esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Analysis of Built-in Sensor in Smart Device under the Condition of Voice Actuating
## 2 Feasibility of Built-in Accelerometer  
Current MEMS (Micro-Electro-Mechanical System) built-in accelerometers in smart devices are usually composed of three main components-an inertial mass, spring legs, and stationary fingers. As shown in Figure 1, a change in the capacitance between the stationary fingers would be caused by inertial mass moving when an accelerometer is working. This change is recorded as an accurate acceleration. Moreover, three separate sets of components could be employed to measure the accelerations separately in three directions.  
Owing to the limitations of hardware, capturing the voice signal is not the target of the built-in sensors in the smart device. Therefore, the accurate frequency response range under the condition of voice actuating should be determined.  
As shown in the Figure 2, we placed two smartphones on a table, and 0-22,000 Hz chirp sound signals were generated by a small louder-speaker which was fixed 10 cm vertical above the two smartphones. One of these smartphones was used to record the responding signal to the chirp sound signals. Meanwhile, the responding signal of the built-in accelerometer to the same chirp signals was recorded by another smartphone for comparison.  
Figure 1: The sketch of a MEMS built-in accelerometer.  
It is clearly found from Figure 3 that the built-in microphone of the mobile phone can successfully capture the chirp signal. In addition, the response signal was also an approximately straight line from 0 to 4000 Hz without aliasing. Conversely, the response signal frequency of the built-in accelerometer was mainly concentrated below 200 Hz, although the frequency of the input chirp sound signals was gradually increasing. This means that the built-in accelerometer usually filters out the sound signal above 250 Hz while arising nonlinearly distorted.  
Next, we use two chirp signals with different starting frequencies as input sound signals which are shown in the Figure 4. The response time-frequency diagrams of respond signals could be found in Figure 5. It was found that both microphone and accelerometer can capture the two chirp signals. Similarly, we also found that for multiple frequency signals, the response signal frequency range of the built-in accelerometer was still mainly concentrated below 200 Hz and nonlinear distortion also still existed.  
Figure 3: Time-frequency diagram of response signals to chirp signal. (**a**) Microphone (**b**) Built-in accelerometer.  
Figure 2: Two smartphones record chirp sound signals.
## 3 Analysis of the IWOLA + FFT Algorithm Characteristics
### Elimination of the Overlap Interference between the Adjacent Subchannels  
After the decimation by \(D_{1}\), the output data rate for each subchannel in the IWOLA filter bank was twice the value of the subchannel bandwidth. Therefore, after the \(N_{1}\)-point FFT, only the FFT bins that correspond to the actual frequency of each sub-band should be selected. Figure 5 presents the actual bandwidth and sampling frequency of each sub-band after the decimation. The actual sampling frequency of each sub-channel \(f_{\text{s1}}\) was equal to \(f_{\text{s}}/(D\times D_{1})\). The shaded parts represent the actual bandwidth \(B\) of each sub-band, \(f_{2}\) is the cut-off frequency of the frequency response characteristic of the aforementioned window function, and \(f_{1}\) is a mirror frequency of \(f_{2}\) relative to \(f_{\text{s1}}/2\). The number of selected bins for Sub-bands 0-\(K\)/2-1 \(N_{2}\) was obtained from (6), \(B\) = 2 \(\times\)\(f_{2}\) = \(N_{2}\)\(\times\)\(df\), instead of \(f_{\text{s1}}\), and \(f_{1}\) = \(f_{\text{s1}}\) - \(f_{2}\). For Sub-bands 0-\(K\)/2-1, their signals were complex. Therefore, it was necessary to select \(N_{2}\)\(N_{1}\)-FFT bins, in which \(N_{2}\)/2 \(N_{1}\)-FFT bins, which were numbered from zero to the point corresponding to \(f_{2}\), were sequentially selected; and the other \(N_{2}\)/2 bins were sequentially selected from the point corresponding to \(f_{1}\) to the point corresponding to \(f_{\text{s1}}\). Thereafter, a combination of the two parts of the bins can only cover the bandwidth of the complex signals in Sub-bands 0-\(K\)/2-1.  
Figure 4: Frequency response of the channels in the WOLA filter bank (where D denotes a decimating factor for the WOLA filter bank).  
