esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GradCAM for Image Processing Layer Explanation  
GradCAM [33] is among the most popular algorithms for visually explaining convolutional neural networks. This is a gradient-based approach that is based on the assumption that the areas of the last convolutional layer of the network whose values vary the most as a result of stimulating the network with a signal representing an object belonging to that class have the most significant influence on the classification of an object into a particular class. In practice, GradCAM computes the average of the gradient values and uses them in linear combination with the output of the last convolutional layer. In the linear combination, only the sum's non-negative components are considered. The resulting feature relevance map is often scaled to an interval of values [0, 1] and then interpolated and superimposed on the input image. This second step is because convolutional features retain spatial information about the input signal. After overlaying the relevance map, GradCAM marks the areas of the input image that are most significant for classification into a particular class. The algorithm can be written as follows: suppose we have an input image \(I\) that can be classified into \(C\) classes. Each class is represented by a separate output from the \(y_{c}\) network. Suppose the last convolutional layer of the network consists of \(K\) filters; each filter returns a resultant image (feature set) with a resolution of \(n\times m\). We calculate the weights \(w_{k}^{c}\) for the given class \(c\) and the kth output of the last convolutional layer as follows:  
\[w_{k}^{c}=\frac{\sum_{i=1}^{n}\sum_{j=1}^{m}\frac{\partial y^{c}}{\partial A_{ ij}^{c}}}{n\cdot m} \tag{4}\]  
where \(y^{c}\) is an output of the neural network that corresponds to classification of an input \(I\) to class \(c\), \(A_{ij}^{k}\) is a feature with coordinates \((i,j)\) of the kth output of the last convolutional layer, \(k\in[1,\dots,K]\), \(c\in[1,\dots,C]\). Finally, the GradCAM map \(M^{c}\) generated for class \(c\) is computed as follows:  
\[M^{c}=Scale_{[0,1]}(max(0,\sum_{k=1}^{K}w_{k}^{c}\cdot A^{k})) \tag{5}\]  
where \(Scale_{[0,1]}\) scales input tensor values to range \([0,1]\) linearly.  
GradCAM can be directly applied to convolutional neural networks where the last convolutional layer can be indicated. In particular, GradCAM can be applied to the example Simple and Nature feature extractors shown in Figure 2.
## 4 Application of the Model
### Level 3 Evaluation: Sensitivity of the Receiver
Therefore, no comparison with the Egli model is given in Figure 6.0  
According to the received power of the receiver in free space, on actual terrain, and in the actual measurement data, the sensitivity reduction of the receiver in three cases was calculated using Equation (18), as shown in Figure 6.1  
As can be seen in Figure 9, the broken red line indicates the receiver sensitivity reduction threshold. Depending on the threshold of the EMC indicators of the system, the sensitivity reduction of the radio receiver should be less than 6 dB. The sensitivity of the receiver is reduced by more than 6 dB when the communication distance exceeds 15.5 km in real measurements. The sensitivity of the receiver is reduced by more than 6 dB when the communication distance exceeds 16.5 km on the actual terrain. The sensitivity of the receiver is reduced by more than 6 dB when the communication distance exceeds 19.1 km in free space. The error in the calculation of the receiver sensitivity reduction in free space and  
Figure 6.2 Actual power received by receiver VHF3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**D (km)** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** \\ \hline Actual received & \multirow{2}{*}{\(-123.6\)} & \multirow{2}{*}{\(-124.3\)} & \multirow{2}{*}{\(-125.1\)} & \multirow{2}{*}{\(-125.5\)} & \multirow{2}{*}{\(-126.1\)} & \multirow{2}{*}{\(-126.8\)} & \multirow{2}{*}{\(-127.2\)} & \multirow{2}{*}{\(-127.8\)} \\ power (dBm) & & & & & & & \\ \hline
**D (km)** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** \\ \hline Actual received & \multirow{2}{*}{\(-128.4\)} & \multirow{2}{*}{\(-129.1\)} & \multirow{2}{*}{\(-130.2\)} & \multirow{2}{*}{\(-130.8\)} & \multirow{2}{*}{\(-131.4\)} & \multirow{2}{*}{\(-131.8\)} & \multirow{2}{*}{\(-132.6\)} & \multirow{2}{*}{\(-133.1\)} \\ power (dBm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Actual received power at different terrain distances. via measurement is 23.23%. The error in the calculation of the receiver sensitivity reduction in the actual terrain and via measurement is 6.45%. Thus, the error was reduced by 16.78%. Therefore, the evaluation result is \(\text{IM}>0\), which indicates that a potential EMI is present and a fourth-level evaluation is required.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 1 Introduction
In the simulation part, we examine the effectiveness of the proposed methods in a noisy environment, where two Gaussian noises with different SNRs (in dB) are added to the simulated lightning signals. This paper is organized as follows. In Section 2, we discuss the background of the broadband ITF, the preprocessing of the original data, the related steps of lightning mapping estimation, and the simulation procedure of lightning data. Section 3 presents the obtained lightning mapping from the measured VHF radiation sources and also a comparison with the simulation result. This paper is concluded in Section 4.
