in dataset B. The above conclusions were also shown in time-frequency domain. The above observation also holds in the time-frequency domain. From the spectrograms of Figure 7 and waterfall diagrams of Figure 8, we can see that the maximum amplitude of the four types of gears was in the range of 0-1000 Hz. The type of wear in gears had a stronger spectrum amplitude than the other three types of gears at certain time points in two datasets, which meant the type of wear in gears could be easily distinguished from the other three types of gears in the time-frequency domain. The above analysis shows that the acoustical signal in time and frequency domain contained sufficient information with four different types of gears. Therefore, we propose to integrate the time and frequency domain signals as a sample matrix to directly input into an end-to-end stacked convolutional neural network. Then, the end-to-end stacked CNNs were trained to adaptively mine available fault characteristics for fault diagnosis without manual feature engineering.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 1 Introduction
### Limited Battery-Life: A Problem  
Figure 1 shows emerging 5G communications aiming to resolve the bandwidth constraint by utilizing the mm-wave spectrum [3]. Until recently, only the beachfront spectrum range had been used for wireless communications. Now 5G utilizes wireless bandwidth up to 300 GHz, excepting oxygen and water vapor absorption bands. This high-frequency spectrum supports large-bandwidth directional communications. Besides the attempts for usage of the extended wireless spectrum, there are various proposals to solve the energy constraints in wireless communications. These proposals include  
1. D2D communications for energy efficiency in mission-critical communication while focusing on disaster scenarios [4];
2. deployment of IoT in wireless communications to utilize energy more efficiently [5];
3. multiple wireless charging techniques proposed to enhance the lifetime of wireless devices [6];
4. various RF energy harvesting schemes to solve power issues in wireless communications [7].  
These power saving schemes extend the usage time of user equipment (UE) with limited battery power. However, limited battery life is a perennial problem that generally leads to the failure of communication systems. Thus, what could be the solution to this persistent problem? This problem motivates us to think about battery-less communications.
## 5 Conclusions  
The TKM successfully incorporates the \"essential\" chemical system using a generic hydrocarbon, RH, in conjunction with NO\({}_{\rm x}\) and O\({}_{3}\) for a typical rural agricultural regional atmosphere with RH having the same reactivity with OH as isoprene. The transport scheme in the TKM assures that significant concentration gradients exist only near the upper and lower boundaries of the CBL-Emissions of NO and RH and dry deposition of species cause gradients near the surface. The turbulence represented in the TKM produces significant segregation in the three reaction pairs among eleven reactions NO + O\({}_{3}\), NO + HO\({}_{2}\), and RH + OH for times up to 200 min. As represented in the TKM, turbulence in the CBL allows for build-up of significantly higher concentrations of NO, NO\({}_{2}\) and RH, while preventing the increase in O\({}_{3}\) concentrations compared to the CKM, in which the reactions proceed with no segregation due to turbulence. The similarity between the CKM results and those of the simple box model (BM) are nearly identical. This provides justification for the application of box model chemistry to represent the behavior of the boundary layer when turbulence effects are not included. However, the present study with the TKM indicates that turbulence effects should not be ignored in the convective boundary layer. Comparisons of the TKM with LES results are difficult because of differences in model structure and parameterizations. The LES gives lower coefficients of segregation for the principal reactions, thus representing the relatively small role of turbulence in affecting reaction rates. The TKM produces results similar to the LES for an increased molecular time scale, or equivalently, a decreased turbulent time scale. The sensitivity analysis, in which several model parameters and boundary conditions were varied, revealed less than linear changes in concentrations of the major species from changes in RH and NO emission rates. Lower emission rates lead to lower segregation effects in the principal reactions involving RH, NO and NO\({}_{2}\). The concentrations of the intermediate radicals OH and HO\({}_{2}\) are more sensitive to the variations than the more stable and abundant species. However, changing the chemical mechanism from the \"essential chemistry\" to CB-4 leads to relatively small changes in species concentrations and reaction segregations. Finally, it would be desirable to have a set of high-resolution time-concentration data of the key species in the CBL to provide critical comparison with the TKM and the LES model.