Throughout the process of the proposed algorithm, the part of the \(N_{1}\)-point FFT bins that does not correspond to the actual bandwidth of each sub-band is discarded. This eliminates the signal overlap interference between the adjacent sub-bands. Moreover, the decimation of \(D_{1}\) reduces the operating frequency for further processing; thus ensuring the utilization of the computing resources.
## 2 Data and Methods  
### Study Area  
The Yangtze River, with its source in the Qinghai-Tibetan Plateau, flows eastward, through 19 provinces in China, to the East China Sea. The length of the river is 6300 km and its drainage area is 1.8 \(\times\) 10\({}^{6}\) km\({}^{2}\) and accounts for 18.75% of land area in China. The Yangtze River Basin, located between 90\({}^{\circ}\)33\({}^{\prime}\)-122\({}^{\circ}\)25\({}^{\prime}\) E and 24\({}^{\circ}\)30\({}^{\prime}\)-35\({}^{\circ}\)45\({}^{\prime}\) N, has a subtropical monsoon climate. The annual average temperature is 12.6-18.0 \({}^{\circ}\)C with a mean annual precipitation of 476 mm. The study area has a large altitudinal difference from Northwest Tibet Plateau (over 4000 m a.s.l.) to lowland areas such as the Yangtze River Delta Plain (below 50 m a.s.l.) (Figure 1). The region has diverse landforms. The superior climate and natural conditions are suitable for forest growth and forest resources are very abundant. The forest area of YRB is about 7.53 \(\times\) 10\({}^{5}\) km\({}^{2}\) and the forest coverage rate is 40.49% [18]. The area of natural forest is about 4.72 \(\times\) 10\({}^{5}\) km\({}^{2}\), and the area of artificial forest is 2.33 \(\times\) 10\({}^{5}\) km\({}^{2}\)[18].
## 3 Materials and Methods  
The proposed IPCL classification method for SESCCs can be summarized as shown in Figure 3.  
On magnifying endoscopy images of the esophagus, it is sometimes impossible to distinguish the microvessels from the background with a single threshold value due to the reflections and shadows that occur in the images. Thus, the ROI is extracted by removing unnecessary information, such as the image capture conditions, from the endoscopy images. The fuzzy stretching technique is applied to the extracted ROI to emphasize the contrast between the microvessel (low brightness value) and background area (high brightness value). [PERSON]'s binarization technique is applied to extract a candidate microvessel area from the ROI with stretched contrast. Subsequently, noise regions are removed from the extracted candidate microvessel area. A fast Fourier high-frequency filter is applied to the ROI with stretched contrast to extract the microvessel boundaries and remove them from the candidate microvessel area. In addition, the ART2-based quantization technique is applied to the ROI with stretched contrast to extract the background area. The final microvessel area is then extracted by removing the background from the candidate microvessel area. Efficient and accurate noise removal is essential as the proposed method relies on pixel clustering to separate the target organ area. Fuzzy stretching and ART2-based quantization procedures are applied to separate the microvessel area accurately as explained in Sections 3.1 and 3.2.  
Eight morphological features such as the \(x\)-axis, \(y\)-axis, perimeter, area, row dispersion, column dispersion, mixed dispersion, and roundness derived from the extracted microvessel area are input to the SVM algorithm as learning data to classify the data into the B1 and non-B1 types (i.e., B2 and B3 types) as explained in Section 3.4. In addition, as the thickness of the B-type microvessels is more than three times that of the B2-type microvessels, which is the criterion suggested by the JES, the B2 and B3 types were classified from the non-B1 type of previously learned data. To use this morphological characteristic, five microvessels of the B2 type are selected based on the ratio of their area to perimeter, and the thickness measurement method is applied, as explained in Section 3.5. After measuring the microvessel thickness, the largest thickness value among 10 representative microvessels is compared with the average thickness of the selected B2-type microvessel. If this ratio is \(\geq\) 3.7, the microvessel is classified as the B3 type.
