esa-sceva/satcom-chunk-collection huggingface dataset:
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 6 Conclusions  
As UAV technology develops gradually, it is necessary to optimize the ever-changing UAV networks. In a UAV network, data are transmitted and received based on broadcasting or ad hoc routing. Although broadcasting can deliver data reliably to the destination, the network load is high. In comparison, ad hoc routing wastes the network bandwidth and introduces network delays in transmitting the routing information needed to establish an effective communication path. Based on these observations, we proposed a method of optimizing data transmission through UAV networks based on RL to exploit the advantages of the two methods. In the proposed system, we utilized geographic location information in a situation where the location changed due to the movement of the UAV and also devised a new DQN called \(aDQN\) to optimize the forwarding area.  
We have several directions for future work. The proposed system can be further improved by using more learning algorithms besides aDQN. Instead of flooding, it may be possible to derive a more advanced routing system by identifying how different routing algorithms are used in the UAV environment and configuring optimization plans. Alternatively, we can increase the accuracy of existing routing algorithms by considering the mobility errors used in AGLAN. In addition, we plan to build an optimized routing path by utilizing the GLAN algorithm in a communication environment where network information is not accurate or does not arrive on time. Regarding security issues, we wish to solve the problem of allowing only data with a separate network key to be transmitted and received.  
Conceptualization, C.P. and H.K.; methodology, C.P.; software, C.P.; validation, C.P. and H.K.; formal analysis, C.P.; resources, H.K.; data curation, H.K.; writing--original draft preparation, C.P., S.L., H.J. and H.K.; writing--review and editing, H.K.; visualization, C.P.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20204010600220) and the National Research Foundation of Korea funded by the Korean Government (grant 2020R1A2C1012389).  
Figure 13: Comparison of bandwidth among flooding, GLAN, and AGLAN.  
Data Availability Statement: Not applicable.  
Conflicts of Interest: The authors declare no conflicts of interest. Furthermore, the funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Tool Application to Evaluate Pollutant Emissions
### Air Nozzle Approach
#### 4.1.3 Real Flowrate Calculation  
The previous equations can be combined to obtain the formulation for calculating the air flowrate as follows \(\dot{m}_{nozzle}\):  
\[\begin{array}{ll}\dot{m}_{nozzle}=\epsilon\dot{m}_{real}&=C\epsilon\pi\frac{ D_{2}^{2}}{4}\sqrt{2\rho_{air}|p_{1}-p_{2}|}=\sqrt{2}C\in\pi\frac{D_{2}^{2}}{4} \sqrt{\rho_{air}|\Delta p_{air\ nozzlele}|}=\\ &=K\sqrt{\rho_{air}|\Delta p_{air\ nozzlele}|}\end{array} \tag{10}\]  
in which:  
\(K=\sqrt{2}C\in\pi\frac{D_{2}^{2}}{4}\left[\text{m}^{2}\right]\) is the Air Nozzle coefficient;  
\(p_{air}\left[\text{kg}/\text{m}^{3}\right]\) is the air density, equal to \(\rho_{air}=\frac{100p_{0}}{R_{air}(t_{1}+273.15)}\), where \(R_{air}\ =\ 287.04\ \text{J}/\text{kg}\);  
\(\left[\Delta p_{air\ nozzlele}\right]\ \left[\text{mbar}\right]\) is the orifice plate differential pressure. Usually, values are between 100 and 250 mmHg\({}_{2}\)O.  
