esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion  
### Acquisition of Diesel Engine Noise  
Engine noise is a major cause of discomfort to vehicle occupants and nearby residents. Vehicles requiring high output power generally use diesel engines, which generate louder noise than gasoline engines. Thus, ANC simulations were conducted to compare the ANC performances of the LSTM controller and FxLMS algorithm to actively reduce diesel engine  
Figure 3: Block diagram of deep neural network filters with long short-term memory (LSTM) for the ANC system. Compared with the conventional FxLMS system, the blocks corresponding to the FxLMS algorithm have been replaced with the LSTM controller block.  
sounds. As shown in Figure 4a, the reference noise generated from a 1600-cc single-cylinder diesel engine was recorded by NoiseBook (HDC 45 Noise Gard) when the engine was operated at various RPMs. The microphone for measuring the reference noise was located 2 m back at a height of 1.5 m from the diesel engine, as shown in Figure 4b. The noise was sampled at a frequency of 44,100 Hz. To evaluate the performance according to the adaptive filter algorithm, it was necessary to verify the performance in a wide frequency band and under abrupt modulation noises. In addition, several noisy sounds were measured when the diesel engines were operating at different rotational frequencies and torques to verify the ANC capability of the LSTM controller for broadband noise. The impulsive sound when the diesel engine began operation was also measured. Each recorded sound was used for the ANC simulation.
## 1 Introduction
Previous studies ([PERSON] et al., 2016) have shown that in the presence of absorbing aerosol, such as dust, smoke, or anthropogenic particles, satellite AOT measurements are distorted, leading to the presence of anomalies in SSA single scatter albedo values and incorrect atmospheric correction. To assess the influence of regional features of the Black Sea region on the operation of the NASA Ocean color algorithm [24], cases of dust transport over the Mediterranean Sea were also considered separately.
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 3 System Model
### Received Signal and Channel Model
Therefore, for a multipath time-varying channel (or a doubly dispersive channel), the received signal \(y(t)\) can be given by the following expressions:  
\[y(t)=\int_{\tau}g(\tau,t)s(t-\tau)d\tau \tag{9}\]Figure 3:0  
Figure 3:1  
where \(S(f)\) represents the Fourier transform of the transmitted signal \(s(t)\) in the time domain, \(g(\tau,t)\) represents the time-varying (or delay-time) channel impulse response, \(H(f,t)\) represents the channel in the time-frequency domain as represented in an OFDM system and \(h(\tau,v)\) represents the channel in the delay-Doppler domain as given in an OTFS system. Using a pair of two-dimensional Symplectic Fourier transforms (SFTs) and Inverse Symplectic Fourier Transforms (ISFTs), the channel can be transformed between the TF domain \(H(f,t)\) and the DD domain \(h(\tau,v)\), as shown in the following equations:  
Figure 3:2  
Figure 3:3  
Similarly, the information-carrying symbols can be transformed from the time-frequency domain to the DD domain and vice versa using the SFFT and the ISFFT, respectively.
## 3 Verification of Noise Reduction Method by Combined Use of Improved Wavelet and EMD  
### Test Rig Setup  
The defect electromagnetic test rig used for the steel cord conveyor belt was built in our laboratory, as shown in Figure 6a. The belt length is 11.0 m and width is 0.8 m. The electromagnetic testing device was installed close to the lower surface of the downward belt, as shown in Figure 6b. The sensitivity of the electromagnetic transducer is 0.5 V/Gs. The location detection resolution is 0.1 mm, and the detecting speed is 0 to 8 m/s. The data transmission rate is 10 M/100 Mbps, the working temperature is \(-20\)\({}^{\circ}\)C to \(+55\)\({}^{\circ}\)C, and humidity is 95% RH. The detection distance is 50 mm to 110 mm. The whole electromagnetic testing system is shown in Figure 6c. The magnetic loading module was installed to magnetize the steel wire rope. The operating velocity of belt was set to 0.5 m/s by a variable frequency inverter. The data were collected by the electromagnetic testing device and transmitted to a computer by TCP/IP protocol.  
Figure 5: Noise reduction flow chart of combined method of improved wavelet and EMD.  
