esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Micro UAV Charging Techniques
## 2 Related Works
The expanding demand for UAVs WPT technologies is clearly validated by their several applications, such as parcel delivery, medical assistance, traffic monitoring, power line inspection, remote sensing, forest monitoring, law enforcement, search and rescue, precision agriculture and disaster management. Far-field WPT techniques have proven stature to support these applications due to mobility, long distance, non-LoS operations and location flexibility. Generally, these technologies are useful for low-powered devices due to their limited energy efficiency. Despite these issues, several studies have reported far-field WPT techniques for UAVs [18]. In this regard, WPT systems to recharge UAVs through optical energy transfer [19] and RF WPT [20] have been proposed in the literature. In [20], the authors propose a relaying system through time switching and power splitting architecture to harvest both energy and information for UAVs. In the proposed system, the authors optimized UAV system parameters and deployment location for throughput maximization and prolonging of lifetime. While in [19], the authors used an optical beam for UAV communication and charging simultaneously. The authors claimed a high  
Figure 3: Predicted values of WPT technology in 2020 [16]. network throughput and a 25% increase in hovering time. However, the proposed system faces limited distance as UAVs must remain in close proximity to the terrestrial base station. To overcome this issue, the authors in [21] suggested terrestrial UAVs (tUAVs) with omnidirectional antennas. However, omnidirectional antennas waste energy in all directions rather than only in the intended direction of the energy receiver. Powering through laser beaming supports longer UAV flight times [22; 23]. Laser beaming can be implemented through a laser array oriented using mirrors towards an intended UAV. Moreover, a practical and cost-efficient system for UAVs has been proposed as distributed laser charging (DLC) [24] using PV cells rather than lenses so that the receiver UAVs do not solely depend on laser charging due to LoS path limitations. To overcome this issue, a hybrid approach can be used for lighter UAVs containing small batteries.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 1 Introduction
Despite these promising results, many models still lack effective multi-scale fusion strategies and rely heavily on single-type features. In light of the above challenges, we propose a multi-scale feature fusion residual attention model (MUSCA), which extends ResNet18 by integrating both depthwise separable and traditional convolutional blocks for enhanced spectral texture extraction [11]. To address the limitation of CNNs in distinguishing frequency-variant but shape-similar spectrograms, MUSCA incorporates channel and spatial attention mechanisms to emphasize relevant features [12]. Furthermore, we introduce a multi-scale feature alignment and fusion strategy to model cross-scale dependencies and improve classification robustness. The main contributions of this paper are as follows:  
1. Birdsong signals contain multidimensional information, such as frequency, timbre, loudness and duration. Extracting effective sound features from them is a challenging task. To address this problem, a multi-scale feature fusion residual attention model (MUSCA) based on spectrogram birdsong recognition is constructed. * Aiming at the problem that CNN cannot distinguish spectrograms with different frequencies but similar shapes due to translation invariance and parameter sharing, channel attention and spatial attention are added to weight the spatial information of features. * Generalization ability and robustness are key indicators for measuring model performance. In the experimental design, we validated the performance of the proposed birdsong recognition model using the publicly available bird dataset. * The processing methods of birdsong signals lead to different scales of feature maps, causing the same model to show significant differences in recognition performance on spectrogram, wavelet spectrogram and Mel spectrogram features. This paper verifies the classification performance of the proposed method on different features, including spectrogram, wavelet spectrogram and Mel spectrogram.
## 5 Stability Analysis of High Slope of Carbonaceous Mudstone Based on Numerical Simulation
### Stability Analysis of Slope After Support
#### 5.4.5 Effective Plastic Strain Analysis of Slope  
By comparing and analyzing the contour map features of Figures 20 and 21, the distribution range of the plastic zone of the slope under different working conditions was obtained. The plastic zone appears below the end of the anchor cable, but compared to the unsupported state, the depth of the potential slip surface increases significantly. The reason for this phenomenon is that the prestressed anchor cable effectively connects with the stable bedrock through its anchoring section, strengthening the stability of the weak area in the upper part of the slope and promoting the transfer of potential damage to the deep soil below the reinforcement area.  