In order to take into account the measurement units used for \(\left|\Delta p_{air\ nozzlele}\right|\ \left[\text{mbar}\right]\), it is necessary to introduce a small change in the previous formula as follows:  
\[\dot{m}_{nozzle}=K\sqrt{100\rho_{air}|\Delta p_{air\ nozzlele}|}\ \left[\text{Pa}\right]. \tag{11}\]  
Eventually, it is worth noting that the air flowrate that effectively reaches the engine is lower, due to compressor losses. Moreover, a situation with multiple inlets to the turbocharger (\(n^{\circ}_{TC}\)) could be the real one. As a result, the following equation is the most correct one to evaluate the real air flowrate \(\dot{m}_{air\ eng}\):  
\[\dot{m}_{air\ eng}=\left(1-\frac{TC_{\text{sailing air}}}{100}\right)\dot{m} ^{\circ}_{TC}\dot{m}_{nozzle} \tag{12}\]  
in which \(TC_{\text{sending air}}\ \left[\%\right]\) represents the air loss within the compressor seal. Its approximated value is usually equal to 1.5%.  
Finally, the exhaust mass flowrate \(\dot{m}_{exh}\) can be calculated as follows:  
\[\dot{m}_{exh}=3600\dot{m}_{air\ eng}+10^{-3}P_{eng}BSFC_{total}\ \left[\text{kg}/\text{h}\right] \tag{13}\]  
where \(BSFC_{total}\ \left[\text{g}/\text{kWh}\right]\)_is_ the total brake-specific fuel consumption, which is defined below. Indeed, for the flow equivalence principle, the exhaust mass flowrate is equal to the air mass flowrate plus the burnt-fuel flowrate.
# Numerical Study on Scale Effect of Repetitive Plate-Loading Test
## 4 Analysis of the Results
### Examination through Comparison with the Scale Effect of the Existing Plate-Loading Test
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Scale Effect of Plate-Loading Test** & \multicolumn{2}{c}{**Scale Effect of Repetitive Plate-Loading Test**} \\ \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Scale Effect**} & \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Scale Effect**} \\  & & **Calculation Factor**\(a\) & & **Calculation Factor**\(a\) \\ \hline \multirow{3}{*}{Sand} & Loose sand (Bond [9]) & 0.8–0.6 & Gravel and & \\  & Dense sand (Bond [9]) & 0.6–0.5 & fine gravel & \\ \cline{1-1} \cline{2-5}  & Pure sand ([PERSON] and [PERSON] [10]) & 0.505 & Sand & 0.2 \\ \hline \multirow{3}{*}{Soil} & Cohesive soil ([PERSON] and [PERSON] [8]) & 0 & Sandy soil & 0.12 \\ \cline{1-1} \cline{2-5}  & Cohesive soil ([PERSON] and [PERSON] [10]) & 0.035 &
\begin{tabular}{c} Cohesive \\ soil \\ \end{tabular} & 0.11 \\ \hline \multicolumn{5}{l}{Korean standard of plate load test, KS F 2444 (KSA [11])} & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Scale-effect comparison between the plate loading and repetitive plate-loading tests. nonlinear behavior due to the decrease in the loading-plate size, and we confirmed that the strain modulus was generally assessed to be somewhat smaller than the elastic modulus. The strain and elastic moduli were compared using the 762-mm loading plate, which was the reference loading plate, and the summary of the results is listed in Table 8. The standard deviation of the ratio of the elastic modulus to the strain modulus was zero in most of the subgrade conditions, and the elastic modulus of the subgrade appeared to be estimated from the result of the strain modulus. We found that the strain modulus was higher than the elastic modulus in the sandy and cohesive soil. Apparently, this was because the estimation equation of the strain modulus was calculated as an approximate value by the regression equation of the repetitive loading curve, and errors occurred in the process. In other words, the ratio of the elastic modulus to the strain modulus was determined to be 1:1 for the sandy and cohesive soil, which was the same as the relationship between the strain and elastic moduli proposed by [PERSON] et al. [2]. By summarizing these results, Table 9 lists the relationship between the strain modulus of the repetitive plate-loading test and the elastic modulus according to the subgrade condition. From these results, we expect that the elastic modulus of the corresponding subgrade can be assessed using the repetitive plate-loading test. Figure 8: Relationship between the subgrade strain and elastic moduli according to the loading-plate size: (**a**) gravel and fine gravel; (**b**) sand; (**c**) sandy soil; (**d**) cohesive soil.