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## 3 Results and Discussion  
### Validation of Line Scan Laser Speckle Contrast Imaging  
To validate the line scan laser speckle contrast imaging method with theoretical models, flow phantoms were studied. Figure 2 illustrates the temporal speckle contrast K of the flow phantom varies with different exposure times under different flow velocities using LS-TSCI setup with a special configuration. The galvo mirror was set at a stationary position without scanning, and temporal speckle contrasts were obtained from the continuously acquired line patterns. As shown in Figure 2, the temporal speckle contrast K values are plotted for unordered Brownian motion and different directional flow velocities. Firstly, the Brownian motion (0 mm/s) experimental data are fitted to Equation (6) (Lorentzian autocorrelation function) with \(\beta=1\). They show that the Lorentzian model is in good agreement with the experimental results, the correlation time of speckles \(\tau_{c,Brownian}\) is estimated at 1.3 ms as a fitting parameter. Then, the directional flow experimental data are fitted to Equation (7) (Combined model) with \(\tau_{c,Brownian}=1.3\) ms, \(\beta=1\). The fitting results are in good agreement with the experimental measurements. The obtained fitting parameter \(\tau_{c,endend}\) is reduced from 1.1 ms for 0.35 mm/s to 68 \(\upmu\)s for 10 mm/s. As shown in Figure 3, \(1/\tau_{c,endend}\) is linear with the flow velocity (\(r^{2}=0.9905\)). The experimental results verified that the correlation time \(\tau_{c,endend}\) is in inverse proportion to the flow velocity of the scattering particles [28].  
Figure 3: Dependence of fitting parameter \(\tau_{c,endend}\) (plotted as \(1/\tau_{c,endend}\)) obtained from the speckle contrast and flow velocities. The linear correlation coefficient is 0.9905.  
Figure 2: Measured temporal speckle contrast of flow phantom (data points) and corresponding fitting results (solid lines) for different flow velocities using LS-TSCI setup.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 4 Solution Approach  
Compared with other dynamic event update strategies, the two-stage vehicle route planning strategy (as shown in Figure 2) can dynamically adjust the delivery route while reducing the amount of calculation with higher flexibility. The problem in the initial planning stage is an NP-Hard problem. As customer size increases, the number of feasible solutions grows exponentially, making it challenging for exact algorithms to obtain optimal solutions efficiently. To solve this problem, this study chose the chimp optimization algorithm, which is simple in principle and has few parameters, to solve it. However, the traditional chimp optimization algorithm has problems with easily falling into local optimum and low convergence accuracy, and it is not directly applicable to optimizing both trucks and drones. Therefore, this study improves the chimp optimization algorithm by combining the characteristics of the collaborative truck and drone delivery problem to obtain the delivery scheme in the initial planning stage. In the dynamic planning stage, the K-means clustering method is used to cluster all customer nodes, and subsequently, the corresponding strategy is used according to the regional dynamic degree level. For specific strategies, see Section 3.2.2.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 4 Discussion
### Sensitivity to Noise
#### 4.2.1 Uniformly-Distributed Random Noise  
In this case, Noise() is a function which follows a uniform distribution from \(-1\) to \(1\). Figure 13 represents the functions \(P_{E}(\mathrm{t})\) and \(P_{C}(\mathrm{t})\) obtained using Equations (32) and (33) with different noise levels.  
