esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper has proposed the adaptive EWT technique combined with the spectral subtraction method for fault diagnosis of a gearbox system. Firstly, the side-band frequency features are extracted to verify the effectiveness of the algorithms with the experimental data when the gear breaks down. The results indicate that the performance of the proposed method is better than other methods. Then the multi-body dynamics model of the gearbox system is established after gear flexibility treatment. The different tooth breakages are designed by the dynamical model. The experimental results indicate that the dynamical model can satisfy the gear fault characteristics, and it can be used to validate the algorithm performance. For smaller tooth breakage, the proposed method can more clearly detect the fault features than EMD, LMD, and DWT techniques. When the fault features are not extracted by EMD, LMD, and DWT techniques at a noise above \(-\)5 dB, the proposed method can still discern the side-band frequency features.  
The spectral subtraction technique can eliminate the strong noise to enhance the faulty component. Then, the EWT method can effectively extract the transient impulse component, and the side-band frequency features are significantly obvious. The multi-body dynamics model of the gearbox system after gear flexibility treatment is helpful for establishing the gearbox fault. Furthermore, it can be used to design multiple faults of the gearbox and provide much data for predicting gearbox faults.  
[PERSON] gave academic guidance to this research work and revised the manuscript. [PERSON] designed the core methodology of this study, programmed the algorithms and carried out the experiments, and drafted the manuscript. Both authors read and approved the final manuscript.  
This work was supported by a 2017 grant from the Russian Science Foundation (Project No. 17-19-01389).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR** & **EMD** & **LMD** & **DWT** & **Proposed Algorithm** & **Percentage** \\ \hline
10 dB & 0.0965 & 0.0518 & 0.0675 & 0.3247 & 236\%, 527\%, 381\% \\
5 dB & 0.0510 & 0.0401 & 0.0351 & 0.3251 & 537\%, 711\%, 826\% \\
0 dB & 0.0266 & 0.0343 & 0.0156 & 0.3225 & 1112\%, 840\%, 1967\% \\ \(-\)5 dB & 0.0109 & 0.0138 & 0.0067 & 0.3136 & 2777\%, 2172\%, 4581\% \\ \(-\)10 dB & 0.0036 & 0.0016 & 0.0063 & 0.2716 & 7444\%, \%, 4211\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The amplitude value of first-order frequency for different analysis methods and SNR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analysis Type** & **Amplitude of fr** & **Amplitude of 2 fr** & **Amplitude of 3 fr** & **Amplitude of 4 fr** & **Amplitude of 5 fr** \\ \hline EMD algorithm & 0.051 & 0.0595 & 0.0619 & 0.0392 & 0.0393 \\ LMD algorithm & 0.0402 & 0.0533 & 0.0465 & 0.0425 & 0.0279 \\ DWT algorithm & 0.0546 & 0.0639 & 0.0651 & 0.0288 & 0.0365 \\ Proposed algorithm & 0.3251 & 0.2965 & 0.2548 & 0.2078 & 0.1642 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The side-band amplitudes for different analysis method.
## 4 Command and Control
### Data Fusion Subsystem
Figure 6: C&C methods for 2D Video sensor data fusion: (**A**) time-based: Sensor 1 (S1) and 2D Video observe objects and persons situated at a pre-defined location; (**B**) Identity-based: 2D Video-C&C and 3D Video tracking handover allow one to locate (re) identified persons for Sensor 2 (S2) detection. The first type of data fusion is challenging for the C&C in case of different commuters crossing the area with a short time distance among each other. The 2D Video and MIC data fusion has been based on the time of the two detections generated by the presence of a person in the common observation area, identified by a shared coordinate system. This time corresponds to the time when the 2D Video detects the person crossing the Init line, and the estimated time when the object identified by MIC before the line would be situated at the line. The last estimation was obtained based on the speed of the object/person while crossing the observed area. As MIC could send more than one message for the same object, to avoid false positives due to close commuters, time delays of about 2 (and 3) seconds between subsequent commuters have been experimented/established at the beginning of the trials. Essentially, the data fusion method creates a queue of pairs (M, V), where M is a MIC message, and V is a 2D Video message, such that the distance of detection time contained in the M and V messages is less than 2 (or 3) s, and M may be received before or after V. Then, the pair with minimum time distance is chosen. Furthermore, to accommodate eventual non-negligible latency increases for receiving the messages from the external components (including the broker or delays due to the network), a time window of 2 min is applied for the observation of the incoming messages. Critical scenarios for this type of data fusion are described in Section 5.2.