## 2 System Model and GD Test Statistics
### Moment Generation Function of the GD Partial Test Statistics \(T_{ GD}({\bf X}_{k})\)  
To define the mean \(m_{\Psi_{6}}^{ GD}\) and variance \(Var_{\Psi_{6}}^{ GD}\) of the test statistics \(T_{ GD}({\bf X})\) under the hypothesis \(\mathcal{H}_{0}\), the moment generation function (MGF) of the GD partial test statistics \(T_{ GD}({\bf X}_{k})\) given by  
\[T_{ GD}({\bf X}_{k})=\sum_{i=1}^{M}s_{i}^{2}[k]+\sum_{i=1}^{M}\eta_{i}^{2}[k] -\sum_{i=1}^{M}\xi_{i}^{2}[k] \tag{23}\]  
is required. The MGF of the GD partial test statistics \(T_{ GD}({\bf X}_{k})\) is presented as:  
\[\mathcal{M}_{\gamma_{ GD}({\bf X}_{k})}(l)=\prod_{i=1}^{M}\frac{1}{1-E_{s} \sigma_{\eta}^{2}\,\alpha_{i}l}\prod_{i=1}^{M}\mathcal{M}_{\gamma_{\gamma_{i}} }(l)\prod_{i=1}^{M}\mathcal{M}_{\gamma_{\gamma_{i}}}(-l)=\prod_{i=1}^{M}\frac {1}{\sqrt{(1-2\,\sigma^{2}\,l)(1+2\,\sigma^{2}\,l)}}\times\frac{1}{1-E_{s} \,\sigma_{\eta}^{2}\,\alpha_{i}l}\]  
\[=\frac{1}{\sqrt{(1-4\,\sigma^{4}\,l)^{M}}}\prod_{i=1}^{M}\frac{1}{1-E_{s}\, \sigma_{h}^{2}\,\alpha_{i}l} \tag{24}\]  
Derivation of Equation (24) in detail is given in Appendix A.  
Based on Equation (24), the mean \(m_{\Psi_{6}}^{ GD}\) and the variance \(Var_{\Psi_{6}}^{ GD}\) of the test statistics \(T_{ GD}({\bf X})\) under the hypothesis \(\mathcal{H}_{0}\) take the following form, respectively:  
\[m_{\Psi_{6}}^{ GD}=E[T_{ GD}({\bf X}_{k})\big{|}\mathcal{H}_{0}]=NME_{s}\sigma_{h}^{2} \tag{25}\]  
\[Var_{\Psi_{6}}^{ GD}=Var[T_{ GD}({\bf X}_{k})\big{|}\mathcal{H}_{0}]=N\Bigg{[}\sum_{i=1}^{M}E_{s}^{2} \sigma_{h}^{4}\alpha_{i}^{2}+4M\sigma^{4}\ \Bigg{]} \tag{26}\]  
For the case \(\sigma_{\xi}^{2}\
eq\sigma_{\eta}^{2}\), Equations (25) and (26) take the following form  
\[m_{\Psi_{6}}^{ GD}=E[T_{ GD}({\bf X}_{k})\big{|}\mathcal{H}_{0}]=NME_{s}\sigma_{h}^{2} \tag{27}\]  
\[Var_{\Psi_{6}}^{ GD}=Var[T_{ GD}({\bf X}_{k})\big{|}\mathcal{H}_{0}]=N\Bigg{[}\sum_{i=1}^{M}E_{s}^{2} \sigma_{h}^{4}\alpha_{i}^{2}+2M\sigma^{4}(1+\beta^{2})\ \Bigg{]} \tag{28}\]  
\[SNR_{wall}^{MF}=\frac{1}{\theta\ K_{c}}\frac{\rho^{2}-1}{\rho} \tag{44}\]  
The ED has the better sample complexity performance at the high _SNR_ in comparison with the MF because the ED uses the total average PU signal power for detection while the MF uses only a fraction of the total PU signal power. In the case of MF possessing the pilot tone detection scheme, the _SNR_ wall phenomenon is a consequence of time-selectivity of the channel fading process and the signal power is increased with the factor \(K_{c}\) owing to increasing the coherent processing gain. This is the reason why we see that the MF is sensitive to the channel coherence time \(K_{c}\). Thus, the effective _SNR_ of the coherently combined signal according to [1, 2] is given by  
\[SNR_{off}^{MF}=\theta\ K_{c}SNR \tag{45}\]