Table 3 shows how adding a uniformly-distributed noise to \(P_{E}(\mathrm{t})\) and \(P_{C}(\mathrm{t})\) impacts into the calculated power depending on the method.  
The results of this analysis show some degradation of the accuracy of the result when noise increases. When the noise level is 25%, the average power obtained is 113.789 mW (which corresponds to an error of 3.5%) and 128.734 mW (which corresponds to an error of \(-9.2\%\)) using methods 1 and 2, respectively. In general, method 1 tends to underestimate the average power, whereas method 2 tends to overestimate it.  
Although method 2 has shown better accuracy with lower noise levels, method 1 has shown better robustness when noise increases. This is mainly because method 2 relies on the peak values of the filtered signals, which are more sensitive to the noise, to determine the averaged power.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Power (mW) for Different Levels of Noise**} \\ \cline{2-7}  & \multicolumn{3}{c}{**(Uniformly Distributed Noise)**} \\ \cline{2-7}  & **0\%** & **5\%** & **10\%** & **15\%** & **20\%** & **25\%** \\ \hline Reference method & 118.05 & 117.969 & 118.377 & 117.987 & 118.310 & 117.931 \\ Instantaneous power (method 1) & 115.983 & 115.814 & 115.788 & 115.216 & 114.332 & 113.789 \\ One-period average (method 2) & 116.691 & 117.355 & 119.879 & 122.161 & 126.007 & 128.734 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the results obtained with different levels of noise (uniformly distributed noise).  
Figure 13: AC component of \(P_{E}(k)\) and \(P_{C}(k)\), filtered and non-filtered, with different noise levels (uniformly distributed noise).
## 2 Materials and Methods
### Data
#### 2.2.4 Static Data  
Static data used for the analysis included land cover, elevation, soil texture, and soil depth. Land-cover data were derived from MODIS products (MCD12Q1 collection 5.0) [35] by taking the most dominant land cover in each quarter-degree grid cell. Elevation data were obtained from GTOPO30 [51]. Soil texture data and soil depth data were created using the Harmonized World Soil Database version 1.2 and ISLSCP II Ecosystem Rooting Depths (mean 95% ecosystem rooting depth in meters [52]), respectively. All data were re-gridded to quarter-degree resolution using the nearest neighbor method.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 4 Experiments
### Study Data  
To validate the accuracy and efficiency of the methods proposed in this paper, experiments were conducted on both synthetic datasets and airborne LiDAR point clouds. Due to the substantial sample requirement in deep learning, which necessitates rigorous standards for scenario diversity and dataset quality, there currently exists no publicly accessible dataset dedicated to the detection of 3D TTs. In the absence of LiDAR point cloud datasets, direct performance testing and comparison are challenging. Therefore, this paper utilizes the TTPLA dataset [6], composed of drone images capturing TTs and power lines, and employs multi-view stereo algorithms [28,29] to reconstruct the 3D point cloud of the entire scenes from the drone images. These reconstructed 3D point clouds serve as the input  
Figure 5: Illustration of a TT extracted from the LiDAR data.  
for the algorithm, allowing for a comparative test of the effectiveness and accuracy of the proposed method. The TTPLA dataset is extracted from a collection of 80 aerial videos. Each video is captured at the resolution of \(3840\times 2160\) with 30 fps. In TTPLA, TTs are classified into three distinct categories based on their lattice and pole types: \"tower-lattice\" for those constructed from steel angle sections, \"tower-hybrid\" for structures featuring a combination of steel and concrete poles, and \"tower-wooden\" for towers made entirely of wood. This study selects seven representative scenarios (illustrated in Figure 6) from the TTPLA dataset for 3D point cloud reconstruction analysis. The selected scenarios include three instances of \"tower-lattice\" (lines 1, 2, and 7 in Figure 6), two of \"tower-hybrid\" (line 3, 4 in Figure 6), and two of \"tower-wooden\" (lines 5 and 6 in Figure 6). These scenarios are primarily used to verify the object detection performance of the proposed local-global dual-path feature fusion network.  
