esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Analysis
### Performance of the Beam-Footprint Partitioning Algorithm  
In this section, we evaluate the performance of the beam-footprint partitioning algorithm. The beam positions generated by Algorithm A are assigned to time slots using Algorithm 1. If Algorithm A produces only \(MN\) beam positions, the assignment performance of Algorithm 1 may degrade in certain scenarios due to the spatial separation constraints among beams within each time slot. This can lead to a reduced number of active beams scheduled within a BH cycle, thereby lowering overall resource utilization. To address this issue, an adjustment factor \(\partial\) is introduced. Specifically, Algorithm A generates \(\partial MN\) candidate beams, and Algorithm 1 selects \(MN\) beams from this larger pool for actual scheduling. In the simulation, \(\partial=1.5\). Each LEO satellite can activate up to 4 beams simultaneously, resulting in a maximum of 24 beam positions that can be scheduled over six time slots. Based on this configuration, a total of 144 candidate beams are generated across four LEO satellites. Theoretically, the maximum number of users that can be scheduled is 432.  
Without the iterative optimization mechanism, a significant number of beams serve fewer than \(F\) users, resulting in a total of 408 users being scheduled, with a resource utilization rate of 94.4%, as illustrated in Figure 2a. After introducing iterative refinement, the number of inefficient beams is significantly reduced. Consequently, the number of scheduled users increases to 430, and the resource utilization rate improves to 99.5%, as shown in Figure 2b.
# Auditory Coding of Reaching Space
## 2 Materials and Methods
### Data Acquisition and Analysis  
The two-dimensional Cartesian coordinates of the reaching point on the digitizing tablet were measured for each trial. The tablet sampled the x and y coordinates of the stylus hit with a spatial accuracy of 0.005 mm. The collected data were stored for later analyses. The reaching error was represented by the vector from the target to the reaching position. The absolute values of the single vector coordinates describe the sizes of the absolute deviations along the three directions in space (termed 'Absolute differences'). The magnitude of the vector represents the distance between the target and the reaching position (termed 'Absolute distance'). T-tests and analyses of variance with repeated measurements were used to compute the significance of the deviations to the target point. For the post hoc test, the Bonferroni test was used. A significance criterion of \(\alpha\) = 5% was established for all results reported.
# Forward/Backward Decomposition for Dispersive Wave Propagation Measurements
## 4 Experimental Validation  
To validate the performance of the wave decomposition technique on real-world data, an experiment was conducted using a rectangular aluminum beam with clamped ends. The test rig is a beam tensioning apparatus which has previously been used in [7]. The objective of the experiment was to validate the performance of the method with respect to its ability to identify reflections in the presence of other sources of random noise.
## 4 Applications of 3D PFFT-BEM  
### Model Comparisons of the PFFT-BEM with the 3D Parabolic Equation Method  
To demonstrate the usefulness of PFFT-BEM, PFFT-BEM is compared with 3D Parabolic equation (PE) models to check the correctness and performance of different PE schemes. We first briefly review the formulation and assumptions of the 3D PE theory. Then, we present three numerical examples for comparison: ASA wedge, underwater seamount, and Gaussian canyon.
## 3 Methods
### Edge Generator  
An edge generator is required in order to establish connections and create a complete graph structure, as there are no direct edges between the newly generated nodes from the resampling module and the original nodes. The ESA-GCN incorporates the attention mechanism and GraphSAGE to predict the links between synthetic nodes. By learning the relationships and weights between nodes, the edge generator generates links between synthetic nodes within the embedding space, resulting in the construction of a balanced augmented graph. This enables the GNN to more effectively capture the structure and information between nodes, thereby enhancing classification performance in node classification tasks.  