Figure 6: Electromagnetic testing rig of steel cord conveyor belt: (**a**) Tested belt conveyor prototype; (**b**) Installed electromagnetic testing device; (**c**) Electromagnetic testing system setup.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 3 Results
### Sentinel-3 Level-2 Product Validation with Buoy Data in the Gulf of Riga  
The buoy placed on the bottom of the Gulf of Riga was collecting \(SSH_{buoy}\) from June 2019 until November 2019. Over this period of time, satellite tracks S3A pass 186 and S3A pass 397 passed the buoy six and five times, respectively. For the validation, the mean \(SSH_{SA}\) for every overpass was calculated using the Level-2 altimetry product within a radius of 5 km from the buoy. It was then compared with the buoy measurements (\(SSH_{buoy}\); Figure 10), representing average of twelve buoy readings taken over a time interval from half an hour before to half an hour after the satellite overpass. According to the results, the agreement between the two datasets was very good (Pearson correlation coefficients \(r\) for S3A pass 186 and S3A pass 397 were 0.88 and 0.95, respectively). The mean residual differences (\(\Delta\)\(SSH_{buoy}\)) were 0.05 \(\pm\) 0.06 m and \(-\)0.03 \(\pm\) 0.10 m for S3A pass 186 and S3A pass 397, respectively.
# Nonlinear Extended-state ARX-Laguerre PI Observer Fault Diagnosis of Bearings
## Nomenclature  
\(V_{stator}\): Stator voltage matrix: \(L_{stator}\): Stator inductance matrix: \(V_{rotor}\): Rotor voltage matrix: \(L_{rotor}\): Rotor inductance matrix: \(Z_{stator}\): Stator impedance matrix: \(M_{stator,rotor}\): Stator and rotor mutual: \(Z_{rotor}\): Rotor impedance matrix: \(\omega_{r}\): Rotor rectangular velocity: \(\varphi_{stator},\dot{\varphi}_{stator}\): stator: \(O(k)\): System's output:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms** & \multicolumn{3}{c}{**Proposed Method**} & \multicolumn{3}{c}{**ALPIO**} \\ \hline
**Motor Speed (RPM)** & 300 & 400 & 450 & 500 & 300 & 400 & 450 & 500 \\
**Normal Stat** & 100\% & 100\% & 100\% & 100\% & 80\% & 86\% & 88\% & 90\% \\
**IR Fault** & 95\% & 95\% & 97\% & 97\% & 67\% & 70\% & 75\% & 83\% \\
**OR Fault** & 95\% & 96\% & 97\% & 98\% & 72\% & 72\% & 76\% & 82\% \\
**Ball Fault** & 96\% & 97\% & 97\% & 97\% & 77\% & 78\% & 82\% & 84\% \\
**IR-Ball Fault** & 93\% & 94\% & 95\% & 97\% & 80\% & 80\% & 83\% & 85\% \\
**OR-Ball Fault** & 97\% & 97\% & 97\% & 98\% & 79\% & 83\% & 84\% & 80\% \\
**IR-OR Fault** & 98\% & 98\% & 98\% & 98\% & 78\% & 82\% & 72\% & 82\% \\
**IR-OR-Ball Fault** & 97\% & 97\% & 97\% & 98\% & 79\% & 76\% & 79\% & 83\% \\
**Average** & 96.4\% & 96.8\% & 97.2\% & 97.9\% & 76.5\% & 78.4\% & 79.9\% & 83.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fault diagnosis results for various motor speeds using the proposed method and ALPIO (crack size = 3 mm).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & \multicolumn{3}{c}{**Proposed Method**} & \multicolumn{3}{c}{**ALPIO**} \\ \hline
**Motor Speed (RPM)** & 300 & 400 & 450 & 500 & 300 & 400 & 450 & 500 \\
**Normal Stat** & 100\% & 100\% & 100\% & 100\% & 80\% & 86\% & 88\% & 90\% \\
**IR Fault** & 96\% & 96\% & 97\% & 97\% & 73\% & 73\% & 78\% & 83\% \\
**OR Fault** & 97\% & 97\% & 98\% & 98\% & 80\% & 76\% & 82\% & 82\% \\
**Ball Fault** & 100\% & 99\% & 99\% & 99\% & 74\% & 78\% & 82\% & 84\% \\
**IR-Ball Fault** & 98\% & 98\% & 98\% & 97\% & 81\% & 84\% & 84\% & 85\% \\
**OR-Ball Fault** & 97\% & 97\% & 98\% & 99\% & 82\% & 83\% & 84\% & 85\% \\
**IR-OR Fault** & 98\% & 100\% & 99\% & 99\% & 82\% & 82\% & 79\% & 82\% \\
**IR-OR-Ball Fault** & 97\% & 98\% & 97\% & 98\% & 78\% & 80\% & 82\% & 83\% \\
**Average** & 97.9\% & 98.1\% & 98.2\% & 98.4\% & 78.8\% & 80.2\% & 82.4\% & 84.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Fault diagnosis results for various motor speeds using the proposed method and ALPIO (crack size=6 mm).
