esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Gear ACTRAN Noise Prediction
### Construction and Analysis of Acoustic Vibration Coupling Models  
The vibration response of the gear train obtained via the modal superposition method was imported into the acoustic module of ACTRAN, the acoustic boundary element mesh and parameter settings were created, and finally, the outer envelope was constructed to complete the acoustic-vibrational coupling model. To simulate the position of the passenger in relation to where the gears are located under the compartment and to analyze the magnitude of the noise heard by the passenger, five field points approximately \(1\) m from the center of the gear subcenter were selected on the created free sound field.  
Figure 9: Distribution locations of the five field points in the free sound field.  
The sound pressure profiles of the radiated noise from the gearing at different field points were obtained by solving Equation (5), as shown in Figure 10. The dB(A) scale was used to more accurately reflect the response of the human ear to frequency. The RMS value of the sound pressure level was selected to derive the dB(A) values for five field points around the gear pair.  
The five RMS values in Table 6 represent the root mean square (RMS) of the sound pressure level at the corresponding position scanning frequency, that is, the amount of noise perceived by the human ear at the five points. It can be seen that the noise values at all five points are greater than 80 dB(A). Studies have shown that long-term exposure to noise above 80 dB(A) may damage human hearing [4], and radiated noise can be further reduced via subsequent gear trimming.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 5 Factors Affecting Acoustic Methods in Pipeline Localization
### Soil Property  
It is well known that electromagnetic propagation suffers from heavy attenuation in the presence of conductive medium, like water. Therefore, detection techniques that rely on the induction or reflection of electromagnetic radiation, such as metal detectors and ground penetrating radar, do not work well for locating objects buried in wet soil. Generally, acoustic detection methods do not experience this limitation [100]. However, the saturation of the soil together with porosity and degree of compaction, has strong connection with the speed of soil, which can impact on acoustic wave radiation or interpretation of results.  
[PERSON] et al. have investigated the effect of soil property on wave dispersion behaviours and attenuation characteristics in buried iron water pipes [77; 78], concluding that whether a mode creates a leaky bulk wave into surrounding soil depends on the speed relationship between mode and bulk wave of soil [76]. Similar findings have been presented in [70; 142], where fluid-borne wave in buried plastic water pipe will not leak compressional waves when the speed of compressional wave in surrounding  
Figure 20: Axisymmetric pipe discontinuities: (**a**) wall thickness change; (**b**) wall elasticity change; (**c**) internal radius change [131], Copyright Elsevier, 2005.  
soil is greater. These analyses can shed some light on practical problems that occur when applying pipe excitation methods in locating buried pipe.  
Furthermore, accurate evaluation in-situ acoustic properties of soils, especially wave speed, plays a significant role in the seismic wave method [101; 102] and pipe leakage detection [143], as wave speed is directly related to the interpretation of measurement results. Therefore, many studies have been conducted on measurement of soil properties, which can be found in [144; 145; 146; 147; 148].
## 5 Case Studies
### Correctness Analysis
Specifically, Model I and Model II/III have the same load energy demand, and their GCVs across the DGN are also similar, resulting in comparable gas flow between Model I and Model II/III. The calculated true \(\mathbf{Z}\) values in Model II/III are generally greater than the given value of 0.8 in Model I. According to the pipeline function Equation (18), for a constant flow rate, a larger \(\mathbf{Z}\) value leads to a greater pressure drop in the pipeline, which explains why the gas pressure in Model II/III is lower in Figure 9a Table 5 further illustrates the output of the coupling devices in Models I-III. Referring to Equation (20), it can be observed that when the compressor flow is constant, the larger the \(\mathbf{Z}\), the greater the compressor energy consumption. Since the compressibility factors of compressor flow in Model II/III are higher than the given value of 0.8 in Model I, the compressor energy consumption in Model II/III, as shown in Table 5, is greater. This energy consumption deviation is transmitted to the DEN through MC, causing a change in CHP output, which in turn affects the output of GB. Therefore, due to the interdependencies within the MES, the energy flow results of DEN and DHN differ between Model I and Model II/III. In summary, the proposed model can avoid the EFC deviation in the DGN caused by differences in the gas compressibility factor, thereby yielding more accurate MES simulation results. Figure 8: Iteration process of Models I–III on the test system. (**a**) With constant gas compressibility factor; (**b**) with variable gas compressibility factor. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CHP**} & \multicolumn{2}{c}{**GB**} & \multicolumn{2}{c}{**Compressor 1**} & \multicolumn{2}{c}{**Compressor 2**} & \multicolumn{2}{c}{**Compressor 3**} \\ \cline{2-9}  & \(\mathbf{\Phi_{\text{CHP (MW)}}}\) & \(\mathbf{P_{\text{CHP (MW)}}}\) & \(\mathbf{f_{\text{CHP (MSCMD)}}}\) & \(\mathbf{\Phi_{\text{CS (MW)}}}\) & \(\mathbf{f_{\text{C}}}\) & \(\mathbf{f_{\text{C}}}\) & \(\mathbf{f_{\text{C}}}\) \\  & & & & **(MSCMD)** & & **(MSCMD)** & \\ \hline Model I & 76.9911 & 61.5929 & 0.2714 & 58.8280 & 0.1465 & 0.2318 & 0.7029 & 0.1128 & 0.0145 \\ Model II & 77.0825 & 61.6660 & 0.2717 & 58.7367 & 0.1462 & 0.2587 & 0.7464 & 0.1162 & 0.0182 \\ Model III & 77.0825 & 61.6660 & 0.2717 & 58.7367 & 0.1462 & 0.2587 & 0.7464 & 0.1162 & 0.0182 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The interdependencies within Models I–III.