## 2 Materials and Methods
### AML System
The All Materials Locator (AML) is a handheld utility detection device that operates based on ultra-high radio frequency (UHRF) sensing technology. Rather than relying on conductivity or acoustic propagation, the AML system detects subsurface objects by identifying differences in material density through modulated UHRF signals. These signals interact with buried structures that present dielectric contrasts, and the reflected signals are analyzed using proprietary digital signal processing algorithms to determine the location and depth of the target. While the AML system operates using ultra-high radio frequency (UHRF) sensing and not acoustic wave propagation, insights from previous ultrasonic detection studies are useful for understanding the broader challenges and principles of non-contact subsurface sensing. For instance, ref. [36] demonstrated that pulse-echo ultrasonic monitoring could evaluate sediment buildup along the interior walls of PVC pipelines with less than 2.5% error. Similarly, ref. [37] proposed a machine learning-based ultrasonic-guided wave technique to detect subsurface cracks in polyethylene pipes. Although these studies involve fundamentally different detection mechanisms, they illustrate the importance of density-related sensing in locating non-metallic underground utilities without requiring direct access. Ultrasonic-based detection systems have long been studied in the field of non-destructive testing (NDT). These systems offer high-resolution measurements and are capable of identifying structural anomalies in non-metallic infrastructure, such as PVC, PE, and fiber-optic cables [38; 39]. However, they also face notable limitations, including a reduced penetration depth in moist or heterogeneous soils and susceptibility to wave scattering, which can result in false positives or inaccurate localization [40; 41; 42]. These challenges underscore the advantage of UHRF-based devices like AML, which are not constrained by the acoustic properties of the surrounding medium. By relying on the electromagnetic density contrast rather than the wave velocity or attenuation, AML provides a robust alternative for detecting deeply buried non-metallic utilities, especially under complex soil conditions where acoustic methods struggle. Similar operational considerations for acoustic detection devices have been addressed in recent field-based evaluations, which emphasize the role of user technique and environmental variables in determining measurement accuracy [43]. A representative example of this process is illustrated in Figure 4, which demonstrates a step-by-step method for  
Figure 3: Schematic illustration of the time-of-flight principle used in subsurface detection. The red solid lines represent the original transmitted wave paths, and the green dashed lines indicate the reflected wave paths from a buried pipeline. This method operates based on the travel time difference between transmitted and reflected signals, similar to the working principle of ultrasonic detection systems. estimating pipe depth using the AML device at a 45\({}^{\circ}\) angle (SubSurface Instruments, 2024 [27]).
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Roles and Importance of Components and Parameters
#### 4.3.1 Digital Image Correlation  
A structure is regularly subjected over its lifespan to recurrent loads that might result in damage and imbalances from its original conditions. The application of DIC on pertinent surfaces suggests significant benefits in SHM. It is possible to identify a reduced kinematic foundation (consisting of \"modes\") and a statistical amplitude distribution for each mode by practicing with DIC since typical loads are statistically immobile. In the use of any DIC algorithm, the damage must be visible on the external structure with an ostensible crack opening [65]. A danger is not appropriate for SHM reliance if it is just weakly perceptible or underlying since it may not be discovered.  
A pedestrian bridge's movement can be monitored using 3D DIC. A bridge, particularly a pedestrian bridge, is subjected to static loads, hence, it is expected that the pedestrian bridge can have a deflection, and this can be monitored using cameras, which are installed by windows of an adjacent building. The use of CV eliminates the subjectiveness and erroneousness of conventional SHM. With the help of CV, it is possible to conduct SHMwith least disruptions to the surroundings and the structure itself [58]. The detection of displacement is crucial, especially for structures that will be subjected to long-term usage. By integrating CV and knowledge of the structure's behavior under certain conditions, the displacement monitoring of a pedestrian bridge is a feasible task; however, it is not only limited to bridges, as the 3D DIC measurement technique can also be used to monitor the behavior of other types of structures. However, as multiple sensors are installed, this entails costly implementation and maintenance.  
The use of DIC is also useful and efficient for displacement monitoring of crossbeams, particularly in an airport runway extension. For instance, ref. [66] focused on monitoring the displacement of the Madeira Airport runway extension, which is composed of a slab supported by frames. The Madeira Airport runway extension is subjected to loads when the landings from aircraft flying in an east-west direction take place, hence, it is expected that displacement may occur. By using DIC, the monitoring of displacement and strain fields of crossbeams is easier and more accurate. DIC measurement techniques are used to assess the permanent displacements of structures, as well as their behavior to dynamic loads. The current monitoring system utilizing the DIC technique is not reliable when it comes to obtaining strain values, still, it is successful in monitoring displacements and their evolution over time.  
In summary, the acclaimed features of DIC are to reveal deformation patterns on structures [47], evaluate the deflection caused by a static load [58], assess seismic response structures [34], and assess exterior displacement fields between damaged and not-damaged conditions [56; 67; 68].