The edge generator incorporates the attention mechanism to dynamically adjust the importance of each node. This ensures that the learning process gives more weight to minority class nodes and takes into account the interaction between nodes when computing the feature vectors. The similarity between the feature vectors of two nodes is calculated using the sigmoid function, and edges are generated accordingly to establish a complete graph structure. The calculation formula for node embedding is given in Equation (5):  
\[\rm H_{i}^{\prime}=a_{ij}Wh_{i}+\sum_{j\in N(i)}a_{ij}Wh_{j} \tag{5}\]  
In the equation, \(W\) represents the weight parameters, \(N(i)\) denotes the set of neighboring nodes for node \(i\), \(h_{i}\) represents the feature vector of node \(i\), \(h_{j}\) represents the feature vector of node \(j\), \(h_{i}^{\prime}\) represents the feature vector of node \(i\) after being computed by the attention mechanism, and \(a_{ij}\) represents the attention of node \(j\) on node \(i\). LeakyReLU is a variant of the rectified linear unit (ReLU) that introduces a small slope for negative input values instead of setting them to zero directly. The calculation formula for \(a_{ij}\) is given in Equation (6):  
\[\rm A_{ij}=\frac{exp\bigl{(}LeakyReLU\bigl{(}a^{T}\bigl{[}Wx_{i}\bigl{|}|Wx_{ j}\bigr{]}\bigr{)}\bigr{)}}{\sum_{k\in N(i)\cup\{i\}}exp\bigl{(}LeakyReLU \bigl{(}a^{T}\bigl{[}Wx_{i}\bigl{|}|Wx_{j}\bigr{]}\bigr{)}\bigr{)}} \tag{6}\]  
Equation (7) is used to compute the relational information \(\rm E_{v,u}\) between the predicted node representations of nodes \(v\) and \(u\). Here, \(S\) is a parameter matrix that captures the interaction between nodes.  
\[\rm E_{v,u}=softmax\Bigl{(}\sigma(h_{v}^{1}\mbox{-}S\mbox{-}h_{u}^{1})\Bigr{)} \tag{7}\]  
By introducing the attention mechanism, the ESA-GCN is able to consider the interactions between nodes more comprehensively. It flexibly adjusts the weights of nodes during the edge generation process, resulting in a more accurate graph structure and making it more effective in handling imbalanced data. Additionally, incorporating the attention mechanism allows for the concentrated processing of crucial information in the input data with higher weights while ignoring irrelevant information. This significantly reduces the model parameters and computational complexity.
## Appendix A Solution for a Cantilevered Beam  
Equation (5) has a general solution of [14]:  
\[\phi_{m}(x)=C_{1m}\cos\beta_{m}x+C_{2m}\sin\beta_{m}x+C_{3m}\cosh\beta_{m}x+C_{4m }\sinh\beta_{m}x \tag{11}\]  
where  
\[\beta_{m}^{4}=\frac{\rho A\omega_{m}^{2}}{EI} \tag{12}\]  
The constant coefficients \(C_{1m},C_{2m},C_{3m}\), and \(C_{4m}\) can be found from the boundary conditions.  
The frequency equation can be derived by applying the frequency determinant method to the eigenfunctions given by (11) and considering the boundary conditions for a cantilever beam with length L, which involve zero displacement and slope at the fixed end, as well as zero shear and moment at the free end.  
A nontrivial solution for coefficients \(C_{1}\) to \(C_{4}\) is obtained when the coefficient matrix is set to zero. Solving the resulting determinant yields the frequency equation.  
\[\cos(\beta L)\cosh(\beta L)=-1 \tag{13}\]  
The roots of this equation can be determined either numerically or graphically. Considering the speed of wave propagation in the material, applying \(\beta_{m}L\) to Equation (12) gives the natural frequency of vibration.  
\[\omega_{m}=(\beta_{m}L)^{2}\sqrt{\frac{EI}{\rho AL^{4}}},\quad m=1,2,\dots \tag{14}\]  
This equation provides the natural frequencies for different modes of vibration, where \(\beta_{m}\) represents the roots of the mode shape equation.  
By determining the coefficients \(C_{1}\) to \(C_{4}\) and substituting them into Equation (11), we obtain the normalized equation for the mode shapes in Equation (15).  
\[\phi_{m}(x)=\frac{1}{\sqrt{\rho LA}}\,\left[(\sin\beta_{m}x-\sinh\beta_{m}x)- \frac{(\sin\beta_{m}L+\sinh\beta_{m}L)}{(\cos\beta_{m}L+\cosh\beta_{m}L)}( \cos\beta_{m}x-\cosh\beta_{m}x)\right] \tag{15}\]  
There is no necessity to numerically solve for a large number of solutions to this equation. For larger solutions, a reliable approximation can be obtained using the following formula:  
\[\beta_{m}L\approx\frac{(2m-1)\pi}{2},\quad m>5 \tag{16}\]  
Given the presence of hyperbolic functions in Equation (13), it becomes crucial to approximate the mode shape for values of \(m\) exceeding 10 to circumvent numerical issues. An approximation can be derived by expanding the precise mode shape and presuming a large value for \(\beta_{m}L\). This results in the expression in Equation (17).  