# A High-Precision Energy-Efficient GPS Time-Sync Method for High-Density Seismic Surveys
## 1 Introduction
Therefore, the time-synchronization method can not only reduce the power consumption of the continuous use of GPS but also ensure the high-precision time service without the GPS signal. Even if the GPS signal is lost for several hours, the node can still acquire the seismic data with its high-precision TCXO continuously. After the node data is collected, the control center analyzes the clock shift of each node to ensure the time accuracy of the collected data automatically. In practical applications, this method has high time-service accuracy and energy efficiency. The remainder of this paper is organized as follows: Development of the energy-efficient GPS time-synchronization method is discussed in Section 2. The performance evaluation and results of system tests, including two field measurements, are presented in Section 3. The test results of the proposed time synchronization method and future research directions are discussed in Section 4. Finally, the conclusions of this work are discussed in Section 5.
## 5 Conclusions  
Using foam with appropriate parameters as a soil conditioner to reduce the permeability of the soil during the EPB TBM in cohesionless soil stratum is an effective method to prevent spewing. This paper presented a series of experiments to assess the influence of foam parameters on conditioned soil permeability. Based on the experimental results, a case study was performed to verify the optimum foam formation. The main findings of the soil permeability experiments and the field tests are summarized as follows:  
(1) The larger \(T_{\rm{hl}}\) is, the lower the conditioned sand permeability. For the foaming agent with \(T_{\rm{hl}}>30\) min (FER = 10 and FIR = 50%), the permeability coefficient of the conditioned sand was less than \(2\times 10^{-5}\) cm/s within 30 min. The results suggested that highly stable foam products were more suitable cohesionless soil. And the field experiments also verified that the ZDF-01P foam agents were effective in reducing the outlet pressure of the screw conveyor.  
(2) When FER increased, the permeability first decreased and then increased (FER \(>\) 15), due to the increase in the FER improved the foam stability and reduced the water content. The results showed that too low or too high FER was not good for reducing permeability. The field experiments verified that the pressure in the chamber and the outlet pressure of the screw conveyor rapidly increased when the FER was increased from 10 to 20.  
(3) The permeability decreased with increasing FIR, and there was no significant change when the FIR = 70%-100%. The FIR primarily affected the rate of change in the permeability coefficient, but had a little effect on permeability. A suitable FIR can reduce the permeability coefficient and simultaneously reduce the consumption of foam. Similar results was obtained in the field experiments: When FIR was higher than 33%, increasing FIR was not an effective method to prevent against water spewing.  
Permeability tests and field experiment results suggested that using high-stability foaming agents with a medium foam expansion ratio (FER = 10-20) and foam injection ratio (FIR = 50%-75%) was effective to reduce the cohesionless soil permeability and to prevent the occurrence of water spewing from the screw conveyor.  
Conceptualization, X.Z. and Y.Y.; methodology, X.Z.; formal analysis, X.Z. and Y.Y.; investigation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z. and Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFC0805008 and the Fundamental Research Funds for the Central Universities, grant number 265201708.  
The authors declare no conflict of interest.