# Motion Sickness in Mixed-Reality Situational Awareness System
## 5 Results
### Vignette  
For the vignette test, 16 tests were conducted. All of these tests lasted 15 min.  
The SSQ scores in Figure 15 indicate that the 54 deg black vignette appeared to perform the best, with the other black vignette and 26 deg blurred periphery following close behind.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Type** & **Subtype** & **RAW Mean** & **RAW Std** & **MSQ Mean** & **MSQ Std** \\ \hline FoV test & 26 deg & 28.98 & 37.09 & **15.15** & 12.79 \\ FoV test & 30 deg & **20.69** & 17.96 & 19.8 & 27.74 \\ FoV test & 34 deg & 26.92 & 32.55 & 38.59 & 67.62 \\ FoV test & 44 deg & 22.17 & 24.63 & 18.58 & 19.78 \\ FoV test & 49 deg & **19.87** & 24.48 & 18.81 & 21.94 \\ FoV test & 54 deg & 22.6 & 26.35 & **15.17** & 17.98 \\ FoV test & 60 deg & 32.49 & 36.74 & 43.25 & 78.98 \\ FoV test & 63 deg & **20.41** & 24.69 & **14.65** & 17.57 \\ FoV test & 84 deg & **11.53** & 16.65 & **8.98** & 13.02 \\ \hline Blur & control & **20.67** & 26.8 & **17.31** & 22.11 \\ Blur & blur & 20.94 & 24.42 & 23.76 & 42.34 \\ \hline Vignette & black 26 deg & 45.35 & 39.78 & - & - \\ Vignette & black 54 deg & **31.42** & **31.59** & - & - \\ Vignette & blur 26 deg & 46.75 & 50.47 & - & - \\ Vignette & blur 54 deg & 58.09 & 41.6 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation of all recorded SSQ values for HMD-based changes. The FoV tests consisted of 49 samples, blur consisted of 10 + 9 control samples and the vignette test consisted of 16 samples.  
Figure 14: Mean SSQ results for the blur test. Both the new control and general reference line have been included.  
Verbally, all participants who tested both setups also indicated their preference to the black vignette over the blurred version.
## 2 Materials and Methods
### Color Change Assessment  
Color changes on the surface of the parsley samples were measured in the CIE L*a*b trichromatic color model. An NH300 portable spectrophotometer (EnviSense, Lublin, Poland) was used with a horizontal construction and equipped with a silicon photodiode as a detector. The color change was measured after ozonation of the parsley samples in water (ozone concentration 0.5 mg/L and 1.5 mg/L) for 15 min, after rinsing with ozonated water in a closed circuit for 15 min (ozone concentration 1.5 mg/L) and after rinsing in non-ozonated water (control sample). Each variant was repeated 10 times in different areas of the parsley sample. The colorimeter was equipped with an 8-mm measuring aperture. Color coordinates representing lightness (L), red-green axis (a*), and yellow-blue axis (b*) were recorded. An average of 10 color readings were taken for each sample. The differences between treated and untreated samples were expressed as \(\Delta\)L, \(\Delta\)a, and \(\Delta\)b. The total color changes \(\Delta\)E after the ozonation processes were calculated using Formula (2),  
\[\Delta E{=}\surd(\Delta L)^{2}+(\Delta a)^{2}+(\Delta b)^{2}, \tag{2}\]  
where \(\Delta\)E is the value of the color change relative to the reference sample; \(\Delta\)L is the difference in lightness (white-black) between the tested and reference sample; \(\Delta\)a is the difference in green-red color between the tested and reference sample, and \(\Delta\)b is the difference in blue-yellow color between the tested and the reference sample.
## 4 Simulation Results
### Ground Displacement  
Figure 9a shows the vertical displacement distribution of the ground surface along the B-B section (Figure 3) during the foundation pit excavation. Shallow excavation results in maximum vertical displacement at the pit's edge. As excavation continues, the location of maximum vertical displacement gradually shifts outward, forming a settlement trough pattern. This shift occurs primarily due to the deformation mode of the retaining structure influencing surface displacement. Initially, with shallow excavation, the retaining structure operates in a cantilevered mode. As excavation deepens, the structure transitions to a compound mode, causing the outer soil to move inward towards the middle of the retaining structure, leading to ground displacement.  
