esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Establishment of Semi-Active Control System
### Design Parameters of the Semi-Active Control System  
Once the power matrix coefficient is determined through the analysis in the previous section, the active optimal control force can be determined through the active optimal control algorithm LQR and the design parameters of the MR damper can be determined according to the following method.  
Assuming that the maximum damping force of the MR damper is equal to the active optimal control force at the corresponding active control position of the bridge and further assuming that the structural vibration absorption effect of the MR damper of the bridge structure is the same as the active optimal vibration absorption effect, then we have  
\[u_{ismax}=c_{id}\Big{|}\dot{x}_{is|u_{ismax}}\Big{|}+f_{idymax}=c_{id}\Big{|} \dot{x}_{ij|u_{ismax}}\Big{|}+f_{idymax}=u_{imax} \tag{15}\]  
where \(i\) is the position of the MR damper; \(u_{ismax}\) and \(u_{imax}\) denote the maximum control force of the \(i\)th MR damper and the active optimal control device, respectively. \(\dot{x}_{is|u_{ismax}}\) and \(\dot{x}_{i|u_{imax}}\) denote the relative velocities of the maximum semi-active control force and maximum active control force, respectively, in the position of \(i\)th MR damper and the active control device.  
Assuming that the minimum Coulomb force \(f_{idymin}\) of the MR damper is 0 and the adjustable coefficient \(s\) is 8, it follows from Equation (15) that  
\[u_{ismax}=sc_{id}\Big{|}\dot{x}_{is|u_{ismax}}\Big{|}=sc_{id}\Big{|}\dot{x}_{ i|u_{imax}}\Big{|}=u_{imax} \tag{16}\]  
Then, the passive viscous damping coefficient of the \(i\)th damper can be calculated as  
\[c_{id}=\left|\frac{u_{imax}}{\left(s\dot{x}_{i|u_{imax}}\right)}\right| \tag{17}\]  
The maximum Coulomb force of the \(i\)th damper is calculated as  
\[f_{idymax}=\left(s-1\right)\,c_{id}\Big{|}\dot{x}_{is|u_{ismax}}\Big{|} \tag{18}\]  
Through the design method of MR dampers mentioned above and the calculation results of active optimal control, the parameters related to the semi-active control system were designed. The design parameter values are shown in Table 2.  
Based on the calculation results in Table 2, the MR damper passive viscous damping coefficients for the PC continuous girder bridge with CSWs were finally set at 81,410 N\(\cdot\)s/m, 124,839 N\(\cdot\)s/m, 81,307 N\(\cdot\)s/m, and 81,263 N\(\cdot\)s/m, respectively and the maximum Coulomb forces of the dampers were set at 221,620 N, 783 N, 221,795 N, and 221,848 N, respectively. It can be seen from the data that the maximum Coulomb force of the MR damper design is smaller than that of similar semi-active control system of a concrete bridge [32] due to the lighter dead weight of the PC continuous girder bridge with CSWs and the lower axial stiffness, which reduces the design cost and is more economical.
# Emerging Technologies for Automation in Environmental Sensing: Review
## 2 Methodology  
The literature review on automation for environmental monitoring involves thoroughly exploring diverse sources, including esteemed academic journals, conference proceedings, and scientific publications, employing rigorous selection criteria centered on relevance, recency, and credibility. Keywords like \"environmental monitoring\", \"transfer learning\", \"automation\", \"sensors\", \"UAVs\", \"robotics\", \"IoT\", \"edge computing\", and \"deep learning\" guide the identification of pertinent articles, revealing significant advance  
Figure 1: Integration of automation, robotics, and edge computing in environmental sensing.  
ments such as unmanned aerial vehicles (UAVs) and underwater robots for data collection, the integration of wireless sensor networks for real-time monitoring, and the application of machine learning algorithms for data analysis. Furthermore, the emergence of edge computing for decentralized data processing is noted. Challenges like interoperability, security, and ethical considerations surrounding autonomous systems in environmental monitoring are also underscored. This synthesis of the literature offers profound insights into cutting-edge technologies, trends, and future trajectories in automation for environmental monitoring, with a focal point on enhancing efficiency, accuracy, and sustainability in data collection and analysis. This comprehensive review was conducted through a thorough exploration of databases like \"Scopus\", \"IEEE Explore\", and \"Google Scholar\", alongside scrutiny of reputable journals, including \"MDPI\", \"Elsevier\", \"Sensors\", and \"IOPScience\", reflecting a systematic and exhaustive approach. Initially yielding 200 results, the process was refined to 128 publications through the classification procedure. This foundal work will not only expand current understanding but also lay the groundwork for future research and development in this crucial domain.