\[\phi_{m}(x)\approx\frac{1}{\sqrt{\rho AL}}\Big{[}\cos(\beta_{m}x)-\sin(\beta_ {m}x)-e^{-\beta_{m}x}-e^{\beta_{m}x-\beta_{m}L}\sin\beta_{m}L\Big{]} \tag{17}\]
## 3 Experiment and Results
### Results of Comparative Experiment  
To evaluate the performance of the PBD-YOLO algorithm proposed in this paper, five high-performing mainstream algorithms, including Faster R-CNN, YOLOv5, RTMDet, RT-DETR, and YOLOv10, were selected for comparative experiments on the previously constructed particleboard defect dataset. The experimental metrics encompassed detection accuracy (measured by mAP\({}_{50}\) and mAP\({}_{50-95}\)), defect detection rate (measured by recall), model parameters count (measured by parameters), and inference time (measured by time). Results of the comparative experiment are presented in Table 1.  
Among all the algorithms evaluated, PBD-YOLO demonstrated the most outstanding performance. It attained the highest mAP\({}_{50}\) at 0.856 and mAP\({}_{50-95}\) at 0.609. Additionally, PBD-YOLO achieved the highest recall of 0.814, significantly reducing the false-negative rate. Meanwhile, it maintained lightweight characteristics with merely 14.1 M parameters and achieved fast inference at 3.16 ms.  
In order to give a more intuitive view of the training process of all algorithms in the comparative experiment, Figure 10(a) exhibits the mAP\({}_{50}\) and iteration numbers of the models as they were trained, Figure 10(b) portrays the bounding box (BBox) loss curves of the validation sets, and the visual detection results are depicted in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **mAP\({}_{50}\)** & **mAP\({}_{50-95}\)** & **Recall** & **Parameters** & **Time** \\ \hline Faster R-CNN & 0.702 & 0.445 & 0.541 & 84.7 M & 25.10 ms \\ YOLOv5s & 0.736 & 0.492 & 0.703 & 18.6 M & **3.08 ms** \\ RTMDet-s & 0.836 & 0.566 & 0.676 & 39.03 M & 54.20 ms \\ RT-DETR-ResNet50 & 0.793 & 0.583 & 0.776 & 86.1 M & 8.10 ms \\ YOLOv10s & 0.801 & 0.575 & 0.684 & 15.9 M & 3.15 ms \\ PBD-YOLO (ours) & **0.856** & **0.609** & **0.814** & **14.1 M** & 3.16 ms \\ \hline \hline \end{tabular} Bold text indicates that the algorithm performs best in this metric.  
\end{table}
Table 1: Results of different defect detection algorithms.  
Figure 10: Distribution of the number of defects for each category before and after augmentation.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 2 Related Work
### Deep Q Network  
The deep Q network (DQN) combines Q learning with deep neural networks, solves the instability of \(Q\) learning in high-dimensional state space through Experience Replay and target networks, and supports end-to-end decision-making in discrete action space. The key formulas and mechanisms are as follows.  
(1) Calculation of the target value of the Bellman equation.  
\[y_{j}=r_{j}+\gamma\cdot\underset{a^{\prime}}{\max}Q\big{(}s_{j+1},a^{\prime}; \theta^{-}\big{)} \tag{1}\]  
where \(\theta^{-}\) is the target network parameter and \(\gamma\) is the discount factor.  
(2) Loss function.  
\[L(\theta)=\mathbb{E}\Big{[}\big{(}y_{j}-Q\big{(}s_{j},a_{j};\theta\big{)}\big{)} ^{2}\Big{]} \tag{2}\]  
(3) Improvement mechanisms.  
Target Network: The target network parameters are synchronized with the main network in each \(C\) step to reduce the fluctuation in the target value.  
Figure 1: Reinforcement learning framework.  
Double-DQN (Double-DQN): By separating the action selection and evaluation network, the overestimation of the \(Q\) value is reduced.  
\[y_{i}=r_{i}+\gamma\cdot Q_{\theta}\big{(}s_{i+1},\mu_{\theta}(s_{i+1});\theta^{-} \big{)} \tag{3}\]  
where the main network selects the action and the target network evaluation value.  