## 2 Materials and Methods
### Data Pre-Processing and Feature Extraction
#### 2.2.3 Activity Count and Class Imbalance in Sensor Data  
The duration of the investigated activities varied significantly. Specifically, \"walking with the crate\" and \"walking without the crate\" took considerably more time compared to other activities, including \"standing still\", \"bending\" to approach the crate, \"lifting crate\", and \"placing crate\" onto the UGV. The duration of each activity directly impacts the classification imbalance (Figure 3a), due to the nature of the data collection process. Consequently, longer activities generate more sensor data points than shorter activities. Furthermore, our approach of excluding all sensor data when any dataset issue was detected led to a balanced dataset for all body parts, as illustrated in Figure 3b. To handle this imbalance, an under-sampling technique was used, similarly to [24].
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
###### Abstract  
This paper surveys the energy-efficient routing protocols in wireless sensor networks (WSNs). It provides a classification and comparison following a new proposed taxonomy distinguishing nine categories of protocols, namely: Latency-aware and energy-efficient routing, next-hop selection, network architecture, initiator of communication, network topology, protocol operation, delivery mode, path establishment and application type. We analyze each class, discuss its representative routing protocols (mechanisms, advantages, disadvantages...) and compare them based on different parameters under the appropriate class. Simulation results of LEACH, Mod-LEACH, iLEACH, E-DEEC, multichain-PEGASIS and M-GEAR protocols, conducted under the NS3 simulator, show that the routing task must be based on various intelligent techniques to enhance the network lifespan and guarantee better coverage of the sensing area.  
wireless sensor networks; routing protocols; performance analysis; taxonomy; classification +
Footnote †: : 10.0  
A performance evaluation through the NS3 simulator is discussed in Section 4. Lastly, the conclusion and our future work are presented in Section 5.
# Study on the Propulsion Characteristics of a Flapping Flat-Plate Pumping Device
## 4 Results
### Effect of Flow Channel Structure on the Water Body
#### 4.2.2 Effect of Flow Channel Structure on Water-Pushing Performance  
To further analyze the effect of the outlet angle on the pumping performance of the flapping flat-plate hydrofoil, Figure 17 shows the pumping efficiency and propulsion efficiency curves for the device at different outlet angles.  
It can be observed from the figure that pumping efficiency and propulsion efficiency show a positive correlation in overall trend, with pumping efficiency slightly lower than propulsion efficiency. As the outlet angle increases, both propulsion and pumping efficiency first increase and then decrease. From the efficiency analysis, both pumping efficiency and propulsion efficiency reach their peak values at an outlet angle of \(-10^{\circ}\). Compared to an outlet angle of \(0^{\circ}\), the \(-10^{\circ}\) angle results in an 8.5% increase in pumping efficiency and a 10.2% increase in propulsion efficiency, indicating that a moderate negative angle can enhance fluid propulsion performance. This finding is consistent with previous analyses  
Figure 17: Pumping efficiency and propulsion efficiency curves for different outlet angles.  
of vorticity and velocity contours, where the flow at the channel outlet remains stable within this angle range, with well-organized vortex structures and moderate shear layer separation, favoring the continuity and directional coherence of the wake.  
As the outlet angle increases outward, both pumping efficiency and propulsion efficiency exhibit a declining trend, with the decrease in pumping efficiency being more pronounced. Under such conditions, the velocity of fluid near the wall is significantly reduced, and the local flow fails to effectively merge with the main stream, resulting in a distinct flow separation region. This separation does not lead to immediate vortex formation, but rather manifests as an elongated low-velocity region stretched along the outlet direction. The expansion of this region indicates that part of the fluid's kinetic energy is dissipated by wall friction, causing deflection in the flow direction and non-uniformity in the wake velocity field, which directly reduces both pumping and propulsion efficiency.  
However, at \(\alpha=-30^{\circ}\), both pumping efficiency and propulsion efficiency drop to their lowest values, at 8.1% and 17.6%, respectively. As shown in Figure 16, a distinct recirculation region forms above the flapping flat-plate hydrofoil, and the wake shifts upward, resulting in a significant decrease in both pumping and propulsion efficiencies.
