esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 4 Backpropagation Neural Network
### Feature Extractions
The interested frequency range of an STFT spectrum is divided into 10 frequency bands, and the corresponding spectral value of each frequency band is adopted as a feature value. Therefore, the feature extraction of four 5 s-windows yields 40 STFT spectral feature values. 4. Dimension setting of features. Considering that the number of feature values will affect the learning effect of the BPNN, two different dimensions of the feature are discussed in this study. Figure 4: Comparison of ground motions: (**a**) Acceleration time histories; (**b**) acceleration response spectra; (**c**) frequency spectra. One is to directly take the 40 STFT spectral values obtained in step (3). The other is to compute the maximum value of feature values in the same frequency range of four windows, which yields 10 feature values. Based on the above methods, the ground motion features of HECTOR and IMPVALL are extracted, as shown in Figures 5 and 6. Figure 5: Forty feature values of ground motions: (**a**) Features of 0–5 s time interval; (**b**) features of 5–10 s time interval; (**c**) features of 10–15 s time interval; (**d**) features of 15–20 s time interval. Figure 5a shows that the amplitude of IMPVALL in the range of 1.2-2.2 Hz is much higher than that of HECTOR. This result is consistent with the response spectrum results in Figure 4, and well reflects the destructive power of IMPVALL to structures in this frequency range. Figure 5b-d shows that the amplitude of IMPVALL is significantly lower than that of HECTOR. This is because the ground motion acceleration of IMPVALL is significantly lower than that of HECTOR in the time range of 7-20 s. From the above, the proposed feature extraction method based on STFT analysis can well reflect the frequency-/time-domain characteristics of ground motion sequences. The 40 feature values of ground motion are further condensed by computing the maximum amplitude value of feature values in the same frequency band from four STFT spectra, which yields 10 feature values. As shown in Figure 6, the 10 feature values can still reflect the great destructive power of the IMPVALL ground motion to structures in the frequency range of 1.2-1.6 Hz. Therefore, the following section will discuss the training results using 40 and 10 feature values, respectively.
# Thermo-Mechanical Properties of PLA/Short Flax Fiber Biocomposites
## 4 Results and Discussion
### Calorimetric Analysis  
Figure 8 shows the DSC thermograms of first and second heating run for neat PLA and its composites. The resultant thermal properties (T\({}_{g}\), T\({}_{cc}\), T\({}_{m}\), \(\Delta H_{m}\), \(\Delta H_{cc}\)) obtained from the first and second heating run are summarized in Tables 3 and 4, respectively. The crystallinity percentage (\(X_{cc}\)) of PLA and its composites is also reported. Furthermore, only the temperature of the second peak was reported as PLA melting temperature.  
Figure 8: Differential scanning calorimetry (DSC) thermograms of neat PLA and its composites with flax fibers: (**a**) first and (**b**) second heating run.  
As shown, in the first heating, the glass transition of amorphous PLA, which typically occurs at 60 \({}^{\circ}\)C, is overlapped with an enthalpy recovery peak, due to permanence of the samples at room temperature before the tests. This effect disappeared in the second heating having deleted eliminated the sample thermal history.  
The crystallinity slightly increased with the flax fiber content and at 40 wt.% fibers the crystallinity of PLA increased more than three times compared to pure PLA. A further trend that can be observed is the shifting of the cold crystallization peak to lower temperatures with the flax fiber amount. Consequently, the stiffness increment of the composites (Figure 2) with the increasing amount of fibers was due not only to the fiber stiffness but also to the PLA crystallinity increment.  
