esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 4 Evaluation Metrics
### Iterative Error Quantification  
Iterative errors arise due to the non-linearity inherent in the governing equations, such as the Reynolds-Averaged Navier-Stokes (RANS) equations, and the iterative nature of numerical solvers [52; 56]. Non-linearities from convective terms, turbulence closure models, and deferred corrections in discretization contribute significantly to iterative errors. Additionally, the solution of linearized algebraic systems through iterative methods introduces convergence-related discrepancies. While iterative errors can be reduced with additional iterations, achieving complete convergence is often computationally prohibitive, particularly in complex turbulent flows. This necessitates the adoption of reliable methods to quantify and manage iterative errors within acceptable limits to ensure the fidelity of CFD simulations.  
Despite significant advancements in CFD, iterative errors remain an open problem, with numerous studies endeavoring to address their impact and quantification [50; 51; 52; 55; 56]. The approach to iterative error estimation employed in this study aligns with the same study utilized for the discretization error, thereby ensuring consistency and credibility within the numerical uncertainty framework. In this context, iterative error fulfills a dual role. It serves as a practical indicator of convergence, enabling engineers to assess whether a solution has stabilized by closely monitoring the error as the solution progresses. Furthermore, it provides an approximation of uncertainty, allowing for the estimation of numerical error should the simulation be terminated prematurely.  
To quantify the iterative error, the methodology proposed by [PERSON] [57] is employed. The iterative error, denoted as \(\varepsilon_{iter}\), is estimated using the formula of Equation (10).  
\[\varepsilon_{iter,j}^{n}\ \cong\ \frac{\left(\varphi_{i}^{n+1}-\varphi_{i}^{n} \ \right)}{\lambda_{i}-1} \tag{10}\]  
Note that \(n\) is the iteration number, \(\varphi_{i}\) is the solution variable, and \(\lambda_{i}\) is the principal eigenvalue of the solution matrix, approximated using Equation (11).  
\[\lambda_{i}=\frac{\left\|\varphi_{i}^{n+1}-\varphi_{i}^{n}\right\|}{\left\| \varphi_{i}^{n}-\varphi_{i}^{n-1}\right\|} \tag{11}\]  
The uncertainty in iteration convergence, \(\delta_{il\sigma}\), is then calculated as shown in Equation (12).  
\[\delta_{iter}=\frac{\left\|\varepsilon_{iter,j}^{n}\right\|}{\lambda_{\text{ ape}}-1} \tag{12}\]In this equation, \(\lambda_{ave}\) is the average value of \(\lambda_{i}\) over a reasonable number of iterations. Following recommendations in prior studies, \(\lambda_{ave}\) is calculated using 1% of the total iterations [51].
## 3 Results and Discussion
### Dicussions  
In this work, an AP-CNN OAM mode detection method was described. By adding a dual path network with an attention pyramid to the backbone ResNet18, the AP-CNN network was constructed. The effects of turbulence intensity and transmission distance on the improvement of OAM mode detection accuracy were numerically analyzed.  
We first studied the performance of the AP-CNN on multi-mode OAM mode detection with similar light intensity distributions. Comparisons of AP-CNN with ResNet18 under different degrees of atmospheric turbulence and transmission distances verified the improvement of the recognition accuracy due to the presence of a dual path structure with attention pyramid. The results reveal that the recognition accuracy increased to some extent with increasing turbulence intensity.  
The demodulator performance of the CNN with three convolution layers and two full connection layers, i.e., ResNet18, ResNet18 + Specify mapping and AP-CNN, on a multi-mode vortex beam are studied. When \(C_{n}^{2}\leq 3.0\times 10^{-13}\)m\({}^{-2/3}\), the BER using AP-CNN demodulator was lower than with the other three methods.  
In addition, OAM mode detection in single mode with a large topological charge was simulated under medium and strong turbulence intensities, i.e., \(C_{n}^{2}\) ranges from \(1.0\times 10^{-14}\) to \(5.0\times 10^{-13}\) at 2000 m and 3000 m transmission distances. A comparison between 'ResNet18'and 'ResNet18 + coherent' verified the effect of plane wave interference. The single mode recognition by AP-CNN showed an accuracy improvement of up to 5.5% compared with ResNet18 when \(C_{n}^{2}=3.0\times 10^{-13}\)m\({}^{-2/3}\) at a 2000 m transmission distance, indicating that the former detection method has strong detailed extraction and learning capabilities for dense interference fringes of single mode vortex beams with large topological charges.
###### Abstract  
With increasing demands from both military and civilian applications, ground moving-target imaging is becoming one of the important research topics for high-resolution SAR systems. However, the existing moving-target imaging methods are not suitable for high-resolution SAR because of their low parameter estimation accuracy and high computational complexity. To solve the problem, an improved ground moving-target parameter estimation and imaging method is proposed. First, the third-order phase model of the uniformly accelerated target signal is constructed, and the Hough transform and the second-order Keystone transform (SOKT) are used to correct the range cell migration into one range cell to achieve target coherent accumulation. Secondly, a delayed cross-correlation function (DCCF) is constructed to reduce the order of the range migration phase response in the slow time domain, and the coupling degree between the cross-correlation peak position and the range migration is reduced, so that the obtained DCCF has a higher gain, which ensures the accuracy of parameter estimation. Parameter estimation is simplified to peak detection by the Shift-And-Correlation (SAC) algorithm and two-dimensional Fourier transform (2D-FFT), avoiding parameter search. Compared with the existing methods, the proposed method has better focusing effect and lower computational complexity. Finally, simulation and measured data are given to verify the effectiveness of the proposed method.  