Figure 6: Seven selected scenarios from TTPLA. All TTs are photographed from different angles.  
The airborne LiDAR data for power transmission corridors encompass three outdoor areas of 220 KV high-voltage power transmission lines, with an average spatial resolution of 0.3 m for the point cloud. Data from each area were collected by one flight using airborne LiDAR equipment, collecting LiDAR point clouds with attributes (such as RGB color), as shown in Figure 7. The primary types of ground features, in addition to TTs, include trees, buildings, roads, etc. Due to the insufficient number of TTs in the LiDAR data used for training as samples, we selected the TTPLA dataset, composed of 2D TTs, as the training dataset to train the proposed object detection network. Subsequently, the network was employed to detect TTs within the LiDAR point clouds.
## 5 Discussion
### Limitation  
1. The proposed method only considers three indicators, namely, CPU usage, memory occupancy, and the ratio of the number of links to the used resources; in the actual running environment, the calculation of system load is more complicated and it also needs to take into account the use of disks, the number of current processes, and so on. The next step of the research needs to detect a more reasonable calculation method for the system load.
2. In this paper, when calculating single-server highly concurrent task requests, only the token fill rate is adjusted adaptively, but the capacity setting of the token bucket is not discussed. The impact of token bucket capacity on task processing efficiency needs to be discussed systematically in the future.
3. Currently, the method in this paper is designed for non-priority task requests, and for multiple mixed task requests, the method in this paper needs to be further improved according to the priority of the task or QoS.
## 5 Implementation
### Parameter Selection  
The preliminary design of the proposed UAV had six equally spaced propellers on the same plane. The propellers were arranged in alternating order of their spinning direction. The propellers' counterclockwise (CCW) rotation about the Z-axis of the propeller coordinate was taken as positive rotation, whereas the clockwise rotation was assumed as negative, and the thrust generated by the propellers was directed parallel to both the airframe and propeller coordinate Z-direction. Even though it was not fully controllable, this arrangement fulfilled the minimum number of propellers required to provide actuator fault tolerance [23].  
Recalling the discussion in Section 1, actuator failure causes the loss of force and moment unbalance, which results in an incapability to maintain entire attitude and altitude control. A typical scheme for solving this situation is scarifying control of one or more DOF, usually yaw motion to control rolling and pitching motion independently [24]. Vectorizing thrust by tilting the propeller was another technique many researchers presented. The inward, sideways, or combined tilting of propellers proved to enhance the multirotor UAVs' fault tolerance and maneuverability [25; 26; 27].  
Thus, as shown in Figure 8, actuators 3-6 were established to tilt inward and outward about the axis perpendicular to the arm axis. In contrast, actuators 1 and 2 were situated to make sideways tilting about the arm axis. The additional parameter \(\beta\) controls the deviation between the lateral thrust vector produced by the vectorization of the thrust produced by tilting the propellers and the arm axis. Angle \(\beta\) results in offsetting two symmetric and opposite propellers' lateral thrust. Figure 8c,d shows the modified configuration of the preliminary design shown in Figure 8a,b. The green line represents the direction of the lateral thrust offsetting by \(\beta\), whereas the grey line represents the preliminary design arm axis.  
Figure 8: _Cont._  
The design parameters to be optimized were chosen as:  
\[p=[\beta,\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4},\alpha_{5},\alpha_{6}]^{T} \tag{22}\]  
where \(\alpha_{i}\) is the propeller's tilting angle, and \(\beta\) is the lateral thrust offsetting angle; the outward tilting angle was taken as a positive tilting angle.