The results of the DMTA, showed in Figure 9, confirm the nucleating effect of the flax fibers for the crystallization of PLA [52; 53]. Both neat PLA and the composites start to soften after 60 \({}^{\circ}\)C (where the glass transition occurs); however, the decay of the elastic modulus is less marked for the composite with 40 wt.% of flax fibers. This result is in agreement with the DSC results in which the major crystallinity content is registered for this composite. More crystalline the polymer is, less marked is its elastic modulus decay near the T\({}_{\rm g}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PLA/Flax (wt,/wt.%)** & **T\({}_{\rm g}\) (\({}^{\circ}\)C)** & **T\({}_{\rm cc}\) (\({}^{\circ}\)C)** & **T\({}_{\rm m}\) (\({}^{\circ}\)C)** & **\(\Delta H_{m}\)** & **\(\Delta H_{cc}\) (J/g)
## 3 Results
### SPM (1D) of Kinematics of Lower Extremity Joints  
For the four stages of the fatiguing process (P1-P4), Figure 2a illustrates the joint angle-time curves in the sagittal plane (mean \(\pm\) SD). SPM (1D) analysis results with significant differences are illustrated in Figure 2b. At the hip, significant differences exist in the initial contact (I), impact loading \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **P1** & **P2** & **P3** & **P4** & \(p\) \\ \hline Joint angle at initial contact (\({}^{\circ}\)) & & & & & \\ CAH & \(46.08\pm 12.7\) & \(46.12\pm 10.32\) & \(42.2\pm 11.77\) & \(42.17\pm 11.77\) & \(c,d^{\circ},e^{\ast}\) \\ CAK & \(13.11\pm 7.43\) & \(9.65\pm 7.55\) & \(7.82\pm 6.78\) & \(7.87\pm 6.09\) & \(a,b,c^{\ast}\) \\ CAA & \(10.35\pm 8.05\) & \(6.81\pm 8.25\) & \(8\pm 8.47\) & \(6.42\pm 9.11\) & \\ Peak joint angle (\({}^{\circ}\)) & & & & & \\ PAH & \(74.08\pm 11.94\) & \(73.3\pm 9.93\) & \(67.34\pm 11.74\) & \(67.84\pm 12.78\) & \\ PAK & \(69.46\pm 8.77\) & \(65.43\pm 9.44\) & \(64.17\pm 10.26\) & \(62.46\pm 8.84\) & \(a^{\ast},b,c^{\ast}\) \\ PAA & \(-20.84\pm 5.7\) & \(-18.74\pm 5.2\) & \(-18.4\pm 4.9\) & \(-18.19\pm 6.55\) & \\ Time to peak joint angle (\({}^{\circ}\)) & & & & \\ PAH & \(41.63\pm 6.59\) & \(42.13\pm 8.79\) & \(38.88\pm 7.55\) & \(36.75\pm 6.94\) & \\ PAK & \(45.88\pm 6.51\) & \(38.63\pm 9.12\) & \(37.25\pm 10.44\) & \(33.63\pm 9.43\) & \(c^{\ast},f\) \\ PAA & \(14.13\pm 2.95\) & \(12.75\pm 2.82\) & \(12.38\pm 3.42\) & \(11.25\pm 2.71\) & \(a,b,c\) \\ Range of Motion (\({}^{\circ}\)) & & & & \\ Hip joint & \(44.57\pm 10.44\) & \(39.16\pm 5.09\) & \(34.54\pm 6.85\) & \(38.67\pm 10.8\) & \\ Knee joint & \(59.32\pm 7.31\) & \(56.63\pm 6.84\) & \(56.74\pm 7.95\) & \(55.07\pm 6.28\) & \(a,c\) \\ Ankle joint & \(34.11\pm 8.09\) & \(29.82\pm 9.46\) & \(34.92\pm 9.68\) & \(31.98\pm 8.23\) & \\ \hline \hline \end{tabular} Notes: CAH, CAK, and CAA: initial contact angle of hip, knee, and ankle joint, respectively; PAH, PAK, and PAA: peak joint angle of hip, knee, and ankle joint, respectively. P1–P4: the 1 st, 2 nd, 3 rd, and 4 th stages of the fatiguing process, respectively. \(a,a^{\ast}\): significant differences between P1 and P2 at 0.05 and 0.01 levels, respectively; \(b\): significant differences between P1 and P3 at 0.05 level; \(c\): significant differences between P1 and P4 at 0.05 level; \(d^{\ast}\): significant differences between P2 and P3 at 0.01 level; \(e^{\ast}\): significant differences between P2 and P4 at 0.01 level; \(f\): significant differences between P3 and P4 at 0.05 level.  
\end{table} 
Table 2: Kinematics variables in the four stages of fatiguing process (P1–P4) from pre- to post-fatigue protocol in the sagittal plane (mean \(\pm\) SD). (II), weight acceptance (III), and pre-drive-off (IV) phases (P2 vs. P3 (I and II: \(p\) = 0.01; III and IV: \(p\) \(<\) 0.001); P2 vs. P4 (I-IV: \(p\) \(<\) 0.001)). At the knee, significant differences were found mainly in the weight acceptance and pre-drive-off phases (P1 vs. P2 and P1 vs. P4: \(p\) \(<\) 0.001).