## 2 Materials and Methods
### Experimental Protocol and Procedure
#### 2.2.2 Dynamic Gait Task Measurements  
Once participants completed static foot structure measurements, a 5-min warm-up and laboratory environment familiarization were conducted before dynamic gait task measurements. Recordings were made in a standard laboratory room with normal indoor temperature and lighting design. A global reference system conforming to the standards of the International Society of Biomechanics was adopted [19]. All participants were instructed to perform two kinds of gait termination tests, including planned and unplanned gait termination. They were asked to walk barefoot at a self-selected speed along a 15 m walkway which contained a 2 m Footscan plantar pressure plate system (RSscan International, Olen, Belgium) in the center of the lab. Then, participants were instructed to stop walking, immediately, if they received an auditory signal delivered when their left foot first stepped onto the pressure plate and to remain still until told to move again. Otherwise, they needed a planned stop at the end of the pressure plate. The recording procedure of the dynamic plantar loading patterns followed a previously established protocol [3,6]. The auditory signals were initiated using a bell. Twenty-five percent of gait trials in each block included the ringing signal, while the remaining 75% did not. This was done so that participants were not able to anticipate sensory cues. There was a 2 min rest interval between both trials to minimize the effects of fatigue influencing the results of the experiment. Each subject was required to provide ten successful gait trial datasets, including five planned gait termination trials and five unplanned gait termination trials.  
A three-dimensional motion capture system (Vicon Motion System Ltd., Oxford, UK) with 8 infrared cameras was used to capture the kinematic data of the dominant lower extremity joints using a frequency of 200 Hz. Twenty-five reflective markers (diameter: 9.0 mm) were placed to define the pelvis, thigh, shank, forefoot, and rearfoot segments (Figure 1) [20]. The MTP joint was defined as the angle between the forefoot and rearfoot anatomical coordinate systems, referring to the definition approach by [PERSON] et al. [21]. The rotation axis was defined as the intermediate point between the first and fifth metatarsal heads and the connection between the markers of the first and fifth distal metataralsals [20,21].
## 1 Introduction
The technique has been applied successively for analysis of carbonyls in water (bidistilled water, ozonated drinking water, and rain water), indoor air, wine, and human blood in combination with PFBHA derivatization [13, 22, 23, 24, 25]. Three options are typically available to perform derivatization with SPME: derivatization inside GC injection port, direct derivatization in sample matrix and derivatization on the surface of the SPME fiber coating. Derivatization in the GC injector calls for injection of derivatization reagent into the injector before thermal desorption of SPME fiber, and derivatization proceeds afterwards. Higher sensitivity was reported in previous studies on aldehyde analysis in water with direct derivatization in a sample matrix (bidistilled water, ozonated drinking water, and rain water) [13], drinking water [10], aqueous particulate matter extracts of diesel exhaust and wood smoke [26], beer [27], spirits and alcoholic beverages [28], and diluted grape pomace distillates [29]. However, longer derivatization times--up to several hours--were needed in a diluted solution of derivatization reagent and carbonyl compounds [10, 13, 26, 28]. Extraction times with direct derivatization in solution was shortened to 20 min [30] with a HS autosampler and GC-MS.  
On-fiber derivatization with SPME continues to be the method of choice where high specificity of extraction from complex food and beverage-related matrices is needed. [PERSON] _et al._[31] developed a SPME-based method for _trans_- and _cis_-resveratrol in wine using an on-fiber silylation derivatization with bis(trimethylsilyl)trifluoracetamide (BSTFA). A summary of on-fiber derivatization with SPME-based methods applied to carbonyl analysis is presented in Table 1. To date, no studies report application of on-fiber derivatization to food processing water with suspended solids except for [32] (_i.e._, quantification of formaldehyde in municipal wastewater). Thus, there is a need to test this approach to multiple carbonyls in a highly complex matrix where apparent competition between analytes for derivatizing agent might be significant. The objective of this research was to develop a sampling and analysis method for quantification of carbonyl compounds in ozonated FL solution/suspension. SPME was combined with gas chromatography and mass spectrometry (GC-MS) with an optimized derivatization procedure for the study of byproducts from the ozonolytic breakdown of food colorants. Effects of dopant concentration, derivatization temperature and derivatization time were studied. PFBHA was selected as a derivatization reagent for its superior on-fiber derivatization performance [33]. 2-Furfural with an electrophilic carbonyl carbon, and a high steric hindrance was expected to have a low affinity to PFBHA and relatively poor detection limits reported in previous studies [26, 27]. The potential toxicity associated with this chemical renderedit necessary to focus specifically on achieving a lower detection limit compared with previous research and addressing apparent competitive extraction of 2-furfurfural from the matrix of many other carbonyl compounds.