Based on the comprehensive support effects of the two working conditions, the stability safety factor is significantly improved after support, the displacement deformation is effectively controlled, and no plastic flow occurs, indicating that the slope engineering performance has been significantly improved. This verifies the effectiveness of the anchor cable + frame beam as a support method for high cut slopes of carbonaceous mudstone.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Conditions** & **Support Method** & \
\begin{tabular}{c} **Maximum Vertical** \\ **Displacement/mm** \\ \end{tabular} \\ \hline Natural state &
\begin{tabular}{c} Before support \\ After support \\ Before support \\ After support \\ \end{tabular} & 19.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical table of maximum vertical displacement results of slope before and after support under different working conditions.  
Figure 20: Plastic strain contour map of slope after support under natural working conditions.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 4 Research Methodology
### Convolutional Neural Network (CNN)  
Since the input source of our LDVRL system is images, we adopt a CNN [30] to process and extract the important features of the images before passing them into our DRL agent. In our system, the observations at each state (five consecutive depth frames, as described in Section 4.6) are passed into the CNN to extract the features. We use three layers of convolution. The first layer contains 16 kernels of 5 \(\times\) 5, and the stride is 4. The second layer has 32 kernels of 3 \(\times\) 3, and the stride is 2. The last layer uses 64 kernels of 3 \(\times\) 3, and the stride is 1. Then, a fully connected network of 3 layers is used to extract the final representations of the input before passing them to the DQN to approximate the value for each possible action at that state. After the value function is trained, inferring the optimal policy is straightforward. The agent only needs to exploit the most profitable action at each state. The illustration of our CNN architecture can be seen in Figure 6.  
Figure 6 illustrates the architecture of the CNN used in our DRL algorithms. The input to the CNN is five consecutive depth frames, which serve as our state representation. Each frame has a resolution of 72 \(\times\) 128 pixels, as this is the image resolution returned by AirSim APIs, as explained in detail in Section 4.6. The input is then fed into three convolutional layers that detect relevant patterns in the frames. The output of these convolutional layers is then flattened and passed through three dense layers that further process the information. Finally, the output is condensed into a 1 \(\times\) 128 vector that represents the state of the system at that particular time step. This vector is then used as an input to the DRL algorithms, together with the bounding box coordinates, as shown in Figure 7, to make decisions about the UAV's actions.
## 3 Related Concepts
### Non-Linear Diffusion  
Speckle-noise phenomena and their restoration are a common and universal problem in SAR image processing. Such noises degrade the quality of the images, which leads to difficulties in feature extraction objects holding the prime significant details related to the image. Various methods and filters are available to smooth the degraded image. These despeckling techniques rely on the spread value of the pixel. There are two diffusion methods used for despeckling in SAR images: isotropic and anisotropic diffusion. In isotropic, the spread values of the pixel in the SAR image, which degrades the image quality, exercise the mean on the entire edge of the images [6; 39; 40]. In anisotropic diffusion, diffusion relies on the direction of the images. It determines the mean of the image, such as the side of an edge, object, and boundary.  
The mathematical expression of the anisotropic diffusion is as follows:  
\[\frac{\partial\mathrm{I}(\mathrm{i},\mathrm{j},\mathrm{t})}{\partial\mathrm{t }}=\mathrm{div}[\mathrm{f}(||\
abla\mathrm{I}(\mathrm{i},\mathrm{j},\mathrm{t}) ||)\
abla\mathrm{I}(\mathrm{i},\mathrm{j},\mathrm{t})] \tag{2}\]  
Figure 1: (**a**) Image decomposition by using 1-Level DWT. (**b**) Image decomposition by using 2-Level DWT.  
The [PERSON] and [PERSON] proposed two functions which can be expressed mathematically as follows [30]:  
\[\mathrm{f_{1}(x)=exp{\left[-{\left(\frac{x}{k}\right)^{2}}\right]}} \tag{3}\]  
and  
\[\mathrm{f_{2}(x)=\frac{1}{1+{\left(\frac{x}{k}\right)^{2}}}} \tag{4}\]  
where \(\mathrm{f(^{*})}\) is diffusion coefficient having exponential and inverse function and \(k\) denotes gradient magnitude threshold parameters.  