Dueling Network: The value of \(Q\) is decomposed into state value \((V(s))\) and dominance function \(A(s,a)\).  
\[Q(s,a;\theta)=V\Big{(}s;\theta^{V}\Big{)}+\left(A\Big{(}s,a;\theta^{A}\Big{)} -\frac{1}{|A|}{\sum_{a^{\prime}}A\Big{(}s,a^{\prime};\theta^{A}\Big{)}}\right) \tag{4}\]  
where \(V\) predicts the basic value of the state and \(A\) measures the relative advantages of different actions and ensures the mean value of the advantage function is 0.  
Prioritized Experience Replay: Priority is defined based on the TD error.  
\[p_{i}=|\delta_{i}|+\varepsilon,\delta_{i}=y_{i}-Q(s_{i},a_{i};\theta) \tag{5}\]  
The sampling probability is  
\[P(i)\propto p_{i}^{\alpha},w_{i}=(P(i)\cdot N)^{-\beta} \tag{6}\]  
where \(\alpha\) controls the priority deviation and \(\beta\) balances the sampling weight to reduce the deviation.
## 5 Results
### Lidars' Performance with No Motion: Baseline Test Cases  
The baseline measurements are shown in the first row of Figure 6. In general, the mean wind speeds are consistent across most tests, with one exception in test 02. During this particular test, WindCube V1 recorded an average wind speed that is twice as high as that measured by the ZephIR 300, at both heights. However, for the majority of tests, the wind speeds recorded by both lidar systems are comparable. The TI estimated by ZephIR 300 is slightly higher than that of WindCube V1. The APEs of lidars in the baseline test cases are less than 2% for both types at 85 m, while this difference exceeds 5% for WindCube V1 at 145 m. Correlation between the two lidars of each type at two heights is more than 0.90 for all test cases. This high correlation suggests that the relative trends and patterns captured by both lidar systems is consistent. Therefore, the non-moving lidars serve as reliable sources of reference data for subsequent test cases.  
Figure 7: Absolute percentage error (APE) between moving and reference lidars (WindCube V1 in blue and ZephIR 300 in orange) at 85 m (**a**) and 145 m (**b**). Additionally, correlations between moving and reference lidars are shown at 85 m (**c**) and at 145 m (**d**). The width of the bars does not convey any specific information.
# Acoustic Negative Refraction and Planar Focusing Based on Purely Imaginary Metamaterials
## 1 Introduction  
The diffraction of light in conventional media limits the sharpness of the image by the wavelength of light. Overcoming this barrier has inspired research on negative refraction, which possesses the ability to focus all the spatial Fourier components of a source, even the evanescent spatial spectrum [1]. However, negative refraction has not readily been found in natural materials. Thus, metamaterials exhibiting anomalous values have gained considerable interest in the last few years [1; 2; 3; 4; 5; 6; 7].  
In particular, the exotic physical properties of parity-time (PT) symmetry materials have been widely studied [8; 9; 10; 11; 12; 13; 14]. PT symmetry in optics corresponds to a spatial distribution with complex refractive indices, i.e., systems that exhibit symmetric gains and losses by adjusting the refractive indices [9]. Many novel phenomena that cannot be achieved in traditional structures have been discovered. For example, PT-symmetry breaking [15; 16; 17; 18], unidirectional invisibility [19; 20; 21; 22], Bloch vibration [23; 24] and planar perfect imaging system [12; 25; 26]. It is shown that by employing a pair of PT symmetric meta-surfaces, combined with a tailored nonlocal response, negative refraction and planar focusing have been realized. In this process, the illuminated half of the structure works as a coherent perfect absorber, fully absorbing the incident waves, while the other part operates in the mode of a coherent laser, reproducing the impinging waves on both sides [12; 25].  
Similar to PT-symmetric materials, purely imaginary metamaterials (PIMs) have been proposed by introducing the gain and loss acting as new freedoms. However, unlike the spatial distribution of loss and gain in PT-symmetric systems, the loss and gain of PIM are uniformly distributed in space but with different parameters. Thus, PIM is a homogeneous medium with a real refractive index. PIMs offer interesting underlying physics to explore and therefore they have been widely studied in the field of optics [7; 27; 28; 29] and have also been introduced into the field of acoustics [30; 31]. Interestingly, the coexistence of laser and anti-laser (coherent perfect absorption (CPA)) is also possible in PIM structures [27; 30].  