###### Abstract  
Rotating machinery is widely used across various industries, making its reliable operation crucial for industrial production. However, in real-world settings, intelligent fault diagnosis faces challenges due to imbalanced fault data and the complexity of neural network models. These challenges are particularly pronounced when defining decision boundaries accurately and managing limited computational resources in real-time machine monitoring. To address these issues, this study presents KDE-ADASYN-based MobileNet with SENet (KAMS), a lightweight convolutional neural network designed for fault diagnosis in rotating machinery. KAMS effectively handles data imbalances commonly found in industrial applications and is optimized for real-time monitoring. The model employs the Kernel Density Estimation Adaptive Synthetic Sampling (KDE-ADASYN) algorithm for oversampling to balance the data, applies fast Fourier transform (FFT) to convert time-domain signals into frequency-domain signals, and utilizes a 1D-MobileNet network enhanced with a Squeeze-and-Excitation (SE) block for feature extraction and fault diagnosis. Experimental results across datasets with varying imbalance ratios demonstrate that KAMS achieves excellent performance, maintaining nearly 90% accuracy even on highly imbalanced datasets. Comparative experiments further demonstrate that KAMS not only delivers exceptional diagnostic performance but also significantly reduces network parameters and computational resource requirements.  
rotating machinery; fault diagnosis; imbalanced data; online machine monitoring; lightweight neural network +
Footnote †: journal: Applied sciences  
Article  
[PERSON], [PERSON], [PERSON], [PERSON], Z. A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data
## 5 Discussion  
The energy of the calibration piece form deviation refers to the distribution of microscopic peaks and valleys not defined in the GT profiles. A metric known as the energy of \(D_{\Delta,\beta}(k)\) is implemented to quantify and analyze these characteristics. The \(D_{\Delta,\beta}(k)\) metric represents the displacement of the milling part, peaks, and residue of the machining process defined as  
\[E=\frac{1}{N}\sum_{k=0}^{N-1}D_{\Delta,\beta}(k)^{2} \tag{9}\]  
The results of the energy analysis for the calibration pieces are illustrated by the shapes in Figure 36 and by quality in Figure 37. Based on \(D_{\Delta,\beta}(k)\) analysis, it becomes evident that the curve generates more energy compared to the line and slope. The heightened energy output can be attributed to the inherent complexities of machining curves, especially in scenarios where the machine simultaneously interpolates the X and Y axes to conform to the specified radius.  
When machining a curve, the machinery co-ordinates the movement along two axes simultaneously, necessitating a more intricate interpolation process. The dual-axis interpolation demands the machine's capabilities, requiring it to precisely synchronize movements to conform to the curvature specified in the program. The intricate nature of this simultaneous interpolation results in increased energy consumption during the machining process, as compared to simpler geometries such as a straight line or a slope. Regarding contour profile quality, calibration pieces with form deviation defects generate more energy during machining due to the inherent challenges of irregular surface features.  
The energy generated in the slope, line, and curve profiles varied for delrin, nylon, aluminum, and steel. Delrin exhibits low energy generation, nylon exhibits high energy generation, and aluminum exhibits moderate energy generation. These differences are primarily influenced by the friction coefficients of the materials and their resistance to deformation under stress [47].  
Figure 36: Energy of \(D_{\Delta,\beta}(k)\) classified by contour profile.  
Figure 38 displays the energy at zero frequency and the frequencies \(0<k>101\) of \(D\Delta,\beta(k)\). The amplitude at zero frequency corresponds to millimetric displacements between the calibration piece placement in the fixture and the zero position. Within the bandwidth \(1<k<100\) for all samples, the energy is non-zero, indicating that small-scale variations in height actively contribute to the texture or deviations of the profile. Remarkably, the curve defective pieces exhibit notably higher energy levels at zero and low frequencies. This heightened energy is attributed to the challenges associated with machining irregularities on the curved surface.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 2 Related Works
### Signal Processing-Based Techniques  
In [24], the authors proposed a detection technique based on uplink received signal strength (RSS). UAVs are required to obtain authorization from the Unmanned Aircraft System Traffic Management (UTM) before each flight, after which they must periodically report their telemetry data, including GPS position in a 5G-enabled environment, as mandated by the Federal Aviation Administration (FAA). The UTM then compares the UAV's reported GPS position with the position calculated using trilateration of distances to at least three base stations determined via RSSI-based distance estimation.  