An anisotropic diffusion despeckling technique is a recursive operation. It is a highly sensitive recursion process. The time (t) as a variable is important as incorrect perception can affect the result obtained from the degraded images while analyzing, and it may still be the speckled-noise artefacts in the images. The correct despeckling methods and gradient threshold variables help to obtain higher PSNR results.
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
###### Abstract  
Massive multiple-input multiple-output (MIMO) transmission has attracted attention as a key technology for use in fifth-generation mobile communication systems. Multi-beam massive MIMO systems that apply beam selection in analog components and blind algorithms in digital components to eliminate the requirement for channel state information have been proposed as a method for reducing overhead. In this study, we developed an adaptive modulation scheme for implementing multi-beam massive MIMO and used computer simulation to compare it with digital and analog-digital hybrid beam-forming methods. The effectiveness of the proposed system was verified in a medium access control layer based on the IEEE802.11 ac and frequency division duplex-LTE representative wireless communication standards.  
massive MIMO; analog multi-beam; hybrid beam-forming; PHY layer; MAC layer Article
## 3 Shearer Positioning Technology
### The Backward Calibration of Shearer Trajectory
Therefore, the global optimization problem of shearer positioning trajectory is reformulated as solving the minimum value of the objective function \(F(e_{k})\) as expressed in (18). In this study, the genetic algorithm (GA) is employed to compute this equation. \[{e_{k}}^{*}=\operatorname*{argmin}_{e_{k}}F(e_{k}) \tag{18}\]  
The corrected heading angle is obtained via (19), which is used to obtain the shearer optimization trajectory with the dead-reckoning algorithm. \[{\varphi_{k}}^{*}=\varphi_{k}+\sum_{k=1}^{k}{e_{k}}^{*} \tag{19}\]In order to compare the performance of MFS and the GOM, an experiment was carried out as shown in Figure 15. The INS was installed at the center location of the mobile platform, and the axial encoder was connected to the wheel of mobile platform. At the same time, a Global Positioning System-Real-Time Kinematic (GPS-RTK) mobile station was installed above the INS, while the GPS-RTK base station was securely fixed on a predetermined location with known coordinates. The measurement trajectory of the base station was used to evaluate the positioning accuracy of the mobile platform. Additionally, it provided the position coordinates of the start point and end point for each cutting cycle. GPS-RTK height measurement exhibited an accuracy of less than 15 mm, while plane measurement demonstrated an accuracy of less than 8 mm. The mobile platform simulated four cutting cycles at a velocity of 0.1\(\sim\)0.2 m/s, and each cutting cycle covered a distance of about 100 m. The calculation period is set as 0.5 s. There were 2013, 2182, 2309, and 2047 sampling data during four cutting cycles, respectively. Figure 16 shows the comparison between GPS-RTK trajectory and shearer trajectories without processing, with MFS, and with GOM. It was evident that the trajectories with MFS and the GOM exhibited a higher degree of proximity to the RTK trajectory in comparison to the unprocessed trajectory. The position error in the north and east during each cutting cycle were calculated as described in Figures 17-20. For both the north and the east, the absolute values of position error with MFS first increased and then decreased. However, there was no such trend in position error with the GOM, which indicated that the GOM could restrain the heading drift well. The maximum position error during four cutting cycles with MFS and the GOM are listed in Table 1. The mean errors with MFS in the  
Figure 16: Comparison between RTK trajectory and shearer trajectory: (**a**) shearer trajectory without processing, (**b**) shearer trajectory with MFS, and (**c**) shearer trajectory with GOM. Figure 17: The position error of the first cutting cycle in the (**a**) north and (**b**) east. Figure 18: The position error of the second cutting cycle in the (**a**) north and (**b**) east.
