esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 2 Materials and Methods
### Method
#### 2.3.2 Feature Extraction  
The gray level of one pixel is not informative for color prediction, so additional information such as texture and local context is necessary [15]. In order to extract the maximum information for color prediction, features that describe the textural information of local neighborhoods of pixels are considered. Previous automatic colorization methods used Speed-Up Robust Features, Gabor features, or a histogram of oriented gradients as base tools for textural analysis [14; 15]. These descriptors are known to be discriminative but also computationally and memory intensive due to their high number of features. Moreover, recent approaches have separated texture from structure using relative total variation, but their descriptors are not sufficiently accurate to discriminate textures among themselves. Consequently, statistical features are utilized here, as this approach is simple, easy to implement, and has strong adaptability and robustness, among which the gray-level co-occurrence matrix (GLCM) is used [47; 48]. The GLCM is used extensively in texture description, and the co-occurrence matrices provide better results than do other forms of texture discrimination [49; 50]. For remote sensing images, four types of statistics--angular second moment, contrast, correlation, and entropy--are better suited to texture feature extraction, so they have been selected for statistics in this study [51]. Also, in order to calculate the GLCM values, the window size should be set. The present study has ultimately selected a 5 \(\times\) 5 window size, which better reflects coarse and fine textures [48]. Furthermore, the intensity value and the mean and standard deviation of the intensity within a 5 \(\times\) 5 neighborhood are included as supplementary components.
# Force Fight and Its Elimination in a Prototype of a Redundant Direct-Drive Avionic Actuator
## 8 Conclusions  
In this paper, the design and the manufacturing of a prototype achieve the new concept of the direct-drive actuator with four BLAC motors. Indeed, the combination of electrical and mechanical redundancies in its design allows on the one hand to ensure high reliability in healthy and faulty operating modes and on the other hand to have a reduced size for its integration as a new subsystem in Fly Control. In addition, with each motor lane having its own control loops with its own position and current sensors, a drawback is highlighted in faulty operating mode creating a conflict between all the motor lanes. This phenomenon, called force fight, is due to faults in position feedback, position command, or torque command. Even when the position command is reached; the force fight induces higher torque for all motors, current consumption, thermal elevation and performance reduction. However, some methods are suggested in order to avoid force fight, using monitoring and communication between lanes in order to merge the different position or command information. Among the proposed methods, the master/slave method has been selected according to the application, to be implemented in the test bench. The experimental results confirm the validity of this method to eliminate force fight between motor lanes and to ensure good performance of the direct-drive actuator during operation.  
In addition, other faults created on the test bench confirmed the robustness of the designed control even in faulty operation mode. However, the designed control confirms the reliability of the system in healthy and faulty operation modes.  
Finally, for the next generation of helicopters, the designed actuator will be able to meet the demand for the electrification of the actual flight control with high reliability. In addition, this new concept will be used for other functions such as flight assistance, automatic pilot, or unmanned flight.  
Conceptualization, P.E. and R.S.; methodology, P.E. and R.S.; software, P.E.; validation, P.E., R.S., G.K. and B.B.; formal analysis, P.E., R.S., G.K. and B.B.; investigation, P.E. and R.S.; resources, R.S., G.K. and B.B.; data curation, R.S.; writing--original draft preparation, P.E., R.S., G.K. and B.B.; writing--review and editing, P.E., R.S., G.K. and B.B.; visualization, P.E., R.S., G.K. and B.B.; supervision, R.S., G.K. and B.B.; project administration, R.S., G.K. and B.B.; funding acquisition, R.S.. All authors have read and agreed to the published version of the manuscript.  
This research is a part of a project supported by Airbus Helicopters and the National Agency of Research and Technology (ANRT) in France.  
The authors thank the company Airbus Helicopters for its support to the project and the company ERNEO for the manufacture of the prototype according to the specifications of the avionics application.  
The authors declare no conflict of interest.