## 3 Results
### Pseudo-Total Cd in Soil Profiles  
The quantification of total soil Cd was performed for the 27 farms assessed in this study. The highest total soil Cd was detected in farms from San Vicente de Chucuri and El Carmen de Chucuri, in Santander (2.76 mg\(\cdot\)kg\({}^{-1}\)\(\pm\) 0.8 Cd on average), followed by farms located in Munoz, Boyaca (2.01 \(\pm\) 0.2 and 2.46 \(\pm\) 0.3 mg\(\cdot\)kg\({}^{-1}\) Cd on average, respectively). In contrast, lower Cd content was found in farms located in Arauca (1.16 \(\pm\) 0.4, 0.74 \(\pm\) 0.6, and 0.36 \(\pm\) 0.3 mg\(\cdot\)kg\({}^{-1}\) Cd on average) and in some farms from Maripi and Rionegro, in Boyaca and Santander, respectively (0.73 \(\pm\) 0.2 and 0.50 \(\pm\) 0.5 mg\(\cdot\)kg\({}^{-1}\) Cd on average).  
Regarding total soil Cd per boundary, the Ap and C boundaries were related to high Cd content in soil profiles in farms located in Munoz, whereas Ap and B boundaries were related to Cd content in the soil profiles from San Vicente de Chucuri. In farms from El Carmen de Chucuri, the 2D-ERT profile shows that the Ap and B boundaries were also found to have higher Cd content (1.92 \(\pm\) 0.7  
Figure 6: Inverse model resistivity section plots of farms in Santander. (**A**) El Carmen de Chucuri (1813 Ohm\(\cdot\)m); (**B**) Rionegro (1729 Ohm-m); and (**C**) San Vicente de Chucuri (1837 Ohm-m). The closely related cadmium compounds are highlighted in red/violet colors ranging from 1002–1837 Ohm\(\cdot\)m.  
and \(2.24\pm 0.4\) mg\(\cdot\)kg\({}^{-1}\), respectively), with a pit distribution of Cd close to San Vicente de Chucuri values (both located in the southwest Santander district). In Arauca, the lower concentrations of soil Cd (\(0.14\pm 0.2\) mg\(\cdot\)kg\({}^{-1}\)) were also related to low resistivity mean values (401 Ohm\(\cdot\)m).  
Regarding other soil parameters, in the Araucan farms, higher Ca and P content were found (\(24.79\pm 0.4\) and \(1143.97\pm 0.9\) mg\(\cdot\)kg\({}^{-1}\), respectively), relating to a low Cd content and resistivity values (\(0.23\pm 0.2\) mg\(\cdot\)kg\({}^{-1}\) and 412 Ohm\(\cdot\)m, respectively) at boundary Ap as an isolated case. However, in farms from Boyaca and Santander, higher P content (\(126.88\pm 0.2\) and \(51.63\pm 0.7\) mg\(\cdot\)kg\({}^{-1}\), respectively) was related to higher Cd/resistivity values (\(2.01\pm 0.2\)/1305 and \(2.76\pm 0.6\)/1815, respectively) in both, B and C boundaries.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Results
#### 5.2.5 Packet Loss  
Each network has an average packet loss. Of course, we also know that the packet loss ratio falls in the descending order of WiFi-LTE-UMTS, which can be estimated by using Erlang models [37]. This difference is explained by the fact that WiFi uses the same carrier for multiple users simultaneously, whereas it is divided by time slots in the UMTS and LTE networks.  
Handover in wireless networks normally produces packet loss, delay, and jitter, thereby significantly degrading network performance and service quality. The effect of handover in packet loss is shown in Equation (17).  
\[P_{packetloss}=N_{handover}\times L_{handover}(\text{s})\times X(\text{pps}) \tag{17}\]  
where \(P_{packetloss}\), \(N_{handover}\), \(L_{handover}(\text{s})\), and \(X(\text{pps})\) are the number of packet losses, the number of handovers, the delay time per handover made and the packet arrival rate (packets per second), respectively. Equation (17) reveals that the number of packet losses is linear with the number of handovers. As a result, minimizing the number of handovers of MADM with CCN leads to a significant decrease in dropped packets and the packet loss ratio.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion
### Comparison with Existing Positioning Techniques
#### 5.2.4 Sonar-Pulse Distance Sensing  
Ultrasonic waves are sent from the sensor and then collected back to estimate the distance of an object from UAV. This method has several restrictions related to environmental noise, UAV flight altitude, and a low resolution. In addition, this method is irregular if too much noise is present in the environment.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 4 Analysis and Discussion of Data Results
### Channel Capacity  
The SNR at the receiving end can reach between a few dB and tens of dB; in this paper, we choose the two conditions of small SNR: 5 dB and large SNR: 40 dB, respectively. As shown in Figure 18, the channel capacity of the planar circle array MIMO communication system is slightly larger than that of the angular circle array MIMO communication system, while the channel capacity of the SISO communication system is significantly smaller than that of the other two systems. At 5 dB and a communication distance of 10 m, our system achieves a channel capacity improvement of 6.64 dB compared to the SISO system. At 40 dB and a communication distance of 10 m, our system achieves a channel capacity improvement of 6.73 dB compared to the SISO system. This is because MIMO communication systems have a larger number of transceivers, resulting in more photons being emitted, which increases the channel capacity. However, in the angular circle array MIMO system, many transceivers have angular offset settings, and the path loss for the side transceiver pairs is greater. Therefore, the channel capacity is lower than that of the planar circle array MIMO communication system at the LOS link.  