## 6 Experimental Results
### Experimental Platform  
The experimental platform for IPMSM ITSC DWT data diagnosis is illustrated in Figure 22. A 400 W IPMSM, with specifications and parameters listed in Table 1, is used as the test motor. A 1.1 kW load motor is coupled to the test motor via a coupling, forming a dynamometer testbed. For fault diagnosis, external acquisition interfaces and current sensors are utilized for acquiring voltage and current waveforms, as well as performing analog-to-digital conversion, with the sampling rate set to 10 kHz. This study primarily records the three-phase voltages of the motor. Each data set, representing a 1 min recording, comprises 600,000 data points.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DWT Coefficient** & **Symbol** & **Frequency Range** \\ \hline Detailed Coefficient of the First Range & D\({}_{1}\) & 2500–5000 Hz \\ Detailed Coefficient of the Second Range & D\({}_{2}\) & 1250–2500 Hz \\ Detailed Coefficient of the Third Range & D\({}_{3}\) & 625–1250 Hz \\ Detailed Coefficient of the Fourth Range & D\({}_{4}\) & 312–625 Hz \\ Detailed Coefficient of the Fifth Range & D\({}_{5}\) & 156–312 Hz \\ Detailed Coefficient of the Sixth Range & D\({}_{6}\) & 78–156 Hz \\ Approximate Coefficient of the Sixth Range & A\({}_{6}\) & 0–78 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency range of each coefficients at 1500 rpm.  
Figure 21: Energy percentage at 1500 rpm with \(\mu\) = 0.7.  
Figure 20: Energy percentage at 1500 rpm with \(\mu\) = 0.3.  
The motor test conditions involve steady-state operation, with the rotational speeds set at 600 rpm and 1500 rpm, and the load set at 25% of the rated torque. The motor conditions are categorized into healthy and ITSC states, with the ITSC ratio set at \(\mu=0.3\). To prevent the shorted coil from burning out, a parallel resistor, \(r_{f}=\)10 \(\Omega\), is used. The experiment first involves the acquisition and compilation of motor data, followed by filtering out noise from the input data. The three-phase voltage dataset is then utilized to calculate \(V_{q}\), and the DWT is employed to obtain the wavelet coefficients of \(V_{q}\). To acquire comprehensive feature information, the IDWT is employed to reconstruct the original waveforms across various frequency bands, which is a crucial aspect of this research. Finally, the novel energy percentage method is used to quantify the characteristics of each frequency band, visualizing the IDWT waveforms, which allows for an effective assessment of the motor's condition.
## 3 Discussion
### Potential Applications and Developments  
In conclusion, this paper introduces a model for predicting the thickness of the layer with radial cracks in cylindrical rock specimens subjected to uniaxial compression with lateral pressure. Sources of potential modeling inaccuracies have been analyzed, with examples demonstrating the model's application and presenting calculation results in graphical and tabular formats (Figures 7-10 and 12 and Appendix A). A comparison with experimental data revealed a strong correlation between the predicted and observed results concerning the cracked surface layer. All data pertain to rocks and focus solely on radial cracks, which appear visually as axial (or longitudinal) cracks on the lateral surface. However, applied studies on microcracks and surface-layer cracks are equally crucial for cement and geopolymer concrete due to their impact on the durability and safety of buildings and other engineering structures.  
Since radial cracks precede spalling, studying the conditions that promote their occurrence and methods for controlling their state holds significant importance in mining and underground construction practices [34]. Therefore, further research is required to enhance models for accurately predicting crack formation and propagation [35]. Such models would allow for the prediction of primary damage within the surface layer before it evolves into macroscopic damage, which can ultimately lead to reinforcement corrosion and structural failure in concrete [36]. As such, the proposed model offers a valuable tool for applied research on crack formation in brittle materials [37; 38; 39].  
This paper is focused on a relatively small but practically significant problem: predicting cracks within the surface layer of pillars (Figure 11). As mentioned, longitudinal cracks on the lateral surface precede specimen spalling, and spalling, in turn, precedes shearing and ultimate failure (Figure 4). Consequently, the appearance and coalescence of longitudinal cracks serve as precursors to failure. Monitoring crack evolution and predicting the state of a brittle-material column using methods such as acoustic emission analysis [40; 41] and fractal analysis [42; 43], and using equally important results [44; 45; 46; 47; 48; 49; 50], expands the scientific basis for addressing this issue comprehensively.
# A Simulation Study of FRP-PCM Reinforcement for Tunnel Liniings with Void Defects
## 3 Results and Analysis
### The Influence of the Creep Phenomenon  
Creep is an important form of material flow deformation. By observing the deformation behaviors of many materials under tensile load, it can be found that under a constant temperature and constant load, the deformation of specimens will slowly increase with the increase in time, a process called the creep phenomenon. This is a narrow definition [36]. The creep analysis mode of Abaqus software usually uses three creep laws to describe the behavior of visco-plastic materials. The power law model can be applied to simulate creep behavior under isothermal and fixed loads, and the laws used are the time-hardening rate and strain-hardening rate relationship equations. To determine the goodness of the fit between creep parameters and reference experimental data curves (a mathematical analysis method used to obtain material parameters), the coefficient of determination R (R square) of regression analysis is used as the basis for judgment. The R value ranges from 0 to 1, and the closer R is to 1, the better the fitting result [37]. To simplify the calculation of creep problems in plastic structures (such as reducing the coupling between creep strain and other inelastic strains), the analysis can be divided into a static loading process and a subsequent analysis of the creep process. The static loading process is a time-independent loading process.