## 3 Case Study B
### Minimum Logarithmic Probability minLPR  
It is crucial to determine the _minLPR_ for noise point removal when employing the proposed method for point cloud classification. Figure 16 illustrates the logarithmic probability distribution curve of the point cloud classification results for case B, with the _minLPR_ set as \(-114.8513\), determined through the knee point algorithm at the curve's inflection point. The inflection point is usually defined as the point of maximum curvature in the curve. However, the logarithmic probability distribution curve in Figure 16 is composed of many discrete points, and its inflection point is difficult to accurately obtain by simply taking the second-order derivatives of the curve. This paper therefore uses the \"knee point\" detection algorithm proposed by [PERSON] et al. [53], which is suitable for discrete data. The basic principle of the algorithm is shown in Figure 17, where the discrete data are first normalized and plotted in an interval where the x and y coordinates are both 0-1 (this process preserves the morphology of the original data), and then the perpendicular distance from each discrete data point to the line y = x is found as shown in Figure 17a. The point with the largest perpendicular distance is taken as the knee point (Figure 17b). For more comprehensive information on the algorithm, refer to prior studies [53].  
Figure 16: Logarithmic probability distribution for case B point cloud classification.  
Figure 17: Principle of knee point algorithm: (**a**) perpendicular distance of the discrete point from the line y = x; (**b**) selection of knee points.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 6 Simulation and Analysis
### BER Performance of Different Algorithms  
The simulation in this subsection will be performed under two channels, and two modulation and coding schemes will be used respectively. We further demonstrate the performance of different channel estimation algorithms by comparing the Bit Error Ratio (BER) of different algorithms.The transmission systems use Low Density Parity Check Code (LDPC) as the channel coding method.  
Figures 10 and 11 show the BER performance of different algorithms under two channel models and use QPSK modulation and 1/2 coding rate. The performance of the DFT algorithm quickly appears floor because portions of the channel components are removed in the time-domain denoising process. The remaining algorithms can achieve a BER of \(10^{-4}\), and the ST-DFT algorithm has the best BER performance, followed by the DWT algorithm. The T-DFT algorithm has inferior BER performance to the first two algorithms but is better than the LS algorithm. The BER performance of the joint channel estimation algorithm is close to that of the ST-DFT algorithm since it can always select the ST-DFT algorithm to estimate the channel under low SNR condition.  
Figure 8: Performance of joint channel estimation algorithm in channel D.  
Figure 9: Performance of joint channel estimation algorithm in channel UMi.  
Figures 12 and 13 show the BER performance of different algorithms under two channel models and use 64 QAM modulation and 2/3 coding rate. The DFT algorithm also has a fast BER performance floor, while the rest of the algorithms can achieve a BER of \(10^{-4}\). The DWT algorithm has the best BER performance for this modulation coding method. The BER performance of the ST-DFT algorithm is close to that of the T-DFT algorithm and the LS algorithm. On the one hand, the MSEs of the three algorithms are close under high SNR; on the other hand, the gain from LDPC channel coding masks part of the gain from the T-DFT and ST-DFT algorithms. Moreover, the BER performance of the joint channel estimation algorithm is close to that of the DWT algorithm since it can always select the DWT algorithm to estimate the channel under high SNR condition.  
Figure 11: BER performance of different algorithms with QPSK and 1/2 coding rate in channel UMi.  
Figure 10: BER performance of different algorithms with QPSK and 1/2 coding rate in channel D.  
Comparing the BER performances in channel UMi with that in channel D leads to the same conclusion as the MSE performances. In addition, the BER performance of the proposed joint channel estimation algorithm is always close to the better one of the ST-DFT and DWT algorithms, regardless of the modulation and encoding methods used in the system.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 4 CBIR Method in the AUV Collaborative Search Mission
### Construction of Behavior Models Based on Fuzzy Inference
#### 4.1.2 Fuzzy Rules  
Fuzzy rules are used to define the relationship between input conditions and output variables. In fuzzy inference theory, fuzzy rules are expressed in the form of \"IF-THEN\", which determines the search strategy of the AUV. In this work, the overall goal of the AUV is to reach the positioning point as quickly as possible. However, during the process, adjustments may be made in individual cycles to maintain communication and optimize detection. For example, when communication quality deteriorates, the AUV is more likely to prioritize maintaining communication and to optimize detection, it may even move away from the positioning point. By listing all possible combinations of antecedents and corresponding consequents, we can derive a rule table (see Table 3).