## 4 Discussion  
### Optimisation of Mix Proportions  
Based on the results of the above orthogonal test analysis, aiming at maximizing the pendulum value, minimizing the noise value, maximizing the 28-day compressive strength, and maximizing the 28-day flexural strength, the optimal level combinations of the four mix design factors (water-binder ratio, sand ratio, coarse aggregate volume ratio, and proportion of aggregates > 9.5 mm) for the SRNR performance and mechanical properties of EAC can be obtained. The results are shown in Table 5.  
The optimal mix proportions of EAC should be based on meeting the basic requirements of concrete strength and surface texture characteristics, while pursuing higher SRNR performance. When the water-binder ratio is 0.46, the EAC tends to exhibit reduced exposed aggregate depth and area ratio due to excessive mortar layer thickness; therefore, the water-binder ratio is chosen as 0.43. When the sand ratio is 28%, the fine aggregates are insufficient to fill the voids between the coarse aggregates, resulting in interlocking between the coarse aggregates, which is not easily dispersed. The coarse aggregates are easily unevenly distributed in the exposed height on the surface of EAC, and the exposed aggregate area ratio is reduced. Therefore, the sand ratio is selected as the optimal level of 31% for the pendulum value and 28-day flexural strength. The coarse aggregate volume ratio is selected as the optimal level of 42% for the pendulum value and noise value. The proportion of aggregates > 9.5 mm has the greatest impact on the surface texture characteristics of EAC. When the proportion of aggregates > 9.5 mm is 70%, both the exposed aggregate depth and the exposed aggregate area ratio will be significantly reduced to low levels. Although the skid resistance performance may improve at this stage due to the increased mortar area ratio, the reduction in coarse aggregates on the surface simultaneously compromises its abrasion resistance. Therefore, in order to ensure that the EAC surface has sufficient exposed aggregate depth and exposed aggregate area ratio, the proportion of aggregates > 9.5 mm is selected as 50%.  
In summary, the optimal mix proportions for EAC with high SRNR performance are determined as follows: water-binder ratio of 0.43, sand ratio of 31%, coarse aggregate volume ratio of 42%, and proportion of aggregates > 9.5 mm of 50%.
## 5 Summary and Discussion
### Comparison of Traditional and IoT-Based Non-Destructive Testing  
Traditional NDT methods such as VI, UT, MT, and RT have long served as the backbone of port crane maintenance. These techniques, however, are inherently constrained by their periodic nature, dependency on manual labor, and inability to offer real-time diagnostic insights. In contrast, IoT-enabled RNDT systems leverage continuous sensor-based monitoring, edge/cloud data processing, and AI-driven analytics to overcome many of these limitations.  
Table 5 presents a structured comparison between traditional and IoT-based NDT approaches across key operational and technical parameters. The comparison is based on recent literature and practical implementation trends in the maritime, energy, and manufacturing sectors. Each parameter has been selected based on its relevance to port crane applications and its impact on inspection reliability, safety, and cost-effectiveness.  
Compared to conventional practices, IoT-integrated systems offer broader inspection coverage, reduced dependence on manual inspection teams, and improved predictive maintenance capabilities. Moreover, the ability to collect and analyze data in real-time enhances safety by minimizing the need for personnel to physically access hazardous areas, such as high crane structures or confined mechanical spaces.