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 3 Downlink Scheduling in LTE  
Dynamic resource allocation, called the packet scheduler algorithm, is an essential feature in network communication and aids in controlling the selection of RBNN for the user equipment (UE) to prevent signal interference. A scheduler algorithm helps to acquire an adequate allocation of physical resource blocks (PRBs) (e.g., frequency, power, time, etc.) to the UEs that fulfil the QoS requirements, according to scheduling standards, for enabling a fair distribution of the available resources amongst the users [21]. The downlink scheduling algorithms are described using the metric \(M_{i,k}\), in which resources were allocated to each UE based on the parallel between the metrics \(i\) of the users who had the highest \(M_{i,k}\) values and the \(k\) RBFNN that was allocated. The popular downlinkscheduling algorithms have been described below and categorized based on their service for real-time and non-real-time applications.
## 3 Un15
### Data Processing  
To ensure data consistency and facilitate subsequent analysis, all collected audio recordings were processed using a fixed-length segmentation approach. Specifically, we employed Audacity 3.6.4, an open-source audio editing software, to segment each recording into non-overlapping 5 s clips. For recordings longer than 5 s, multiple independent 5 s segments were extracted, whereas recordings shorter than 5 s were discarded to maintain dataset uniformity. Each segmented audio clip was stored in WAV format with a sampling rate of 44.1 kHz, a bit depth of 16 bits, and a single audio channel (mono). To mitigate discontinuities caused by abrupt cuts, care was taken to preserve key portions of the original recording. After this segmentation and selection process, a total of 1620 high-quality audio clips were obtained. Figure 4 presents the number of audio clips per defined category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Sensitivity** & **Dynamic Range** & **Frequency Response** \\ \hline Scien CM-73 & \(-\)33 dB re 1 V/Pa (22 mV/Pa) & 135 dB & 20 Hz–20 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of the directional microphone employed in the recording setup.  
Figure 4: Distribution of audio clips across the 15 categories in the UN15 dataset. Each bar represents the number of labeled clips per sound class, highlighting the dataset’s category balance and real-world diversity.  
In terms of labeling, all audio clips were manually annotated by three graduate students with expertise in acoustics and computer science. Although formal consistency metrics such as Cohen's kappa were not employed, each retained sample required at least two annotators to reach agreement before being included in the final dataset. For ambiguous or multi-source audio clips, we referred to the labeling strategy of UrbanSound8K [8], using audio editing software to isolate specific time intervals containing relevant events. In cases involving multiple overlapping sound sources, annotators subjectively determined whether the target sound was perceived as foreground. If the sound was unclear or annotator disagreement occurred, the segment was discarded. This hybrid approach ensured both the reliability and usability of the labeled data.  
Following standard deep learning training protocols that balance model generalization and evaluation stability, the dataset was split into a training set (75%) and a test set (25%). To preserve data integrity, each audio clip retained its original metadata, including recording device specifications, environmental context, and timestamps. By implementing this standardized segmentation and labeling procedure, we significantly enhanced the dataset's consistency, reproducibility, and applicability for deep learning-based urban sound classification tasks.