# Avian Influenza Virus Strain Specificity in the Volatile Metabolome
## 1 Introduction  
Avian influenza, commonly known as bird flu, is an infectious disease caused by influenza A viruses with strains that range from mild to highly pathogenic [1]. While avian influenza is predominantly found in wild birds, it can also infect domestic poultry, such as chickens, ducks, and turkeys, as well as domestic companion birds like parrots and canaries. In rare cases, certain strains of the virus can be transmitted to humans, raising public health concerns [2]. Avian influenza viruses (AIV) are typically spread through contact with the saliva, nasal secretions, and feces of infected birds. Although most human infections are linked to direct exposure to infected birds or contaminated environments [3], there is concern about the potential for the virus to mutate and become more easily transmissible among humans [4].  
Due to its potential to cause both economic losses in the poultry industry and pose serious health risks to humans, AIV infection is closely monitored by public authorities worldwide. Early detection, control measures, and prevention strategies are crucial in managing outbreaks and minimizing the spread of this infectious disease. One of the greatest challenges in mitigating the impact of avian influenza is the virus's ability to rapidly evolve and spread across vast geographic areas. Given its rapid mutation rates and capacity for RNA segment re-assortment, early detection becomes crucial in controlling the spread of highly pathogenic avian influenza [5]. Without effective early detection, outbreaks can escalate quickly, resulting in both economic losses for the poultry industry and a heightened risk to public health [6].  
Using trained animals and chemometric analyses, we previously demonstrated that AIV infection was associated with a marked increase in acetoin (3-hydroxy-2-butanone) in the feces of experimentally infected ducks [7; 8]. Acetoin was suggested to result from _Bacillus_ spp. acting on elevated levels of pyruvate, which was made available through virus-induced alterations in host cell glycolysis [7]. This key finding was exploited by training ferrets to recognize increased acetoin concentrations (in ratios with 1-octen-3-ol) and then having those trained animals generalize the learned response to feces collected from healthy and infected ducks [8]. The results of that work strongly suggested that acetoin is a repeatable, salient cue for identifying AIV infection in ducks.  
The current research emphasized the critical role that the volatile metabolome can play in the early detection of AIV. We highlight the potential of non-invasive monitoring through the identification of volatile biomarkers, which can enable early intervention and help prevent large-scale epidemics. In this context, we explore alterations in the volatile metabolome caused by infection with two strains of AIV, both of which were isolated from wild mallard ducks, and identify areas for concern in the study of the volatile metabolome for infectious disease detection.
###### Abstract  
Decoupling uplink and downlink access (DUDA) has latterly proven to effectively enhance transmission efficiency in wireless communication systems, with particular effectiveness observed in both terrestrial and unmanned aerial vehicle (UAV) systems. In this paper, we propose an innovative DUDA approach specifically designed for non-geostationary orbit (NGEO) multi-layer satellite systems (MSS), integrating strategies to mitigate in-line interference to ensure spectral coexistence between geostationary Earth orbit (GEO) and NGEO satellites. Notably, the interference from the main lobe of directional antennas on NGEO satellites is meticulously characterized using a spherical surface model based on the geocentric angle. Within the framework of proposed DUDA method, a user terminal (UT) can establish communication with the satellite which provides the highest average power of received signal in compliance with the unique exclusion angle constraints of NGEO satellites. The association probability of DUDA is analyzed based on stochastic geometry. The performance evaluation, conducted in terms of transmission rate, reveals that the proposed DUDA methodology yields significant improvements when compared to conventional access schemes.  