The study proposed in [18] leveraged an existing global network of 700 air traffic control sensors to detect and mitigate GPS spoof attacks. UAVs and aircraft periodically transmit advertisement messages containing their GPS positions to at least four of these air traffic controllers. The system then verifies these positions by comparing them with estimated locations, calculated by multilateration, using the distances between the aircraft and the controllers. These distances are computed using the Time Difference of Arrival (TDoA)-based technique.  
A GPS spoofing detection system inspired by burglary scene analysis was proposed in [25] to identify sophisticated GPS spoofing attacks launched from multiple sources. The approach analyzes the absolute signal power and the carrier-to-noise ratio (C/N\({}_{0}\)) of the received GPS signals to detect anomalies. Since neighboring UAVs also receive propagated spoofing signals, they can act as witnesses to confirm the presence of any attack. In addition, a trust policy is implemented for witness nodes to prevent the dissemination of false information.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 2 Structure and Control Strategy of FSC-VSPSU
### Optimal Efficiency Control  
As shown in Figure 1, the unit control system mainly includes the converter control system, governor system, excitation system, and the coordinated control system. The unit adopts an optimal efficiency control strategy. The coordinated control system is responsible for receiving regulation commands and generating the reference signals for the optimal speed. The principle of generating the optimal reference speed under power generation and pumping conditions is shown in Figure 2. It takes the waterhead \(H\), power reference, or guide vane opening reference as input. Based on the comprehensive characteristics of the pump-turbine, the system obtains the speed corresponding to the highest efficiency of the unit under the current operating conditions. This allows for achieving the best operational speed under varying conditions, ensuring the pump-turbine operates at peak efficiency.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **OTUs** & **ACE** & **Chao1** & **Evenness** & **Shannon** & **Coverage** \\ \hline TC & \(-0.28\) & \(-0.12\) & \(-0.13\) & \(-0.21\) & \(-0.07\) & \(0.01\) \\ TN & \(0.05\) & \(0.10\) & \(0.10\) & \(0.05\) & \(0.19\) & \(0.02\) \\ pH & \(0.50\)* & \(0.57\)* & \(0.57\)* & \(0.17\) & \(0.36\) & \(-0.48\) \\ EC & \(0.04\) & \(0.37\) & \(0.38\) & \(0.41\) & \(0.46\) & \(-0.36\) \\ Cr & \(0.05\) & \(-0.07\) & \(-0.08\) & \(-0.13\) & \(-0.08\) & \(0.11\) \\ Ni & \(0.14\) & \(0.03\) & \(0.03\) & \(-0.16\) & \(0.01\) & \(0.12\) \\ Cu & \(0.11\) & \(0.02\) & \(0.03\) & \(-0.23\) & \(-0.09\) & \(0.04\) \\ Zn & \(0.32\) & \(0.01\) & \(0.01\) & \(-0.04\) & \(-0.06\) & \(0.15\) \\ As & \(-0.08\) & \(-0.29\) & \(-0.29\) & \(0.00\) & \(-0.26\) & \(0.18\) \\ Cd & \(-0.23\) & \(-0.53\)* & \(-0.53\)* & \(-0.47\) & \(-0.62\)* & \(0.38\) \\ Pb & \(-0.03\) & \(0.21\) & \(0.20\) & \(0.13\) & \(0.14\) & \(-0.29\) \\ \hline \end{tabular} \({}^{1}\) Note: * \(p<0.05\).  
\end{table} 
Table 3: Pearson correlation analysis between bacterial diversity and environmental variables \({}^{1}\).