## 2 Study Area and Data Used
### Landslide Explanatory Variables
#### 2.3.1 Topography Factors  
Topography factors include altitude, slope degree, slope aspect, profile curvature, and plan curvature. Altitude is an influential factor among the various landslide explanatory variables, because it is affected by several geomorphological and geological processes. Slope, which can be described as the form between any section of the surface and a horizontal datum, has considerable influence on slope stability ([PERSON], 1983). The degree of vulnerability to landslides may differ based on slope direction, because the water content of the surface, vegetation type, and soil strength may be different. In addition, both the profile and plan curvatures can be classified as flat, concave, or convex. During the rainy season, concave slopes may contain more moisture than convex slopes or flat slopes, so the concave slopes may be more vulnerable to landslides. All of these variables were extracted from the 10-m digital elevation model (DEM), using the spatial analyst tool of ArcGIS. The DEM was produced from 1:5000 topographic maps provided by the Korean National Geographic Information Institute.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Factor** & **Source** & **Scale** & **GIS and** \\  & & & **(Resolution)** & **Data Type** \\ \hline - & Landslide inventory & Aerial photographs & 1:5000 & Raster \\ \hline Topography & Altitude & Topographic maps & 1:5000 & Vector \\  & Slope degree & Digital elevation map & 10 \(\times\) 10 m & Raster \\  & Slope aspect & & & \\  & Profile curvature & & & \\  & Plan curvature & & & \\ \hline Hydrology & Distance to streams & Digital elevation map & 10 \(\times\) 10 m & Raster \\  & Topographic wetness index & & & \\  & Stream power index & & & \\  & Sediment transport index & & & \\  & Terrain roughness index & & & \\ \hline Forestry & Timber type & Forest map & 1:5000 & Vector \\  & Timber diameter & & & \\  & Timber age & & & \\  & Timber density & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information and sources of data used for the landslide susceptibility assessment at Woomyeon Mountain.
# A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation
## 2 Mathematical Modeling of PEMFC
In the PEMFC anode (1):  
\[\mathrm{H_{2}}\rightleftharpoons\mathrm{H^{+}}+2e^{-} \tag{1}\]  
In the PEMFC cathode (2):  
\[\frac{1}{2}\mathrm{O_{2}}+2\mathrm{H^{+}}+2e^{-}\rightleftharpoons\mathrm{H_{2}O} \tag{2}\]  
In the entire PEMFC (3):  
\[\mathrm{H_{2}}+\frac{1}{2}\mathrm{O_{2}}\rightleftharpoons\mathrm{H_{2}O}+W_{ \mathrm{el}}+Q_{\mathrm{heat}} \tag{3}\]  
reactions occur. The electrochemical and fundamental thermodynamic equations resulting from these reactions form the basis for deriving the mathematical model of the fuel cell [30]. The operating voltage of a single cell, \(v_{\mathrm{fc}}\), is presented in Equation (4). It is defined as the difference between the open-circuit voltage \(E\) and the sum of various voltage losses. Specifically, the cell voltage is obtained by subtracting the activation loss \(v_{\mathrm{act}}\), ohmic loss \(v_{\mathrm{ohm}}\), and concentration loss \(v_{\mathrm{conc}}\) from \(E\), as defined in Equation (5). These loss components are calculated using Equations (6)-(11). \[v_{\mathrm{fc}}=E-v_{\mathrm{act}}-v_{\mathrm{ohm}}-v_{\mathrm{conc}} \tag{4}\]  
\[E=1.229-8.5\times 10^{-4}(T_{\mathrm{fc}}-298.15)+4.308\times 10^{-5}\,T_{ \mathrm{fc}}\,\ln\left(\frac{P_{\mathrm{O_{2}}}}{2}+P_{\mathrm{H_{2}}}\right) \tag{5}\]  
\[v_{a}=\left[\xi_{1}+\xi_{2}T_{\mathrm{fc}}+\xi_{3}T_{\mathrm{fc}}\ln(C_{ \mathrm{O_{2}}})+\xi_{4}T_{\mathrm{fc}}\ln(I_{\mathrm{fc}})\right] \tag{6}\]  
\[C_{\mathrm{O_{2}}}=\frac{P_{\mathrm{O_{2}}}}{5.08\times 10^{6}}\exp\left( \frac{498}{T_{\mathrm{fc}}}\right) \tag{7}\]  
Figure 1:0  
Figure 1:1  
Figure 1:2  
Figure 1:3  
The theoretical output voltage obtained from this mathematical modeling differs significantly from the ideal behavior of the fuel cell when compared to performance losses encountered in practical applications. Although this framework is effective in describing the voltage-current relationship of PEMFC systems at a theoretical level, the accuracy and validity of the model largely depend on the precision of the parameters used and the extent to which the model reflects actual system behavior [31]. Accurate identification of parameters related to physical phenomena such as activation losses, ohmic resistances, and concentration effects is crucial for ensuring reliable model predictions under both static and dynamic operating conditions. However, direct measurement of these parameters is often impractical, and conventional methods frequently fall short due to high computational costs or convergence to local minima. In this context, optimization-based approaches provide an effective solution by enhancing parameter fitting, minimizing discrepancies between model predictions and experimental data, and yielding more reliable simulation outputs [32]. In this study, the SHO algorithm, inspired by the adaptive movements of seahorses in nature, is employed to determine the optimal values of the unknown parameters \(\varepsilon_{1},\varepsilon_{2},\varepsilon_{3},\varepsilon_{4},b,\lambda,R_{c}\) in the PEMFC model.