Figure 16: CIR fitting for planar circle array MIMO communication system when \(L=2m\): (**a**) \(h_{11},h_{22}\) and (**b**) \(h_{12},h_{21}\).  
Figure 17: CIR fitting for angular circle array MIMO communication system when \(L=2m\): (**a**) \(h_{11}\), \(h_{22}\) and (**b**) \(h_{12}\), \(h_{21}\).  
Comparing Figures 18 and 19, it can be seen that the channel capacity is reduced for all communication systems under the same SNR condition with transmitter deflection. At 5 dB and a communication distance of 10 m, our system achieves a channel capacity improvement of 13 dB compared to the SISO system. At 40 dB and a communication distance of 10 m, our system achieves a channel capacity improvement of 10 dB compared to the SISO system. This reduction is due to the transmitter deflection breaking the LOS link, resulting in fewer photons being received, an increase in path loss, and hence a reduction in channel capacity. Additionally, we can observe that the channel capacity of the angular circle array MIMO communication system is greater than that of the planar circle array MIMO communication system when the transmitter is deflected. This is because the special structural design of the angular circle array MIMO system mitigates the path loss caused by NLOS links.
# Research on the Attitude Control Strategy of TBM Digging with V-Shaped Propulsion System
## 5 Experimental Verification  
To validate the effectiveness of the V-TBM digging attitude control strategy proposed in this paper, a small turning radius digging attitude control experiment was conducted using the V-TBM built by a company. As shown in Figure 10, the TBM on-site digging measurement interface allows for the observation and evaluation of the entire TBM digging attitude through this interface.  
As an example, a section of field conditions is considered, where the TBM transitions from the straight section to a small curve with a radius of R = 30 m. The proposed digging attitude control strategy from this paper is applied to regulate the TBM digging attitude.  
Figure 10: TBM on-site excavation measurement interface.  
Figure 9: Excavation pose following parameters.  
The horizontal and vertical errors from the circular curve digging section are derived using the field digging measurement interface, as shown in Figures 11 and 12.  
From the error curve, it can be observed that the maximum absolute error of the horizontal attitude during the circular curve digging process is 84 mm, with the average error being less than 20 mm. The maximum absolute error of the vertical attitude is 90 mm, with the average error being less than 22 mm, which is consistent with the pre-test error. The experiment demonstrates that the TBM digging attitude control strategy proposed in this paper successfully achieves attitude regulation and control during the TBM small curve digging process.
## 4 Results
### Development Phase Observations
GPS tracking was also incorporated in order to detect the location of the patient upon detecting a fall event. As the system is still in its final stages of development, the experimental evaluation of the Monit4 Healthy system so far has shown tangible gains in communication efficiency, confirming the effective application of structured data processing and communication techniques. The outcomes demonstrate that the system consistently gathers and processes biomedical signals while maintaining steady and dependable data exchange in a laboratory environment. Performance assessments further show that the system successfully reduces transmission inconsistencies, preserving an ongoing communication flow under various operational conditions. In enhancing the dependability and continuity of real-time data exchange, the Monit4 Healthy system has the potential to overcome important challenges in connected health ecosystems, which is in line with the goals of next-generation IoT health monitoring systems. A preliminary laboratory evaluation of the system's reliability focused on evaluating fault tolerance, connection resilience, and signal consistency throughout extended usage. No significant issues were found over multiple continuous 8 h testing sessions, and under controlled conditions, the system maintained consistent wireless connectivity with a packet loss rate of less than 1%. Real-time data transmission was uninterrupted, and sensor signal integrity was maintained. Although these results show dependable short-term performance, further research to guarantee long-term dependability in various healthcare contexts will still need to include large-scale deployment measurements and prolonged runtime evaluations (such as the Mean Time Between Failures). These findings demonstrate that the Monit4 Healthy system, in its current laboratory configuration, achieves stable performance and efficient resource utilization under typical monitoring conditions, validating its readiness for further clinical validation and optimization.