## 2 The Assessment of the Energy Management System for HRES
### System Components  
The cost and characteristics of each component, such as lifetime, efficiency, and power curve, need to be figured out for the calculation in HOMER. Table 3 shows all the kinds of components used in the project, including their technical specifications, economic costs (investment cost, replacement cost, operation and maintenance cost), and the search spaces of their capacity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Month**} & **Daily Solar Radiation** & **Ambient Temperature** & **Average Wind Speed** \\  & **(kWh/m\({}^{2}\)/day)** & **(\({}^{\circ}\)C)** & **(m/s)** \\ \hline January & 3.149 & 23.40 & 9.33 \\ February & 3.739 & 23.41 & 8.39 \\ March & 4.834 & 24.17 & 6.88 \\ April & 5.262 & 25.29 & 5.86 \\ May & 5.939 & 26.54 & 4.95 \\ June & 5.229 & 27.03 & 5.57 \\ July & 5.378 & 27.11 & 5.58 \\ August & 4.966 & 27.17 & 5.60 \\ September & 4.529 & 27.24 & 6.05 \\ October & 4.079 & 27.11 & 8.47 \\ November & 3.194 & 26.00 & 9.96 \\ December & 2.993 & 24.34 & 10.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weather data in Basco Island.  
Figure 8: The typical daily load profile in Basco, Philippines.  
\begin{table}
\begin{tabular}{c c} \hline
**PV** & **Generic Flat Plate PV** \\ \hline
**Factors** & **Value** \\ \hline Nominal power & 1 kW \\ Materials & Polycrystalline silicon \\ Derating factor & 80\% \\ Slope & 21 degree \\ Ground reflection & 20\% \\ Lifetime & 25 years \\ Capital cost & 2500 US$/KW \\ Replacement cost & 2250 US$/KW \\ Operation and Maintenance (O\&M) cost & 10 US$/kW/year \\ Search space & 0\(\sim\)15,000 kW \\ \hline
**Battery** & **Generic 1 kWh Lead Acid** \\ \hline
**Factors** & **Value** \\ \hline Nominal capacity & 1 kWh \\ Maximum capacity & 83.4 Ah \\ Nominal voltage & 12 V \\ Maximum charge current & 16.7 A \\ Maximum discharge current & 24.3 A \\ Maximum charge rate & 1 A/Ah \\ Lifetime & 8 years \\ Capital cost & 700 US$/unit \\ Replacement cost & 500 US$/unit \\ O\&M cost & 10 US$/year \\ Search space & 0\(\sim\)25,000 kW \\ \hline
**Electrolyzer** & **Generic** \\ \hline
**Factors** & **Value** \\ \hline Lifetime & 25 years \\ Capital cost & 2250 US$/kW \\ Replacement cost & 2025 US$/kW \\ O\&M cost & 0.1 US$/op. hr. \\ Search space & 0\(\sim\)5000 kW \\ \hline
**Window Tunbines** & **Generic 10 kW** \\ \hline
**Factors** & **Value** \\ \hline Rotor diameter & 3 m \\ Rated power & 10 kW DC (at 12.5 m/s) \\ Voltage & 48V DC \\ Lifetime & 25 years \\ Starting wind speed & 3.31 m/s \\ Cut-off wind speed & 15 m/s \\ Capital cost & 50,000 US$/unit \\ Replacement cost & 45,000 US$/unit \\ O\&M cost & 500 US$/year \\ Search space & 0\(\sim\)1000 units \\ \hline \end{tabular}
\end{table}
Table 3: Technical and economic specifications of the system components.
# Matched-Filter Thermography
## 3 Matched Filtering
### Frequency Domain Interpretation of Matched Filtering  
Implementation of Equation (1) in instrumentation requires allocation of sizable computation power for calculation of the cross-correlation integral. In order to avoid this complication, cross-correlation can be computed from the spectrums of filter and response (i.e, the frequency-domain interpretation of MF) as:  
\[R_{xy}(\tau)\ =\ \Im^{-1}\big{\{}\Im\big{\{}x(t)\big{\}}^{*}\Im\big{\{}y(t)\big{\}} \big{\}}. \tag{3}\]  
Here, \(\Im,\Im^{-1}\) and \(*\) denote Fourier transform, inverse Fourier transform and the complex conjugate operator, respectively. Implementation of Equation (3) instead of Equation (1) in instrumentation considerably improves the efficiency of computations thanks to the optimal fast Fourier transform routines available in most computational and instrumentation commercial software such as Matlab R2018a (MathWorks, Natick, MA, USA) and LabVIEW 2017 (National Instruments, Austin, TX, USA). Moreover, computation power can be drastically increased by parallel computing and performance of the Fourier transforms on graphics processing units (GPUs), for example through the readily available CUDA platform on most Nvidia (Nvidia Corporation, Santa Clara, CA, USA) GPUs.