## 6 Implementation Experiments
### Demonstration Experiment of Accumulated Error Correction  
To indicate the necessity of position calibration, we add a gauge bar in the main UI of the prototype that decreases according to the increase in the user's moving distance, as shown in Figure 15a. When the walking distance goes over 30 m, and the gauge becomes empty, an alert, as shown in Figure 15b, will be popped to remind the user to find a marker to calibrate the position. At the same time, a photo of the nearest reference marker is displayed. As shown in Figure 15c, the gauge bar becomes full again after calibration.  
In a preliminary experiment, the user successfully navigated to an exhibit within a walking distance of 20 m without positioning error correction. However, when navigated to an exhibit with a 40 m walking distance, as shown in Figure 16a, the user could not find the way to the target because the accumulated positioning error became unignorable that the waypoint geofence could not be correctly triggered, as shown in the orange circle in Figure 16b. If the user calibrated the position in the middle of the route with the marker  
Figure 14: Examples of indoor AR navigation services for the Mineral Industry Museum (IF), Akita University: (**a**) transition from a corridor to an exhibition space; (**b**) arriving at the destination exhibition object.  
indicated as a red dot in Figure 16, they could successfully find the exhibit. The red circle in Figure 16 shows that the trajectory jumped because of calibration.  
We suggest that utilizing exhibits as calibration makers instead of additional images or signboards can be a low-cost method as the calibration can be conducted naturally and  
Figure 16: The user’s moving trajectories when navigated with/without accumulated positioning error correction: (**a**) The route of navigation and the position of the marker for calibration (red dot); (**b**) The user lost direction (at the place in the orange circle) without positioning calibration; (**c**) The user reached the target with positioning calibration (conducted at the red circle).  
Figure 15: Examples of indoor AR navigation services for the Mineral Industry Museum, Akita University (ZF), with positioning error correction implemented: (**a**) Calibration gauge decreases as walking distance increases; (**b**) Reminder of calibration, which occurs with the nearest marker displayed when the moving distance is over 30 m; (**c**) Calibration gauge recovers after calibration.  
unconsciously while the user is viewing the exhibits. Figure 17 demonstrates an example of this proposed method, in which the user's current position is calibrated three times along the navigation route when the smartphone's camera is pointed toward the exhibits. The corrected positions are visualized as the red dots on the map.
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations
#### 4.3.4 Predictive Capabilities  
One of the most significant benefits of data analysis in maritime IoT is predictive capabilities. Through the analysis of real-time and historical data from IoT devices, potential issues such as machinery failures, route obstructions, or dangerous weather conditions can be predicted before they occur. This foresight allows for preemptive actions, which can prevent costly downtime and improve the reliability of maritime operations [88].
# Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
## 2 Method
### Deep Network Model for Interpolation  
We utilized the U-net as the foundational network architecture to achieve effective interpolation of the seismic data, as illustrated in Figure 2. It mainly consisted of two parts: an encoder and a decoder. The encoder was constructed with four down-sampling blocks, while the decoder was built using three up-sampling blocks, followed by a transposed convolution layer to produce an output of the same size as the input. The detailed construction is explained as follows:
## 4 UAV Navigation and Control  
The channel model and communication performance indices of the UAV-aided communication depend on the UAV position in the 3D coordinates. Also, when it comes to multiuser scenarios or remote areas, the UAV path impacts communication performance. However, the onboard battery capacity is limited, and energy consumption for hovering and propulsion must be considered to obtain a minimum path with minimum energy consumption for the endurance of the navigation. Nevertheless, in the context of UAV-enabled communication support for mobile nodes, such as IoVs, the UAV speed should be taken into account in the path-planning technique. Moreover, in dense urban areas with buildings and skyscrapers appearing as obstacles, obstacle-free trajectory optimization for crash-avoidance navigation is necessary where motion constraints such as speed, acceleration, and nonholonomic must be considered [20; 115]. The nonholonomic constraint, which is explained as the heading change limit of fixed-wing UAVs, has been considered in a few works in the wireless communication community, e.g., as curvature-constrained  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Refs.**} & \multirow{2}{*}{**Navigation and Control**} & \multicolumn{2}{c}{**Application**} & \multicolumn{2}{c}{**Communication**} & \multicolumn{2}{c}{**Optimization Problem**} & \multicolumn{1}{c}{} & & & \\ \cline{3-10}  & & & **Service** & ** UAV Type** & **Classical Model** & \multicolumn{1}{c}{**MA**  
trajectory [116]. Nevertheless, quadrotor drones possess rotational limitations that can be modeled as nonholonomic constraints. In this light, the UAV dynamic-aware kinematic (kinodynamic) motion model should be used [117], which is the main shortcoming in the wireless communication community.  