## 1 Introduction
The Low Frequency Atmospheric Weather Electromagnetic System for Observation, Modeling, and Education (LF AWESOME) has a bandwidth of 0.5 kHz to 470 kHz, timing accuracy of 15-20 ns, magnetic field sensor sensitivity of 0.03 \(fT/\sqrt{\mathrm{Hz}}\), and electric field sensor sensitivity of 0.677 nV/(m\(\sqrt{\mathrm{Hz}}\)). It can be used for global detection of lightning discharges, monitoring transmission signals in the long- and medium-wave bands, undersea and subsurface sensing and communications, navigation and timing, and remote sensing of the ionosphere and magnetosphere [23; 24]. Based on the previous literature, we summarize the overall performance of long-wave broadband receivers proposed in recent years, as shown in Table 1. This table briefly lists the parameters of sensors or receivers proposed in key studies over the past 20 years. Notably, some sensitivity parameters mentioned in the abstracts of these papers represent the theoretical limits achievable with large antenna configurations (25 m\({}^{2}\)) within the operating bandwidth, and sensitivity can fluctuate significantly across this bandwidth. This aligns with the focus of their research, which primarily examines the limits of such sensors, the effects of different design methods on sensor parameters, or requires a detailed study of specific physical phenomena. In contrast, our work primarily focuses on designing a more stable, compact long-wave receiver system with consistent sensitivity across the operating bandwidth. As a result, these parameters are not directly comparable to our work,and specific values should be carefully reviewed in the original papers for a more accurate comparison. The parameters provided in the table are those proposed in these studies and can be compared with our work, though they may differ from the general descriptions in prior works. However, both descriptions are correct in their respective contexts. Based on the information from the table and the specific literature cited, in the work presented in this paper, we have designed a receiver with high synchronization accuracy and a minimal antenna size, while meeting the sensitivity requirements for the application. The high timing accuracy (10 ns) ensures higher direction-finding accuracy and resolution when constructing an interference network. The device's ability to simultaneously collect horizontal magnetic field and vertical electric field signals, combined with its compact size, significantly enhances its versatility. Furthermore, our receiver exhibits an exceptionally flat sensitivity response across the operating bandwidth, a feature not present in most of the receivers discussed above. This ensures that wideband signals, such as lightning signals, remain undistorted across the entire bandwidth. The system can be effectively applied in direction finding, ionospheric remote sensing, lightning signal research, and other related fields.
# All About Audio Equalization: Solutions and Frontiers
## 3 Parametric Equalizers
### High Order Filter Designs  
In most audio production applications, low order filters are used. The smooth transitions are often advantageous in that the effect of equalization is subtle and not perceived as an artefact. However, they lack the ability for fine control. To address this, higher order IIR filters can be employed. They provide steep transitions at crossover frequencies for shelving filters, or at the upper and lower crossover frequencies for peaking and notch filters.  
[PERSON] suggested techniques for the design of high order minimum phase filters based on a variety of classic filters: Butterworth, Chebyshev I, Chebyshev II, and elliptic polynomials [39]. [PERSON] and [PERSON] derived high order Butterworth shelving filters [40]. [PERSON] _et al._ focused on the design of such filters based on a parallel connection of two stable allpass filters [41]. [PERSON] and [PERSON] modeled analog parametric equalizer responses accurately using high order IIR filters [42]. However, all these high order designs begin with analog prototypes. But it is possible to design high order filters entirely in the digital domain, as described in [4]. We summarize the digital approach next.  
Consider a prototype first order, lowpass filter with crossover frequency \(\omega_{\rm c}=\pi/2\);  
\[H(z)=\frac{1+z^{-1}}{2z^{-1}}. \tag{31}\]  
Replace the pole at 0 by \(N\) poles given by  
\[p_{k}=j\tan\left(\frac{(2k-N-1)\pi}{4N}\right) \tag{32}\]  
and scale the transfer function to again have unity gain at \(z=1\). So our \(N\)th order prototype filter becomes  
\[H_{\rm P}(z)=\prod_{k=1}^{N}\frac{1}{2\cos\gamma_{k}}\frac{1+z^{-1}}{-j\tan \gamma_{k}+z^{-1}},\quad\text{where }\gamma_{k}=\frac{\pi[(2k-1)/N-1]}{4},\text{with }k=1,2,...,N. \tag{33}\]  
This filter has the same crossover frequency and same behaviour at DC and Nyquist as the first order filter. But now the poles result in a sharper transition at \(\pi/2\). Then, simple transformations may be applied to each first order section in order to transform this prototype filter into any of the standard designs. The steps to follow in these transformations are depicted in Figure 6.