## 2 Computation of the Inertial Characteristics of the AD Elements for the FBV Movements  
To use the AMA, it is necessary to compute the equations of the FBV movements for the AD elements, so it must be established from the beginning the movements of each AD element. Therefore, it was considered that the tulip and the bowl had deflections and rotations specific to the rigid body movements. In contrast, the midshaft had continuous deflections and rotations typical to a continuous elastic beam. These assumptions agreed with the technical reality because the rigidity of the tulip and the bowl are more significant compared with the midshaft's rigidity. Each AD element has its referential system, presented in Figure 5, namely the cartesian systems \(X_{1}Y_{1}Z_{1},X_{2}Y_{2}Z_{2},X_{3}Y_{3}Z_{3}\). The planes \((X_{1}Y_{1}),(X_{2}Y_{2}),(X_{3}Y_{3})\) are coplanar planes, so the axes \(Z_{1},Z_{2},Z_{3}\) are parallel, and the movements considered for each AD element for the FBV movements are as follows (see Figure 5):  
Figure 4: Details of the bowl-inner race (inner race fixed by splines on the midshaft) joint. Figure 5: Schematic representation of an automotive driveshaft. The rigid torsion rotation angles \(\varphi_{1}\), \(\varphi_{2}\), \(\varphi_{3}\) concerning the axes \(X_{1},X_{2},X_{3}\) for the tulip, midshaft, and bowl;
* The rigid deflections in bending \(w_{1},w_{3}\) concerning the axes \(Z_{1},Z_{3}\) for the tulip and bowl;
* The rigid twisting angles in bending \(\Phi_{1},\Phi_{3}\) concerning the axes \(Y_{1},Y_{3}\) for the tulip and bowl;
* The rigid rotation angle \(\beta_{1}\) of the tulip axis \(X_{1}\) concerning the midshaft axis \(X_{2}\);
* The rigid rotation angle \(\beta_{2}\) of the bowl axis \(X_{3}\) concerning the midshaft axis \(X_{2}\);
* The elastic deflection in bending \(w_{2}(x,t)\) concerning the midshaft axis \(Z_{2}\);
* The elastic twisting angle in bending \(\Phi_{2}(x,t)\) concerning the midshaft axis \(Y_{2}\). To compute the equations of the FBV movements of the AD elements using [PERSON]'s principle, it was necessary to reduce the mass and geometric moments of inertia of the tulip and bowl to the cartesian system of reference of the midshaft \(X_{2}Y_{2}Z_{2}\) using the variation of the geometric moments of inertia concerning the concurrent axis and parallel axis ([PERSON]'s theorem). The geometric tulip's design (see Figure 3) is composed of the tulip's bell (TB) and tulip ax (TA), while the geometric bowl's design (see Figure 4) is composed of the bowl's bell (BB) and bowl ax (BA).
## 4 LiDAR Photon Imaging
### Geiger-Mode LiDAR  
A single large aperture divergent laser pulse is emitted to the FoV in the GMLiDAR. The reflected signal is received by focal plane array sensors which are named GMAPD. GMAPD has a combination of advantages over other photon-counting technologies. Each cell of the matrix-shaped sensor has a single photon-sensitive receiver to estimate the range of the reflected object points. Photon recording of the cells is maintained until no further echo can be measured from the same laser pulse. If it is carried out, the photon recording is stopped by photodiode triggers, and the range is estimated via the time to the digital converter (TDC). Then, the GMAPD array resets the operation for the photon records of a subsequent laser pulse.  
GMAPD has a high measurement rate (10-200 kHz frame rate). This measuring speed allows mobile mapping with a low-level energy laser beam. Thanks to the high measuring speed, large areas can be measured at a low cost. GMAPD records range data only, without the intensity. The intensity values are calculated by statistical analysis. Possible delays with short time intervals in measurement direction should be taken into account to reduce measurement errors in mobile measurements [33]. The negative effect of GMLiDAR on human health is very low [34; 35].  
GMAPD calculates the distance by counting the received photons and statistical evaluation. A distance can be calculated for a single photon or even for very weak signals. The high sensitivity of pixels to photons will increase the measurement speed while expending low energy. The high measuring speed causes a density in the read-out units and data process. This density is quite high compared to the LMAPD method.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### Research on Sea-Surface Ka-Band Stochastic Multipath Modeling  
[PERSON] et al. presented a channel model using the Ka-band [20]. It also used the stochastic bridge process [45] to design the sea-surface multipath propagation channel. The Ka-band ranges from 26.5-40 GHz. This band features high system capacity, small size terminal antenna, narrow beam, etc. Therefore, it can play a significant role in wireless channel modeling for over-the-sea communication by overcoming the serious interference caused by multipath components. Stochastic bridge channel modeling denotes the emission of electromagnetic waves as rays, and it considers propagation multiple paths as stochastic process samples. The starting and ending points of the stochastic bridge process were the transmitter and receiver. Waves emitted from the transmitter were assumed to be random rays. Apart from these rays, the random rays can reach the receiver through multiple reflections getting spread on the sea surface. The probability density function for the effective random rays at the receiving end was expressed as:  
\[Q(x,y,z)=\frac{27}{4\pi^{2}D_{k}^{3}}\exp\left(-\frac{3r}{D_{k}}\right) \tag{39}\]where \(D_{k}\) is related to the number of reflection and sea conditions and \(r\) is the distance between the transmitter and receiver. Energy attenuation of the horizontally polarized waves is huge when they fall upon the sea surface obliquely. That is why incident signals used in this channel model were vertically polarized waves. Reflection coefficient of vertical polarization was calculated using the following equation:  
\[\Gamma=\frac{\varepsilon_{c}\sin\theta_{i}-\sqrt{\varepsilon_{c}-\cos^{2} \theta_{i}}}{\varepsilon_{c}\sin\theta_{i}+\sqrt{\varepsilon_{c}-\cos^{2} \theta_{i}}} \tag{40}\]  
where \(\theta_{i}\) is the angle of incidence of radio waves and \(\varepsilon_{c}\) is the complex permittivity. Time delay, phase delay, and amplitude gain of each effective path were calculated using the approaches presented in [46; 47; 48], subsequently.  