Although these motion constraints with kinodynamic models have been extensively used in the robotic and control communities [118; 119], UAV navigation in the context of wireless communication brings other objectives and constraints to the optimization problem which makes it computationally more complicated. Notably, for autonomous navigation, the trajectory optimization, including both channel performance and navigation indices, must be solved in real-time and without relying on external resources. We review relevant works and highlight the shortcomings from this critical perspective for dense urban areas.
# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
## 3 Co-Simulation Based E2E Evaluation Framework
### Co-Simulation Framework
The above co-simulation framework is realized by minimally modifying Simu5G and CoFluent, coding some software tools to interface the two. In the UL direction the following elements have been implemented:  
Figure 5: Implementation of the co-simulation framework. 1. An application that issues service requests periodically at a UE, written in C++. 2. A C++ collector application that collects all the UE messages (after they have got to the respective BSs) and prints Logfile#1. 3. A program written in python, simu2 mec.py, that converts Logfile#1 to a structured JSON file, to be used as an input to CoFluent. Dually, some more apps have been coded to cope with the DL return trip:  
1. A program written in python, mec2 simu.py, that parses the MEC packet trace produced by CoFluent, and generates a structured JSON file, which specifies for each packet, its ID, the ID of the UE that generated it, and the timestamp. 2. A C++ application that takes the above JSON file as an input and generates a trace of DL messages accordingly. These messages are those that will be injected at step 3, to reach the interested UEs via their serving BSs. 3. A C++ application, running on UEs, that collects responses and generates reports (e.g., by computing the RTT of each request and assembling it with some aggregator, say the mean value).
## 3 CNN and Bi-LSTM Model with MODWT and ANFN Layers
### MODWT (Maximum-Overlap Discrete Wavelet Transform) Layer  
The MODWT is an extended transformation of the discrete wavelet transform (DWT) that can represent data in the time domain as multi-resolution-like spectrograms, scalograms, etc. The MODWT can also be applied without limitation to the signal length, unlike DWT. By overlapping signal sampling, a high-resolution time-frequency representation can be obtained. In addition, it has a shift variance for time movement; thus, it can be used for noise removal or voice feature extraction. The MODWT can be converted through the scaling filter and the wavelet filter, as shown in Equations (1)-(4) below, depending on each scale level and the index of the filter coefficient.  
\[\mathrm{H_{j,k}}=H_{2^{j-1}kmod}\;\mathrm{\textit{N}}\prod_{m=0}^{j-2}G_{2^{m} kmod}\;\mathrm{\textit{N}} \tag{1}\]  
\[\mathrm{W_{j,t}}=\frac{1}{N}\mathrm{\textit{N}}\mathrm{\textit{k}}^{N-1}\;H_{ j,k}e^{\frac{i2\pi nk}{N}} \tag{2}\]  
\[\mathrm{G_{j,k}}=\prod_{m=0}^{j-1}G_{2^{m}kmod}\;\mathrm{\textit{N}} \tag{3}\]  
\[\mathrm{V_{j,t}}=\frac{1}{N}\mathrm{\textit{N}}\mathrm{\textit{k}}^{N-1}\; \mathrm{G_{j,k}}e^{i2\pi nk/N} \tag{4}\]  
The signal converted by the above method can be returned to the original signal through inverse transformation. The standard algorithm for MODWT implements direct cyclic synthesis in the time domain. The MODWT implementation in our experiment performed a circular convolution in the Fourier domain. The wavelet and scaling filter coefficients at level j were calculated by taking the inverse discrete Fourier transform (DFT) of the product of the DFTs. The DFT of the product was the DFT of the signal and the DFT of the j level wavelet or scaling filter. The MODWT has a fixed-frequency band, so it may not be sensitive to nonlinear changes in the signal. However, for ECG reconstruction, in this study, the purpose was to extract heart information in the low-frequency band. For this reason, we used the MODWT, which enabled multiple-resolution analysis and allowed the easy separation of the desired frequency band.  
Figure 2: Overview of the ECG signal’s reconstruction process using MODWT, deep learning, and ANFN.