NGEO multi-layer satellite systems; decoupling access; interference suppression; stochastic geometry +
Footnote †: journal: _electronics_  
_Article_  
Decoupling Uplink and Downlink Access for NGEO Satellite Communications with In-Line Interference Avoidance. _Electronics_  
2024133245[[https://doi.org/10.3390/electronics13163245](https://doi.org/10.3390/electronics13163245)]([https://doi.org/10.3390/electronics13163245](https://doi.org/10.3390/electronics13163245))
## 6 Simulation Results and Discussion
### Variation in Average Cost Function (UAV Energy and Latency) with Number of Vehicles (\(V\))  
The variation in average cost function (UAV energy and latency) vs. number of vehicles (\(V\)) for FL is illustrated in Figure 6 for \(V=1\)-100. The average throughput and the average transmission power also increases with the vehicle speed and road segment length. Also, the FL algorithm on vehicles converges after 20 iterations. When vehicles follow FL at a low vehicle speed of \(V=40\) km/h and for a smaller road segment length 1 km, the UAV transmits at a lower power but experiences high interference and reduced throughput. For a higher vehicle speed of \(V=80\) km/h and a larger road segment length 4 km, the UAV power consumption is notably higher.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Vehicle Mobility & Manhattan Mobility \\ \hline Number of vehicles (\(V\)) & 1–100 \\ \hline Number of UAV & 1 \\ \hline UAV deployment altitude & 100 m–3 km \\ \hline Edge server location & In-vehicle \\ \hline Communication frequency & 5.9 GHz \\ \hline Distance between vehicles & 30–100 m \\ \hline Road length & 1–5 km \\ \hline Vehicle speed & 0–100 km/h \\ \hline Payload size for BSM, CPM & 1 byte–3 Megabytes \\ \hline Payload size of FL models & 1 byte–10 Megabytes \\ \hline Dataset used & V2X-Sim, LTE 1/Q \\ \hline \(T_{BSM}\) & 100 ms–1000 ms \\ \hline \(T_{CPM}\) & 100, 200, 300, 500 ms \\ \hline Packet arrival rate (\(\lambda\)) & 1000, 2000 packets/s \\ \hline Speed of UAV & 20–50 km/h \\ \hline UAV transmission power & 20 dBm (100 mW) \\ \hline UAV receiving threshold & \(-80\) dBm \\ \hline Vehicle transmission power & 25 dBm (316.2 mW) \\ \hline Standard deviation in speed & 10 km/h \\ \hline Noise power, \(N_{o}\) & \(-110\) dBm \\ \hline SNR threshold & 3 dB \\ \hline Size of static obstacles & 20 m \(\times\) 20 m \\ \hline Channel gain & \(-30\) dBm \\ \hline BER threshold & \(10^{-4}\) \\ \hline Doppler speed & 30 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
The variation in queuing delay (\(D_{que}\)) in the FL scenario with time slots is illustrated in Figure 7, where the lowest queuing delay (\(D_{que}\)) of approximately 11.5 ms is obtained for the fed-DDPG approach. The actor-critic mechanism with LSTM also performs comparably well, with a maximum \(D_{que}\) of approximately 13.5 ms. The GRU \(D_{que}\) reaches a maximum of approximately 12 ms. As CNN is more suited to image processing applications, the CNN-LSTM and CNN-GRU lead to a higher \(D_{que}\) of 15-20 ms.  
The variation in total delay (\(D=D_{que}+D_{proc}\)) is illustrated in Figure 8. The variation in \(\mathcal{D}\) is dependent on the inter-arrival time of the packets. With fed-DDPG, we notice a max \(\mathcal{D}\) of 13 ms, which is approximately 40% less than the max \(\mathcal{D}\) of the RNN and CNN-GRU models.  
Figure 6: Variation in average cost function (UAV energy and latency) with number of vehicles (\(V\)).  