Figure 9b shows the vertical displacement distribution of the ground surface along the A-A section (Figure 3) during the foundation pit excavation. Notably, both sections B-B and A-A exhibit traditional settlement trough deformation patterns, though there are differences within a certain range between them. This discrepancy is attributed to the interaction between the existing tunnel and the surrounding soil, resulting in the significant vertical settlement of the tunnel. As a result, the maximum settlement value in section A occurs directly above the existing tunnel, highlighting the differential settlement patterns between the B and A sections and underscoring the interaction between the tunnel structure and the soil. Figure 9c depicts the surface displacement cloud after the completion of the excavation, illustrating the significant disturbance to the surrounding strata caused by the excavation.  
Figure 8: Horizontal displacement distribution of tunnel vault.
###### Abstract  
Underwater wireless communications face significant challenges due to high attenuation, turbulence, and water turbidity. Traditional methods like acoustic and radio frequency (RF) communication suffer from low data rates (<100 kbps), high latency (>1 s), and limited transmission distances (<10 km).Visible Light Communication (VLC) emerges as a promising alternative, offering high-speed data transmission (up to 5 Gbps), low latency (<1 ms), and immunity to electromagnetic interference. This paper provides an in-depth review of underwater VLC, covering fundamental principles, environmental factors (scattering, absorption), and dynamic water properties. We analyze modulation techniques, including adaptive and hybrid schemes (QAM-OFDM achieving 4.92 Gbps over 1.5 m), and demonstrate their superiority over conventional methods. Practical applications--underwater exploration, autonomous vehicle control, and environmental monitoring--are discussed alongside security challenges. Key findings highlight UVLC's ability to overcome traditional limitations, with experimental results showing 500 Mbps over 150 m using PAM4 modulation. Future research directions include integrating quantum communication and Reconfigurable Intelligent Surfaces (RISs) to further enhance performance, with simulations projecting 40% improved spectral efficiency in turbulent conditions.  
Visible Light Communication (VLC); underwater communication; modulation techniques; security and privacy
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 6 Conclusions  
The SFSS is an innovative and efficient method of intrusion detection, providing farmers with a dependable and secure solution. This smart monitoring system uses cutting-edge technology and AWS capabilities to allow farmers to protect their crops and animals proactively. The technology guarantees farm security by offering real-time object detection, rapid alerts, and speaker control. The SFSS's adaptability expands its value beyond the agricultural industry. The incorporation of cloud-based object identification opens up a world of possibilities in industries ranging from railway track monitoring to border security, wildlife protection, forest fire detection, disaster response, industrial safety, environmental monitoring, agricultural pest control, and oil and gas pipeline inspection. This adaptive technology revolutionizes surveillance approaches by providing real-time monitoring and threat identification, boosting security across a wide range of industries.  
The SFSS is more than just a solution to today's problems; it is also a foundation for future advancements. As technology advances and new possibilities emerge, the system's ability to solve the security demands of a variety of industries will only improve. The SFSS is a beacon of innovation and efficiency in the surveillance and security industry, constantly refining intrusion detection systems and increasing its range of applications. This smart monitoring system shows the potential of employing technology to preserve and defend assets, whether they are crops on a farm or important infrastructure in other sectors, with its cutting-edge features and versatility.  
Conceptualization, T.S.D. and M.S.; methodology, S.M. and T.S.D.; validation, A.S.M.S.H. and A.K.; formal analysis, A.S.M.S.H. and M.S.; investigation, Y-w.L. and T.S.D.; resources, Y.-w.L. and J.-Y.R.; data curation, S.M. and Y.-w.L.; writing--original draft preparation, A.S.M.S.H. and T.S.D.; writing--review and editing, Y.-w.L. and J.-Y.R.; supervision, M.S., Y.-w.L. and J.-Y.R.; funding acquisition, A.S.M.S.H. and J.-Y.R. All authors have read and agreed to the published version of this manuscript.  
This work was supported by the Woosong University Academic Research Fund 2025, South Korea.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
We are thankful to the National Research Foundation (NRF) Korea for sponsoring this research under Project BK21 FOUR (Smart Robot Convergence and Application Education Research Center, PKNU).  
The authors declare no conflicts of interest.