## 4 Numerical Results and Discussion
### Loading Increment Convergence Analysis and Grid Spacing Influence Study  
In order to investigate the reliability of the present modeling, loading increment convergence analysis and grid spacing influence study are conducted. Four different grid spacing dx are studied: 1.0, 0.5, 0.25, and 0.125 (mm). For each grid spacing, four different loading increments dL are studied:  
1. For dx = 1.0, dL: 0.128, 0.064, 0.032, 0.016 (mm);
2. For dx = 0.5, dL: 0.064, 0.032, 0.016, 0.008 (mm);
3. For dx = 0.25, dL: 0.032, 0.016, 0.008, 0.004 (mm);
4. For dx = 0.125, dL: 0.016, 0.008, 0.004, 0.002 (mm).  
Firstly, loading increment convergence analysis for each grid spacing dx was conducted, as shown in Figures 7-10. It can be seen that with the decrease of loading increment dL, the PD modeling load-displacement curve converges for all grid spacing. As expected, the slope of the linear load-displacement part is not influenced by the loading increment, and the loading increment only influences the peak force value for a fixed grid spacing.  
Figure 6: PD DCB modeling flowchart.  
Figure 8: Convergence analysis of loading increment dL for grid spacing dx = 0.5 mm, PD modeling load–displacement.  
Figure 7: Convergence analysis of loading increment dL for grid spacing dx = 1.0 mm, PD modeling load–displacement.  
Then, the influence of grid spacing was studied in Figure 11. The second least loading increment, dL, was used for each grid spacing. For example, dL = 0.008 mm was used for grid spacing dx = 0.25 mm. It can be seen that with the decrease of grid spacing, the linear loading part of the PD modeling load-displacement curve converges. However, PD modeling delamination growth initial point of load-displacement curve seems sensitive to grid spacing. Similar mesh sensitivity of PD modeling can be found in the work by [PERSON] et al. (2014), [PERSON] et al. (2015), and [PERSON] et al. (2016). In the present study, in order to balance the computation reliability and efficiency, dx = 0.25 mm and dL = 0.008 mm are used in the following discussion.  
Figure 10: Convergence analysis of loading increment dL for grid spacing dx = 0.125 mm, PD modeling load–displacement.  
Figure 9: Convergence analysis of loading increment dL for grid spacing dx = 0.25 mm, PD modeling load–displacement.
# Application of the Approximate Bayesian Computation Algorithm to Gamma-Ray Spectroscopy
## 7 Discussion and Summary  
A new ABC-based RIID was developed. The results show that the ABC-based RIID is _well calibrated_ and that ABC is effective for inferring peak location, width, and area, which are key steps in peak-based RIID algorithms. It is important to recall that the simulated spectra are clearly distinct from the corresponding real spectra. Therefore, the forward model used in ABC, such as that in GADRAS [21] or that in Equation (1), has model bias that was addressed by comparing real to simulated spectra. More comparison of real to simulated spectra should be a next step. As real experiments with RIIDs become more costly, simulated data are likely to be more extensively used. Recall that already in practice, one quality check used by some RIIDs is whether simulated data agree reasonably well with the observed data; if so, there is reasonable confidence that the inferred subset is at least close to correct. However, simulation model bias is not yet considered in any existing fielded RIID to our knowledge. Future work will also adapt ABC for use with peak ratios as in [8] and will explore candidate summary statistics for ABC as in [8].  
The RIID results in [8] relied on the simulated gamma library from [31]. Similarly, the RIID results in [9] were largely based on simulated data that were used to train a convolutional neural network using moderate resolution gamma detectors. It is likely that the simulated spectra in [9; 31] are also distinct from the corresponding real spectra. However, the challenge problems in [8; 9] were not difficult because the library isotopes (isotopes) were well separated (and so were the energy peaks), as were the isotopes analyzed in this paper. Simulation quality appears to be adequate in some situations, such as that in [8; 9] and in the simple example in Section 6 summarized in Figure 10 where both groups 1 and 3 (Lu177m) are distinct from groups 2 and 4 (WgPu). Another remaining challenge is to assess to what extent RIID results on simulated data can be used to infer RIID performance on the corresponding real data [9].  
**Author Contributions:** [PERSON] analyzed the data and developed the ABC applications. [PERSON], [PERSON] and [PERSON] simulated the spectra and collected the real data. All authors contributed to planning and writing the paper. All authors have read and agreed to the published version of the manuscript.  