## 4 Implementation and Results
### System Configuration of Prototype Version 2  
In order to solve the problems of prototype version 1, prototype version 2 was built to incorporate a server with low power embedded board modules, the CCTV camera was replaced to reduce power consumption, while the soil sensors, environmental sensors and GPS modules used were the same as in prototype version 1. Figure 12 shows the finished prototype version 2. Figure 13 shows the low power embedded board module used in prototype version 2, in which a database and a Web server are built to process and store the collected information or to provide it to users from the embedded board. The low power embedded board module used in prototype version 2 has the hardware specifications listed in Table 4.  
Figure 12: Prototype Version 2 of Agricultural Environment Monitoring Server System.  
The problems of prototype version 1 could be satisfactorily solved through these changes, and the agricultural environment monitoring server system could collect information without malfunctioning even in a real outdoors environment and provide real-time monitoring and various application services based on the information. Table 5 shows the power consumption of each module and the power supply of the solar cells installed on prototype version 2.  
\begin{table}
\begin{tabular}{l|l} \hline
**Type** & **Specifications** \\ \hline System & CPU: 32 Bit RISC ARM1179 JZF–667 MHz \\  & RAM: 128 MB(mDDR), OS: 48 MB/APP: 80 MB \\  & Flash: NAND Flash: 128 MB (OS: 50 MB, Storage: 78 MB) \\  & SD Memory: SD Support 16 GB \\  & Audio: Stereo Sound \\  & Power: DC 9 \(\sim\) 24 V (9 V/500 mA) \\  & Operating Temperature: \(-\)10 \(\sim\) 60 °C \\ \hline Communication & RS232: 2 ch \\  & RS485: 1 ch (Auto) \\  & TTL: 1 ch \\  & USB Host: 2 ch–USB 1.1 \\  & USB Device: 1 ch–USB 2.0 \\  & Ethernet: 10 Mbps \\  & WLAN: USB Type Wireless LAN \\  & I2C: 1 ch \\  & SPI: 1 ch \\ \hline Extension Port & GPIO: 16 EA(3.3 V) \\  & PWM: 2 ch \\  & AD Converter: 12 Bit/4 ch \\ \hline \end{tabular}
\end{table}
Table 4: Hardware Specifications of the Embedded Board Module.  
Figure 13: Embedded Board Module (Gateway, Database, and Web Server).
## 3 Results
### Spatial and Temporal Differences of Global GPP Estimates
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Percentage (\%) & **CASA** & **CFix** & **CFlux** & **EC-LUE** & **MODIS** & **VPM** & **VPRM** \\ \hline ENF & 4.87 & 3.18 & 3.98 & 3.50 & 3.82 & 2.05 & 2.06 \\ EBF & 22.26 & 26.42 & 30.86 & 33.97 & 33.64 & 27.85 & 28.85 \\ DNF & 1.96 & 1.52 & 1.69 & 1.65 & 1.67 & 1.20 & 1.22 \\ DBF & 7.00 & 3.44 & 3.61 & 3.67 & 3.32 & 4.00 & 3.15 \\ MIF & 7.17 & 5.77 & 7.39 & 6.72 & 6.32 & 5.17 & 5.27 \\ SHR & 10.25 & 8.38 & 6.96 & 6.00 & 6.46 & 8.40 & 8.31 \\ SAV & 27.00 & 27.61 & 25.15 & 25.90 & 24.98 & 27.04 & 26.54 \\ GRA & 8.56 & 8.71 & 6.35 & 5.70 & 6.21 & 9.05 & 8.56 \\ CRO & 14.67 & 14.97 & 14.02 & 12.88 & 13.59 & 15.99 & 16.03 \\ \hline \end{tabular}
\begin{tabular}{c} ENF: evergreen needleleaf forest, EBF: evergreen broadleaf forest, DNF: deciduous needleleaf forest, DBF: deciduous broadleaf forest, MIF: mixed forest, SHR: shrubland, SAV: savanna, GRA: grassland, CRO: cropland. The unit of GPP is Pg\(\cdot\)C\(\cdot\)yr\({}^{-1}\).  
\end{table} 
Table 2: Percentage of the GPP estimates of various vegetation types to the global GPP. Figure 2: Spatial patterns of global GPP trend from 2000 to 2010 estimated by **(a)** CASA; **(b)** CFix; **(c)** CFlux; **(d)** EC-LUE; **(e)** MODIS; **(f)** VPM; and **(g)** VPRM models; **(h)** the number of models with increased GPP. DES is no vegetation area.