# Propagation Characteristics of Modulated EHF Signal in the Wake Region of Plasma Sheath
## 2 Characteristics of EHF Wave in Plasma Sheath
### Spatiotemporal Evolution Characteristics of EHF Wave in the Wake Region of Plasma Sheath
According to geometrical optical theories, the refractive indices of plasma sheath for EHF waves are the functions of wave frequencies. In addition, the refractive indices are not close to 1, which is the refractive index of vacuum or neutral atmosphere. On the other hand, the wavelength is proportional to the refractive index. Thus, the EHF wavelength in the plasma sheath is obviously different from that in vacuum or neutral atmosphere. The refractive index depends on the electron density, electron collision frequency, and the wave frequency. In other words, the time-varying inhomogeneous plasma sheaths make the wavelength of propagating EHF signal vary in time during propagation. It should be noticed that the phase shifts are measured at a fix position in the present frame of reference. Hence, the varying wavelength makes the measured phase shifts vary with time. The above results indicate that the total transmission coefficient and phase shift of the EHF communication in the plasma sheath wake region are very dependent on carrier frequency and channel angle \(\alpha\), and that this dependence will affect the BER characteristics of EHF communication in the plasma sheath wake region, which are analyzed in detail in the next section.
## 6 Mathematical Representations
### Proposed System  
Taking into account Section 6.2.1, we now propose a semi-active suspension and a pneumatic actuator that work together with a Skyhook action. As explained in previous  
\begin{table}
\begin{tabular}{c c} \hline \hline \(m_{c}\) = 1794.4 (kg) & \(k_{br}\) = 18,615 (N/m) \\ \(I\) = 3443.05(\(N\)/\(A\)) & \(k_{urf}\) = 101,115 (N/m) \\ \(m_{wf}\) = 87.15 (kg) & \(k_{urf}\) = 101,115 (N/m) \\ \(m_{urf}\) = 140.14 (kg) & \(L_{f}\) = 1.27 (m) \\ \(c_{fb}\) = 1190 (Ns/m) & \(L_{r}\) = 1.72 (m) \\ \(c_{br}\) = 1000 (Ns/m) & \(L\) = 2.99 (m) \\ \(k_{bf}\) = 66,824.4 (N/m) & \(F_{AD}\) = \(F_{AT}\) = 1500 (N) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vehicle parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(m_{c}\) = 1794.4 (kg) & \(k_{br}\) = 18,615 (N/m) \\ \(I\) = 3443.05(\(N\)/\(A\)) & \(k_{urf}\) = 101,115 (N/m) \\ \(m_{wf}\) = 87.15 (kg) & \(k_{urf}\) = 101,115 (N/m) \\ \(m_{urf}\) = 140.14 (kg) & \(L_{f}\) = 1.27 (m) \\ \(c_{fb}\) = 1190 (Ns/m) & \(L_{r}\) = 1.72 (m) \\ \(c_{br}\) = 1000 (Ns/m) & \(L\) = 2.99 (m) \\ \(k_{bf}\) = 66,824.4 (N/m) & \(F_{AD}\) = \(F_{AT}\) = 1500 (N) \\ \hline \hline \end{tabular}
\end{table}
Table 3: MR damper parameters.  
sections, it is not an active suspension, since the actuator is not always in operation, but only when lifting is required due to some speed hump/bump. See Figure 6.  
The equations of motion derived from Figure 6 are the following:  
\[\begin{array}{l}m_{s_{1}}\dot{x}_{1}=-k_{T}(x_{1}-w_{r})-b_{T}(\dot{x}_{1}- \dot{w}_{r})+k_{1}(x_{3}-x_{1})+b_{1}(\dot{x}_{3}-\dot{x}_{1})+F_{D1}-F_{AT}\\ m_{s_{2}}\dot{x}_{2}=-k_{T}(x_{2}-w_{f})-b_{T}(\dot{x}_{2}-\dot{w}_{f})+k_{2}(x _{4}-x_{2})+F_{D2}-F_{AD}\\ m_{c}\dot{x}_{3}=-k_{1}(x_{3}-x_{1})-k_{2}(x_{4}-x_{2})-F_{D1}-F_{D2}+F_{AT}-Bm _{c}\ddot{\theta}\\ m_{c}\dot{x}_{4}=-k_{1}(x_{3}-x_{1})-k_{2}(x_{4}-x_{2})-F_{D1}-F_{D2}+F_{AD}+Am _{c}\ddot{\theta}\\ I\ddot{\theta}=A[k_{1}(x_{3}-x_{1})+F_{D1}-F_{AT}]-B[k_{2}(x_{4}-x_{2})+F_{D2}- F_{AD}]\end{array} \tag{11}\]  
Above, \(F_{D1}\) and \(F_{D2}\) are defined as:  
\[\begin{array}{l}F_{D1}=k_{p}(x_{3}-x_{1})+c_{p}(\dot{x}_{3}-\dot{x}_{1})\\ +f_{f}\tanh[x_{friction}(x_{3}-x_{1})+v_{friction}(\dot{x}_{3}-\dot{x}_{1})]\\ +IC_{MRpostyield}\tanh[x_{preyield}(x_{3}-x_{1})+v_{preyield}(\dot{x}_{3}-\dot{ x}_{1})]\end{array} \tag{12}\]  
\[\begin{array}{l}F_{D2}=k_{p}(x_{4}-x_{2})+c_{p}(\dot{x}_{4}-\dot{x}_{2})\\ +f_{f}\tanh[x_{friction}(x_{4}-x_{2})+v_{friction}(\dot{x}_{4}-\dot{x}_{2})]\\ +IC_{MRpostyield}\tanh[x_{preyield}(x_{4}-x_{2})+v_{preyield}(\dot{x}_{4}-\dot{ x}_{2})]\end{array} \tag{13}\]  
\(F_{AD}=\) front actuator force, \(F_{AT}=\) rear actuator force, and \(x_{c}\), \(\theta\) are defined as in Equations (9) and (10), respectively. Moreover, \(SH_{1}\) represents the Skyhook controller for the rear suspension, whereas \(SH_{2}\) is the Skyhook controller for the front suspension.