## 4 Results and Discussion  
This section presents numerical results for the streamlines, isotherms, and nanoparticle distribution for five parameters. These are number of oscillations \((1\leq N\leq 4)\), amplitude \((0.05\leq A\leq 0.15)\), Rayleigh number \((10^{4}\leq Ra\leq 10^{7})\), and nanoparticle volume fraction \((0\leq\phi\leq 0.04)\). The contents concerning the remaining parameters remain fixed at \(\Pr=4.623\), \(Le=3.5\times 10^{5}\), \(Sc=3.55\times 10^{4}\), \(N_{BT}=1.1\), and \(\delta=155\). Notably, the conservation equations remain entirely coupled. Such \(\mathbf{V}\) depends toward \(\phi\) through viscosity; \(\phi\) relies upon \(\theta\) mainly due to thermophoresis force; \(\theta\) relies on \(\phi\) by thermal conductivity including the Brownian and thermophoretic effects inside the energy equation; and \(\phi\) and \(\theta\) depend toward \(\mathbf{V}\) due to the convection phases within the nanoparticle momentum and energy equations, respectively. The employed thermo-physical features of the working liquid (water) and the solid Al\({}_{2}\)O\({}_{3}\) phases are listed in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Physical Properties** & **Fluid Phase (Water)** & **Al\({}_{2}\)O\({}_{3}\)** \\ \hline \(k\left(\mathrm{Wm^{-1}K^{-1}}\right)\) & 0.628 & 40 \\ \(\mu\times 10^{6}\left(\mathrm{kg/ms}\right)\) & 695 & – \\ \(\rho\left(\mathrm{kg/m^{3}}\right)\) & 993 & 3970 \\ \(C_{p}\left(\mathrm{J/kgK}\right)\) & 4178 & 765 \\ \(\beta\times 10^{5}\left(1/\mathrm{K}\right)\) & 36.2 & 0.85 \\ \(d_{p}\left(\mathrm{nm}\right)\) & 0.385 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermo-physical properties of water with Al\({}_{2}\)O\({}_{3}\) nanoparticles at \(T=310\) K [41].  
Figure 4: Validations of (**a**) average Nusselt number of the current numerical work with the experimental outcomes of [PERSON] et al. [38] and the numerical outcomes of [PERSON] et al. [37] for various Rayleigh numbers at \(\phi=\) 0.01 and \(N=0\), (**b**) thermal conductivity ratio with experimental outcomes of [PERSON] et al. [39] and numerical data of [PERSON] et al. [37], and (**c**) dynamic viscosity ratio with experimental outcomes of [PERSON] et al. [40] and numerical data of [PERSON] et al. [37].
## 9 Future Outlook toward 6G-Era  
Despite the transformative impact of 5G networks on user experiences and IoT applications, their limitations, particularly in remote or isolated regions, are undeniable. These areas lack terrestrial network coverage, hindering seamless access and service provision for 5G-enabled IoT applications. In response to this challenge, ongoing research and scientific trends highlight the need for continuous 5G standard evolution, particularly in the realm of non-terrestrial networks. By seamlessly integrating these networks with their terrestrial counterparts, we pave the way for the emergence of futuristic 6G networks [137].  
This shift in focus aims to address the limitations of current IoT connectivity and unlock new opportunities for remote applications [138; 139]. As we look towards the 2030s, the advent of 6G networks is poised to overcome these obstacles, fulfilling the fundamental need for ubiquitous connectivity [140; 141]. Research and industry are actively exploring the capabilities and requirements of 6G, with ultra-high frequency communications, artificial intelligence, edge computing, and non-terrestrial networks at the forefront [142; 143; 144]. These elements together will construct a fully networked 6G world, empowering remote IoT applications and emerging services. A 6G network provides the vision of a unified environment that seamlessly connects the globe, bridging the divide between remote and accessible areas[145; 146; 147].
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## 3 Proposed Optimal Range Prediction Filter Design  
To resolve the problems arising with conventional approaches, this section proposes the optimal gain setting method to optimize range prediction. To achieve this, the RMS-index-based design approach that was proposed in previous work [13] is adapted to an LFM \(\alpha\)-\(\beta\) filter. This approach can be considered as a modification of the maximum RMSE-based method for the minimization of errors in \(r_{\rm p,k}\), and not for \(r_{\rm g,k}\). Moreover, as the RMS-index-based approach does not require assuming process noise, the problems caused by the Kalman filter equations and described in the previous section do not exist.