## 2 Models and Data  
### Motivation  
Snow interacts strongly with the global climate system, serving as an energy bank [15; 16], radiation shield [17], insulator [18], reservoir [19], and transport medium [20; 21; 22]. For these reasons, it is vitally important to understand and accurately measure the physical properties of snow.  
An important parameter in climate research is the ratio of reflected light to the incoming global shortwave radiation, also know as the albedo [23]. For a given spectral distribution of the incoming solar radiation, the albedo of a homogeneous sufficiently thick snow layer depends mainly on snow quality, which can be described by the type of snow (snow density and grain size) [24] and its impurity concentration (concentration of light absorbing particles in the snow, such as black carbon or dust) [25]. Since snow grain size and impurity content change over time, the albedo of a snow-packed region often decreases with time until it is refreshed by new snowfall [26]. This cycle can be used to determine the melting processes of a snow-packed region as well as the monitoring of snow accumulation from remote sensing data collected by sensors deployed on satellites.  
Light from the sun will be absorbed and scattered in predictable ways depending on the snow's physical and optical properties. Remote sensing instruments deployed on satellites can collect reflected radiances at the top of the atmosphere. These TOA radiances depend on snow properties as well as the sun-satellite geometry, i.e., the solar zenith angle, the sensor polar viewing angle, and the relative azimuth angle. A radiative transfer tool can be used to model these TOA radiances as a function of wavelength. A flowchart summarizing the methods used in data formation, modeling, and algorithm formation is shown in Figure 1.
## 5 Case Study and Simulation Results
### Simulation Cases  
Figure 9 shows the daily measurements of PV generation and load consumptions of the energy router-based HEMS. The HEMS is in grid-connected mode and imports energy from the power grid during 0-t1 and t2-24, since the PV power generation is lower than load consumption. During the period between t1 and t2, the HEMS is in isolated mode. Because the HEMS will experience operation mode switch between grid-connected mode and isolated mode at the moment of t1 and t2, the operation of the HEMS at these moments will be investigated as study cases to verify the effectiveness and feasibility of the proposed control and management strategy for the energy router. The load power reaches the maximum point at t3, before which the power of PV output is already zero. Then the situation around the moment of t3 is chosen as another case to demonstrate the potential of the energy router-based HEMS in collaboration with the power grid to suppress the impact of load consumption fluctuations.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 2 Related Works
### Loop-Free Alternates and Remote-LFA FRR  
The LFA FRR mechanism uses a loop-free alternate next-hop to bypass failed element in the network [59]. LFA uses specific conditions to select a loop-free next-hop router. Therefore, the LFA is only reliable in topologies with a high mesh architecture and \"correct\" metrics. The LFA next-hop can be selected only if the potential backup next-hop is loop-free. There are many situations where LFA is unable to select the next-hop correctly.  
Because the LFA FRR mechanism has a problem with determining a valid next-hop in some topologies and under specific conditions, and therefore its deployment is not possible here, an extended version called Remote LFA has appeared. The main enhancement in the case of the Remote LFA is the use of the remote, loop-free router that is more than one hop away from the source router S. Being able to reach this remote loop-free router, the Remote-LFA FRR mechanism creates a tunnel from the source router S to the remote loop-free router (D router). When packets enter the tunnel, they are encapsulated (tunnelled). Whenthey reach the tunnel endpoint, they are decapsulated. After the decapsulation process, packets are routed via classic IGP unicast routing.
# Precision Agriculture: Assessment of Ergonomic Risks of Assisted Driving System
## 2 Materials and Methods
### Satellite System  
The StarFire 6000 ([PERSON], Moline, IL, USA) receiver, equipped with dual frequencies and RTK (Real-Time Kinematic) correction, can precisely determine the tractor's position, speed, and current time with centimeter-level accuracy to facilitate the effective functioning of the associated guidance systems. Additionally, these data were transmitted to and compiled by the farm information system for further analysis.