###### Abstract  
The design of robust yet simple communication mechanisms, that allow the cooperation through direct interaction among robots, is an important aspect of swarm robotics systems. In this paper, we analyze how an identical continuous-time recurrent neural network (CTRNN) controller can lead to the emergence of different kinds of communications within the swarm, either abstract or situated, depending on the problem to be faced. More precisely, we address two swarm robotics tasks that require, at some extent, communication to be solved: leader selection and borderline identification. The parameters of the CTRNN are evolved using separable natural evolution strategies. It is shown that, using the same starting conditions and robots' controllers, the evolution process leads to the emergence of utterly diverging communications. Firstly, an abstract communication, in which the message carries all the information, results from evolution in the leader selection task. Alternatively, a purely situated communication, meaning that only the context is communicative, emerges when dealing with the borderline identification problem. Nonetheless, scalability and robustness properties are successfully validated.  
surm robotics; minimal communication; situated communication; abstract communication; artificial neural networks; continuos-time recurrent neural network; separable natural evolution strategies +
Footnote †: journal: Applied  
Article  
Evolution of Situated and Abstract Communication in Leader Selection and Borderline Identification Swarm Robotics Problems
# Corpsia--A Feasible Second Best Solution?
## 2 Institutional and Technical Background
### Challenges for CORSIA's Climate and Regulatory Integrity
#### 2.3.3 Permanency and Carbon Leakage  
Permanency and carbon leakage are two major challenges in ensuring effective net carbon reductions. However, non-permanency and carbon leakage are problems that are specific to certain project types, especially projects based on natural carbon storage such as reforestation. By declaring regions as protected, carbon leakage may occur if the agricultural industry simply shifts to unprotected locations. Non-permanency occurs, for example, if carbon releases are only delayed. Guaranteeing permanency of carbon storage is important, as it can only develop its climate effect if storage is guaranteed for at least 100 years, assuming that climate change will have been solved by that time [3]. Ironically, climate change challenges natural carbon storage, as it increases the risk of wildfires or drought [39]. Furthermore, human activity affects permanency if the stored biomass competes with other economic interests the economic value of which we do not yet know today [40]. Therefore, the project region can have a sizable impact on the climate integrity, especially with respect to carbon leakage of land use projects. The more attractive the land used for reforestation is for agricultural production, the higher the probability of carbon leakage by cutting unprotected forests as a substitute. Thus, climate integrity is higher in areas with degraded peatlands, for example, which are no longer in agricultural use [41]. The scale of activities matters for permanence risks and leakage as well. Large-scale forest programs, for example, are more difficult to control and predict considering the size of territories, political priority shifts, oscillations in price of agricultural commodities, and variations in government budgetary spending for forest protection [4].
## 4 Results
### IV-CatBoost Model Results
#### 4.1.2 IV-CatBoost Model Non-Landslide Point Selection  
Choosing correct and fitting positive samples is a pivotal stage in machine learning models. To ensure the precision of machine learning, this research utilized the available data on the classification of influencing factors' states (Table 2). These data were superimposed using ArcGIS 10.8, resulting in IV outcomes within the study area. The data were divided into five different susceptibility intervals: very low, low, moderate, high, and very high, using the natural breaks method. Within the very low and low categories, a random selection of the same number of non-landslide points as the existing 1133 landslide points was made. Refer to Figure 6 for details. The landslide points were assigned a value of one, indicating past or current geological landslide occurrences in the area. Conversely, non-landslide points received a value of zero, denoting regions with a low historical occurrence probability of geological landslides. Thirty percent of the data were randomly sampled from each dataset to create a validation set, which was used to evaluate the model's accuracy. The remaining 70% of the data were utilized as input for the machine learning model.  
Figure 5: Impact factor correlation heat map (PlaneC: plane curvature; ProfC: profile curvature; Rough: roughness; Litho: lithology; LandU: land use; DTF: distance to faults; DRI: distance to rivers; DTR: distance to roads; AnnualP: annual precipitation).