The authors of [20] presented a channel model that had high data rate and used small terminal antennas. They did not specify, formulate, or assume any kind of propagation loss model. In addition, only the vertical polarization was used for the incident rays.
## Appendix A Converter Power Losses Analysis  
In this appendix, a brief analysis of the power losses in the proposed converter is presented [31].  
**Main switches' power losses (\(\mathbf{P_{MS}}\))**: they include conduction losses and switching losses. Thus, power losses are defined by (A1) or (A2), where \(d\) is the duty cycle, \(t_{r}\) is rising time, and \(t_{f}\) is the falling time of the switch.  
\[P_{MS}=3\bigg{(}\int_{0}^{dT_{s}}v_{DS}(t)\cdot i_{D}(t)dt+\int_{0}^{t_{r}}v_{ DS}(t)\cdot i_{D}(t)dt+\int_{0}^{t_{f}}v_{DS}(t)\cdot i_{D}(t)dt\bigg{)}\] (A1)  
\[P_{MS}=3\left(\underbrace{\int_{0}^{dT_{s}}i_{D}(t)\cdot R_{ds_{un}}dt}_{Conduction \text{ losses}}+\underbrace{\int_{0}^{t_{r}}i_{D}(t)\cdot R_{ds_{un}}dt+\int_{0}^{t _{f}}i_{D}(t)\cdot R_{ds_{un}}(t)dt}_{Switching\text{ losses}}\right)\] (A2)  
**Snubber-switches losses (\(\mathbf{P_{SS}}\))**: the power loss in the snubber switches is defined by:  
\[P_{SS}=3\left(\underbrace{\int_{0}^{dT_{s}}v_{DS}(t)\cdot i_{D}(t)dt}_{Conduction \text{ losses}}+\underbrace{\int_{0}^{t_{r}}v_{DS}(t)\cdot i_{D}(t)dt+\int_{0}^{t _{f}}v_{DS}(t)\cdot i_{D}(t)dt}_{Switching\text{ losses}}\right)\] (A3)  
**Cables' losses (\(P_{\mathbf{C}u}\))**: the power loss in the cables of the converter is defined by (A4), where \(i_{Cms}\) is the RMS current, and \(R_{cu}\) is the cables' resistance (skin effect include).  
\[P_{Cu}=i_{Cms}^{2}(t)\cdot R_{cu}\] (A4)  
**Capacitors' losses (\(P_{Cs}\))**: the power losses in capacitors are defined by the current waveform for each capacitor of the converter. In the converter, all capacitors are in polypropylene for low series-equivalent resistance. Thus, the capacitor losses are defined by (A5), where \(i_{Cms}\) is the RMS capacitor's current, and \(R_{SE_{Cu}}\) is the series-equivalent resistance. The same equation is valid to calculate the losses in every converter capacitor considering the corresponding current and equivalent resistance.  
\[P_{Cs}=3\left(i_{Cms}^{2}(t)\cdot R_{SE_{Cu}}+i_{Cnsplus}^{2}(t)\cdot R_{SE_{ Cns}}\right)+i_{Cnsplus}^{2}(t)\cdot R_{SE_{Co}}\] (A5)  
**Flyback transformer's losses (\(\mathbf{P_{FT}}\))**: the power losses in the flyback transformer are defined by (A6), where \(i_{prin_{rms}}\) and \(i_{sec_{rms}}\) are the primary and secondary winding RMS current, respectively, \(R_{eq_{prin}}\) and \(R_{eq_{sec}}\) are the equivalent-resistor of the primary and secondary winding, respectively.  