# Development of a Pyrometer That Measures the True Temperature Field of the Two-Dimensional Array
## 1 Introduction  
Radiation thermometry [1; 2; 3] frequently uses a multi-wavelength pyrometer (MWP) [4; 5; 6] to overcome the restrictions from the unknown emissivity of traditional one-color pyrometers and the constant emissivity vs. wavelength for two-color ratio pyrometers [7; 8]. A multi-wavelength pyrometer can obtain the true temperature of the target without emissivity information by using the principle of multi-wavelength radiation thermometry [9]. [PERSON] et al. developed a six-wavelength pyrometer with filter arrays for metal melting applications [10]. [PERSON] et al. developed a six-color pyrometer with a fiber-optic bundle [11]. [PERSON] and [PERSON] developed an ultraviolet pyrometer to measure shock temperatures with a semi-transparent and semi-reflective mirror in 1989 [12]. [PERSON] et al. developed a three-color ratio pyrometer that employed one visible and two near-infrared wavelengths to measure the surface temperatures of burning carbonaceous particles [13]. [PERSON] and [PERSON] explored a multi-wavelength pyrometer with a spectrometer to measure the true temperatures of transparent substances and combustion gases [14]. In 2017, [PERSON] et al. developed a long-distance multi-wavelength pyrometer [15], which placed the core of the instrument 100 m from the probe.  
The current multi-wavelength radiation pyrometers [16; 17] are all designed for single point or multi-point targets. This paper develops a multi-wavelength radiation pyrometer that can be used to measure the true temperature field of the two-dimensional array. It consists of an optical part, a circuit part and a software part. In the optical part, the data of the two-dimensional array are achieved by combining the longitudinal scanning by the rotating mirror with the transversemulti-target points' method. In this way, the hardware space resources are saved, and the operation process of data acquisition is simplified at the same time, illustrating that the hardware resources and the measurement speed are both considered. The circuit part includes an I/V conversion circuit, a V/V amplification circuit and a data acquisition card. The I/V conversion circuit uses AD820 to convert the signal into a voltage signal. The V/V amplification circuit uses OP07 to amplify the voltage signal. Finally, the pyrometer uses the USB5630 data acquisition card to obtain the voltage signal, so as to realize the information interaction between hardware circuit and upper computer. The software part is compiled by using Visual Studio as the development environment and using C# as the language, mainly including the functions of pyrometer calibration, data acquisition and data processing. Data processing adopts a secondary measurement method to calculate true temperatures [18] and uses a multi-threaded method to improve the operation efficiency.  
After the development of the pyrometer, this work carries out the uncertainty analysis and the two-dimensional array true temperature measurement experiment, and finally obtains the uncertainty of the two-dimensional array pyrometer.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 4 Discussion
When comparing the two approaches, it was observed that both the mask decoder, which relies solely on the magnitude spectrum and disregards phase information, and the complex decoder, which uses only the complex spectrum, exhibited lower performance than the DeCGAN model across all metrics. This finding suggests that the integration of the mask decoder and complex decoder within the DeCGAN model creates a complementary structure that is essential for significantly enhancing speech quality and intelligibility. Finally, by comparing Case 7 and Case 1, the impact of the metric discriminator on speech enhancement was verified. A decline in all metrics was observed, indicating that incorporating evaluation scores into the model training, as designed with the metric discriminator, significantly improved the reconstruction quality of the speech.