Inspired by the PT symmetry counterpart, there are some attempts to study the imaging phenomena in optics [28]. However, there are few works related to acoustic imaging based on PIM structures. In this work, we explore the anomalous scattering properties in the framework of acoustic PIM through analytical derivations and full-wave simulations. All-angle negative refraction and planar focusing have been demonstrated by using a PIM slab that supports the acoustic CPA mode and another PIM slab with conjugate parameters operating in the coherent acoustic laser mode. The proposed structure provides an alternative basis for the possible applications of acoustic amplification, acoustic absorption, and acoustic perfect imaging.
# Assessing Gender Bias in Auditory-Perceptual Ratings of Tracheoesophageal Speakers
## 2 Methods
### Orientation and Listening Procedure  
As an a priori requirement of the experimental procedure, all listeners were required to participate in two listening sessions. Upon arrival for the first listening session, participant listeners were informed that the speech samples to which they would listen were abnormal voice samples where the quality of the voice was reduced from normal expectation. Each listener was then asked to listen to four TE speech samples (two males, two females) that were not part of the experimental stimuli. These four samples had been compiled into a single audio file so that each voice sample would play continuously, one after another. The purpose of this task was to familiarize and orient listeners to the unique types of voices on which they would soon be making judgements.  
This exposure task was included to reduce any potential surprise related to the unusual and abnormal voice qualities that often characterize TE speech. By doing so, we believed that potential confounds related to less favorable ratings of early samples due to the unusual acoustic nature and listener adaptation could be reduced. While the gender of each of the four exposure samples was not specified, listeners were told that these samples would include both male and female voices. Listeners were allowed to listen to these familiarization samples as many times as they desired before formally beginning the experimental rating session.  
Each listener had control over the computer mouse so that he/she could independently select and repeat the audio file as many times as desired. Listeners completed this task while seated comfortably at a personal computer (Dell, Round Rock, TX, USA), listening to the audio files via headphones (Sony MDRV-150); each listener was able to independently adjust the listening volume to a comfortable loudness level. Once the listener was ready to begin the experimental rating task, he/she was presented with a randomized playlist of the 16 TE voice samples (12 primary samples and 4 duplicates, for reliability) and a series of rating scales that were provided on paper in numerical order, 1 through 16. Two auditory-perceptual dimensions were assessed in the sessions, either \"speech acceptability\" (SA) or a dimension termed \"listener comfort\" (LC).  
In the first listening session, the gender of each sample was indicated in the margin on the rating form for each sample by the letter \"F\" for female or \"M\" for male. However, in this first listening session, the gender indicated was in actuality the opposite of the speaker's true gender. For the purposes of data analysis, this first session was identified as \"Gender Opposite\". Each listener was asked to read the definition of the feature on which he/she would be making their judgments (either SA or LC) and was then asked to systematically play the list of randomized voice samples and rate each one independent of one another in a sequential manner.
# On the Impact of Wavelength Dependency on Supercontinuum Generation in Photonic Crystal Fibers
## 4 Discussion  
In this work, we tried to establish, to the best of our knowledge, a different approach to the study of supercontinuum production in the PCF using the wavelength dependence of the dispersion parameters \(\beta(\lambda)\). As the most-relevant result, we have seen a considerable change in the distribution of the power associated with the different wavelengths.  
This effect, in our opinion, is not negligible, as can be seen in the case of the narrow pulses in Figure 8 or in the case of the wider pulses for higher powers in Figure 9. In such a wavelength-dependent effect, as the supercontinuum generation is, we considered that this result must be taken into account in order to design better and more-accurate experimental devices.  
To finish this section, some future lines of research associated with the proposed results could be proposed, e.g., from a theoretical point of view, the study could be extended to different geometries of the device or include the wavelength dependency for the rest of the parameters that appear in the generalized NLSE or, in a more-applied way, the study of the sensitivity characteristics when the PCFs are used as detectors. In this last direction, our group has already begun with some simulations.  
[PERSON] and [PERSON] designed the simulations; [PERSON] and [PERSON] developed the theoretical part. All authors contributed to the analysis and writing of the results of the work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. We would like to thank the SIGOMF-TIC176 group of the Physics Department of the University of Cordoba for their support during the research process.  
Not applicable.  
We are considering the possibility of creating a git-hub-type repository with the developed material. The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors appreciate the support of [PERSON] with the grammatical corrections of the final document.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PCF & photonic crystal fiber \\ GNLSE & generalized nonlinear Schrodinger equation \\ SPM & self-phase modulation \\ GVD & group velocity dispersion \\ S-SSFM & symmetrical split-step Fourier method \\ \end{tabular}