\[P_{FT}=3\left(i_{prin_{rms}}^{2}(t)\cdot R_{eq_{prin}}\ \right)+i_{sec_{rms}}^{2} (t)\cdot R_{eq_{sec}}\] (A6)  
**Output diode's losses (\(\mathbf{P_{D_{6}}}\))**: the power losses in the output diode are defined for the conduction and non-conduction states. In Equation (A7), \(v_{d}(t)\) and \(i_{d}(t)\) are the diode's voltage and current, respectively.  
\[P_{D_{o}}=3\left(\int_{0}^{dT_{s}}v_{d}(t)\cdot i_{d}(t)dt+\int_{0}^{t_{f}}v_{ d}(t)\cdot i_{d}(t)dt\right)\] (A7)  
Using the simulator, the power losses for each element were obtained, considering the converter operating at the rated power, and the results are shown in Table A1:
## 2 Materials and Methods
### AuNPs Synthesis Using a Sonochemical Approach  
Sonochemical synthesis involves the use of sound or sonic energy to excite particles, thereby initiating Brownian motion. The sonochemical technique can be used to efficiently disperse nanoparticles in fluids; therefore, it is regularly used in nanotechnology to facilitate homogeneous dispersion of NPs suspended in liquids. In addition, sonic energy can transform the chemical mechanism that controls the synthesis of the particles. The probe sonicator model (Vibra-Cell Ultrasonic Solid Horn) used for synthesis has a tip size, frequency and ultrasound output power of half-inch, 20 kHz and 17.9 W\(\cdot\)cm\({}^{2}\), respectively. Precursor materials used to synthesize citrate-coated AuNPs include HAuCl\({}_{4}\), sodium citrate and distilled water. In order to prepare the sample, the first step involved the dissolution of HAuCl\({}_{4}\) in distilled water with a concentration of 0.03 M. Subsequently, the solution of HAuCl\({}_{4}\) (2 mL) was added dropwise to 20 mL of aqueous 0.03 M sodium citrate solution. During the mixing phase, a high-density ultrasonic probe (20 kHz) with an ultrasonic horn was inserted into the solution for 5 min at a sounding power of 17.9 W\(\cdot\)cm\({}^{2}\). Ultrasound power has been determined using the underlying equation (Equation (1)) [45].  
\[Power\left(W\right)=Energy\left(\text{J}\right)/Time\left(\text{s}\right) \tag{1}\]  
where energy (J) and time (s) denote energy consumed (J) and duration of reaction, respectively.  
Finally, in order to eliminate impurities and non-reactive particles, the AuNPs were centrifuged at 9000 rpm for 10 min and washed thoroughly twice using distilled water. Subsequently, the obtained particles were stored in two ways: (1) re-dispersed in distilled water for size, optical properties, stability and hydrodynamic size measurements; (2) some dispersion was dried for 24 h at 90 \({}^{\circ}\)C to obtain the powder for XRD, cytotoxicity and CT imaging measurements.
## 3 Results and Discussion
[39] associated different AE signal groups to the crack behaviour based on a spectral analysis of the AE data. The authors discovered that the AE events generated in the top 75-85% of the load range were statistically different from those below 60% and were related to the fatigue crack growth. In the present study, due to the large amount of noise generated by the full-scale experiment, only data collected above 3820 kN are shown to offer good correlations. A closer look at the frequency content of the signals detected during the experiment, presented in Figure 9, shows a continuous shift of the average frequency towards higher values throughout the monitoring period. Figure 8: Peak amplitude (dB) vs. load (kN). Details of the minimum–maximum loading, and the 3820–3873 kN range is highlighted. The shift in the AE signal frequency content may be indicative of a change in the damage mode. For example, in reinforcing steel bars used in reinforced concrete, a decrease of the average AE frequency is attributed to the onset of phase three of the corrosion loss phenomenological model [40]. Additionally, tensile cracking is associated with a higher AE signal frequency range when compared to shear cracking in the same class of materials [41]. In composites, the AE signal frequency can be correlated to the fracture mechanism [42]. Finally, the observation of the cumulative energy during the last 31 days of the experiment did not show any particular change except during the final cycles (yellow circle in Figure 10). The fast rise indicates a rapid release of energy corresponding to rapid crack propagation. No statistical change was noted in the slope of the cumulative energy after the crack was initially detected. A quick increase in the cumulative energy precedes the exposure of the 120 mm crack. The displacement measured by the gauge did not exceed the pre-set limit until 120 mm was reached. This indicates a sharp crack growth, as seen in Figure 10.  
Figure 9: Hits vs. average frequency (kHz). Shift of the average frequency.