Figure 7: Variation in queuing delay (\(D_{que}\)) in FL scenario with time slots.
## 3 Analytical Method for Spatial-Temporal Prediction of the Extreme Pressure
### Dimensional Analysis  
The dimensional analysis provides insight into the spatial-temporal prediction of extreme pressure in a tunnel, identifies nondimensional parameters, and obtains general results. The appearance of MPP and MNP is caused by the superposition of PWSs. As the time difference between entry-induced PWS and exit-induced PWS is \(L_{tu}/v_{tr}\) (corresponds to the train passing time in the tunnel), and between nose-induced PWS and tail-induced, PWS is \(L_{tr}/v_{tr}\) (corresponds to the passing time of train body). The characteristic time of pressure wave propagation in the tunnel is \(L_{tu}/c\). The location and time of the extreme pressure in the tunnel are \(x\) and \(t\), respectively. Thus, the issue concerning their position and time of appearance involves six variables, which are \(L_{tu}\), \(L_{tr}\), \(v_{tr}\), \(c\), \(x\), and \(t\), but only two fundamental dimensions, length \(L\) and time \(T\). According to [PERSON]'s \(\pi\) theorem [50], \(6\ -2\ =4\) dimensionless products are determined, as shown in Equations (17)-(20).  
\[M=\frac{v_{tr}}{c}=\frac{\frac{L_{tu}}{c}}{\frac{L_{tu}}{v_{tr}}} \tag{17}\]  
Figure 6: Flowchart of spatial–temporal prediction of the extreme pressure in a tunnel.  
\[\varepsilon=\frac{L_{tr}}{L_{tu}} \tag{18}\] \[x^{*}=\frac{x}{L_{tu}}\] (19) \[t^{*}=\frac{t}{\frac{L_{tu}}{c}} \tag{20}\]  
The train/wave velocity ratio \(M\) is the ratio of the train speed to the local velocity of sound and represents the relative magnitude of the pressure wave propagation time \(L_{tu}/c\) and the characteristic time of the train passing through the tunnel \(L_{tu}/v_{tr}\). The train/tunnel length ratio \(\varepsilon\) is the ratio of the train length to the tunnel length. The dimensionless position of the extreme pressure \(x^{*}\) indicates the relative position of extreme pressure in the tunnel. The dimensionless time of the extreme pressure \(t^{*}\) is the ratio of the appearance time \(t\) to the characteristic time of pressure wave propagation \(L_{tu}/c\).  
Therefore, the relationship between the six variables can be given by the following:  
\[x^{*}=F(M,\varepsilon) \tag{21}\]  
\[t^{*}=F(M,\varepsilon) \tag{22}\]  
where the symbol \(F\) only represents a relationship, not a specific function. Equations (21) and (22) show that the dimensionless location and time of extreme pressures in a tunnel are related to \(M\) and \(\varepsilon\), and \(M\) and \(\varepsilon\) could be used to describe the different operating conditions. If the dimensionless quantities of \(M\) and \(\varepsilon\) are used to describe the conditions in Figure 6, then the dimensionless results would be obtained accordingly.  
In addition, the frequency of the pressure fluctuation in the post-train stage can be calculated as Equation (23), which is related to fatigue damage.  
\[f=\frac{1}{T}=\frac{c}{2L_{tu}} \tag{23}\]
the reflected echoes, the necessary information can be obtained [8; 9; 10]. It is a promising application for autonomous vehicles as it provides high-speed resolution and precision, making it superior to traditional microwave-based radars.  
Nevertheless, atmospheric conditions can adversely affect the performance of photonic radars. Factors such as scattering and absorption by various atmospheric gases and elements can lead to a diminished range resolution, impacting the radar's effectiveness in accurately gauging distances and detecting objects. Several studies have been conducted to improve the performance of photonic radars in adverse weather conditions, and industries operate in the X-band (8-12 GHz) or S-band, which offers narrow beams for tracking targets and robust immunity against atmospheric turbulences, respectively.