## 3 Results
### Numerical Modelling Augmentation
#### 3.7.2 Novel Numerical Techniques
They reported results on blood pressure, PWV, wall properties and axial velocity and radius variations, which presented good agreement with theory and empirical data. [PERSON] et al. [48] presented an electric analogue of the cardiovascular system (0D model) and found good agreement with in vivo data in the numerical results. [PERSON] et al. [116] compared in vitro measurements on a silicone model with results obtained from 1D models of the cardiovascular tree. The main purpose was to study the effects on pulse wave propagation of energy dissipation at vessel bifurcations. The numerical model presented small errors in pressure and flow measurements, thus proving to be able to accurately capture the main haemodynamic features. [PERSON] et al. [117] estimated planar WSS using 1D models of the thoracic aorta. The group of [PERSON] et al. [49] developed 1D models based on patient-specific data obtained via CT and MRI scans. The purpose of both works [49; 118] was to estimate PWV, which may be used to assess aortic wall compliance. The results were in good agreement with in vivo data. [PERSON] and [PERSON] [53] coupled a 1D model of the trans valvular aortic flow with a FEM model of the leaflets and compared the results with regular FSI simulations. The proposed 1D model presented good accuracy while reducing the computational time. [PERSON] et al. [52] performed the same analysis for ATAA models and found similar conclusions. [PERSON] et al. [119] developed 0D models of the cardiovascular system to extract correlations between model parameters (estimated with AutoRegressive Moving-Average models) and pathological flow conditions. Besides ROM, two other works from [PERSON] et al. [59] described the use of innovative techniques. These works resorted to the SPH technique to model AAA haemodynamics. Interestingly, although the aortic wall was not considered, a displacement field obtained via CT angiography was applied to the outside surface of the lumen. The results showed that velocity fields in rigid wall simulations underestimated blood pressure and overestimated velocity magnitude. Maximum WSS was similar but differed in distribution [60].
## 2 Materials and Methods
### Participants  
A priori sample size determination was conducted using G*power software (version 3.1.9.6., Universitat Dusseldorf, Dusseldorf, Germany). For a two-group ANOVA repeated measures design, considering two evaluations, within-between interaction, an effect size of 0.3 (medium), and a significance level of 0.05, a power of 0.95 was aimed for. The recommended sample size generated was 40.  
Recruitment was carried out at the Varsovia Football Academy within the Central Junior League, employing convenience sampling. A total of 76 potential participants were initially identified and screened against eligibility criteria, which included: (i) age between 13 and 15 years old, actively engaged in soccer practice; (ii) absence of lower limb and lumbar spine surgery or injuries within the last 6 months (hip, knee, ankle), no current pain in the hip, knee, or ankle joints, no hypermobility of the lower limb joints, and no history of neurological or connective tissue disorders; and (iii) availability for both assessment time-points. Following the exclusion of 16 participants for not meeting the age criteria and 10 for recent injuries within the last month, a total of 50 participants remained eligible for random assignment to study groups (Figure 1).  
To uphold allocation concealment, randomization took place prior to the initial assessment. This process utilized a simple randomization method facilitated by the research randomizer website. To preserve the integrity of the blinding process, evaluators remained unaware of participants' group assignments.  
Fifty adolescent male soccer athletes, averaging 15.0 \(\pm\) 0.9 years old, actively engaged in regional-level competition, volunteered for this study. As general anthropometric data, their average height was 167.3 \(\pm\) 4.3 cm and their average body mass was 64.5 \(\pm\) 2.8 kg, with an average of 4.9 \(\pm\) 1.2 years of soccer training experience. They adhered to a structured regimen comprising three training sessions per week, alongside weekend matches. Each session, lasting 80 to 90 min, generally included a warm-up focused on analytical drills (such as running, dynamic stretching of the lower limbs, and coordination exercises), followed by general strength and conditioning exercises (including resistance training, aerobic exercises, or speed drills, depending on the day of the week). The session then moved to the core segment, which emphasized technical and tactical development, and concluded with small-sided and large-sided games.