Figure 14: PC 2 versus PC 1 for U-235 (HEU), Cu-67, and Ga-67 for (**a**) 5 min count time without model bias and (**b**) 0.5 min count time with item-specific variation in the model bias.  
Funding:This research was funded by [Los Alamos National Laboratory] grant number [US DOE 89233218 CNA000001] and the APC was funded by [Los Alamos National Laboratory].  
Research reported in this publication was supported by the LANL Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory.  
The authors declare no conflict of interest.
## 3 Outer Loop Controller Design
### NSB Control  
With the NSB control approach introduced in [16; 17; 18], three different tasks for the escort mission with obstacle avoidance should be considered in this study, namely, the obstacle avoidance mission, a task for scattering robots around the target evenly, and a task for maintaining a platoon on the surface of a sphere/hypersphere. The latter two tasks belong to the escort mission. According to [32], the desired velocity for the escort mission with obstacle avoidance is designed as  
\[\dot{q}_{d}=\dot{q}_{1}+\big{(}I-J_{1}^{\dagger}J_{1}\big{)}[\dot{q}_{2}+(I-J_ {2}^{+}J_{2})\dot{q}_{3}] \tag{2}\]  
where \(\dot{q}_{d}=\big{[}\dot{q}_{1d}^{T}\dots\dot{q}_{md}^{T}\big{]}^{T}\in\mathbb{R }^{pn}\) and \(\dot{q}_{1}=\big{[}\dot{q}_{1,1}^{T}\dots\dot{q}_{n,1}^{T}\big{]}^{T}\in\mathbb{ R}^{pn}\) represent the desired velocity of the obstacle avoidance mission and \(\dot{q}_{2}=\big{[}\dot{q}_{1,2}^{T}\dots\dot{q}_{n,2}^{T}\big{]}^{T}\in \mathbb{R}^{pn}\) and \(\dot{q}_{3}=\big{[}\dot{q}_{1,3}^{T}\dots\dot{q}_{n,3}^{T}\big{]}^{T}\in \mathbb{R}^{pn}\) are the desired velocity vectors of the escort mission.  
The priority order depends on practical considerations (e.g., safety behaviors, such as obstacle avoidance, always have a higher priority) or on design choices in which a behavior in the case of conflict needs to be achieved [33].  
The sketch of NSB control architecture with three different tasks is depicted in Figure 2, in which the supervisor can arrange the priority of each task. The supervisor may change the priority (and weighting) of the different tasks throughout the mission if new requirements are given.  
In order to eliminate the conflicting components, the different task velocities need to project onto the null space created by the Jacobian matrices of higher prioritized tasks in NSB control architecture. As shown in Figure 3, \(\dot{q}_{2}\) needs to be projected onto the null space of \(\dot{q}_{1}\). This means that the effective element of each task is combined to construct the integrated velocity command to drive the robot [34].  
Figure 1: Block diagram of the overall control system.  
Figure 3: Velocity command composition of two tasks.  
Figure 2: Sketch of null-space-based behavioral (NSB) control with three different tasks.
## 3 Current State of Object Detection Algorithms
### Region-Based CNN
#### 3.1.3 Mask R-CNN  
Mask R-CNN is a deep learning model that extends the Faster R-CNN architecture by adding another output, the object mask. This model was introduced by [PERSON] et al. [36] in 2017 and has since become a popular model for object detection and semantic segmentation tasks.  
One of the key innovations of Mask R-CNN is the use of the RoI Align layer, which replaces the RoI Pool layer used in Faster R-CNN. The RoI Align layer helps to prevent misalignment at the pixel level, which leads to a more precise spatial distribution of the object. In Mask R-CNN, the two-stage procedure is maintained, with the same RPN being used in the first stage. In the second stage, Mask R-CNN outputs not only the class and bounding box predictions but also a binary mask for each ROI. This mask represents the spatial distribution of the object, and its pixel-to-pixel correspondence provides a natural way to extract the spatial structure of the mask. However, one of the main limitations of Mask R-CNN is its high computational cost, as it requires additional computations to generate the object masks.