Synthetic Aperture Radar (SAR); ground moving-target imaging; Hough transform; second-order Keystone (SOKT); Shift-And-Correlation (SAC)
## 5 Results and Discussions
### Larger Production Volumes for Future Scenarios  
The studied company has a plan to increase its production capacity in a few years. Thus, it would be valuable to examine larger production volume cases. Table 9 presents three additional test cases and results for double (C8), triple (C9), and quadruple (C10) production volumes using the LVFU policy. The prescribed number of vessels and speed factors in cases C8 and C9 show a similar pattern to the current production volume: (1) only large vessels are utilized, and (2) the lowest speed is best.  
However, in case C10, the model could not find any solution to transport more than 99% of the produced products to the destinations even with a sufficient number of vessels. The current capacity of the plant-side port (i.e., three docks) is not sufficient to handle such a large transportation volume, making many vessels wait to enter the port. Thus, to quadruple the production volume, the company should invest not only in its manufacturing facilities and vessels but also in the port infrastructure to accommodate more vessels.
# Default Detection Rate-Dependent Software Reliability Model with Imperfect Debugging
## 5 Numerical Example
### Comparative Standard  
The fitting indicators are the mean square error (_MSE_), _Variance_, _RMS-PE_, _BMMRE_ and _R-square_ of the regression curve equation, and the predictive indicator is the relative error (_RE_).  
\[MSE=\sum_{i=1}^{k}\frac{\left(y_{i}-m(t_{i})\right)^{2}}{k} \tag{24}\]  
\[R-square=\frac{\sum\limits_{i=1}^{k}\left[m(t_{i})-\overline{y}\right]^{2}}{ \sum\limits_{i=1}^{k}\left[y_{i}-\overline{y}\right]^{2}},\ \ \overline{y}=\frac{1}{k}\sum\limits_{i=1}^{k}y_{i} \tag{25}\]  
\[RE=\frac{m(t_{q})-q}{q} \tag{26}\]  
\[Variance=\sqrt{\frac{\sum\limits_{i=1}^{k}\left(y_{i}-m(t_{i})- Bias\right)^{2}}{k-1}},\ \ Bias=\frac{\sum\limits_{i=1}^{k}\left[m(t_{i})-y_{i}\right]}{k} \tag{27}\]  
\[RME-PE=\sqrt{Bias^{2}+Variance^{2}} \tag{28}\]  
\[BMMRE=\frac{1}{k}\cdot\sum\limits_{i=1}^{k}\left|\frac{m(t_{i})-y_{i}}{\min(m (t_{i}),y_{i})}\right| \tag{29}\]  
where \(y_{i}\) represents the number of cumulative failures at \(t_{i}\), \(m(t_{i})\) represents the estimated values at \(t_{i}\), and \(k\) represents the number of failure data samples. Obviously, the smaller the values of _MSE_, _Variance_, _RMS-PE_ and _BMMRE_ are, the closer the _R-square_ value is to 1, the better the fitting effect. The more _RE_ approaches 0, the better the model predicts.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 5 Experimental Validation and Results Comparisons
### The Wireless Power Transfer Device  
Figure 8 depicts the tested coupled resonator; the device is mounted over a designed set-up facing the two inductors through a controlled variable air gap. The main constitutive characteristics of the device are:  
* Emitter side: 28 turns 0.1 mm of insulated copper wire wound over an ACME-H5 core material.
* Receiver side: 53 turns 0.1 mm of insulated copper wire wound over an ACME-D28 core material.  
In order to provide a detailed description of the device, the most significant and directly measurable lumped parameters are provided in Table 1.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Deep Learning-Based Methods  
In recent years, deep learning has become the core driving force in the field of computer vision. Compared with traditional methods, deep learning mostly uses the powerful representational learning ability of deep neural networks to automatically extract high-level abstract features from raw data, which can effectively distinguish normal behaviors from abnormal behaviors. According to the network structure and flow processing method, deep learning abnormal behavior recognition methods are classified into the following categories: abnormal behavior recognition methods based on Convolutional Neural Networks, based on autoencoders, based on generative adversarial networks, based on Long Short-Term Memory Networks, and based on self-attention mechanisms.
## 2 Material and Methods
### Model Description
#### 2.1.4 Turbulence Closures in Delft3D  
Delft3D provides three possible models of turbulence closures. The algebraic closure is a zero-equation model and assumes a logarithmic velocity profile. This leads to a linear relation between the turbulent kinetic energy at the bed and the turbulent kinetic energy at the free surface:  
\[k=\frac{1}{\sqrt{c_{\mu}^{0}}}\left((u_{b}^{*})^{2}\left(1-\frac{z+H}{H}\right) +(u_{s}^{*})^{2}\left(\frac{z+H}{H}\right)\right), \tag{25}\]  
where \(H=h(x,y)+\zeta(x,y,t)\) is the total depth; and \(c_{\mu}^{0}\) is the stability coefficient which was discussed earlier and is assumed equal to 0.09. \(u_{b}^{*}\) and \(u_{s}^{*}\) are the friction velocities at the bottom and at the free surface respectively. In this case the vertical eddy viscosity \(\
u_{tv}\) is defined as follows:  
\[\
u_{tv}=(c_{\mu}^{0})^{1/4}L\sqrt{k}, \tag{26}\]  
where \(L\) the mixing length defined by [PERSON] [28] as follows  
\[L=\kappa(z+H)\sqrt{1-\frac{z+H}{H}}. \tag{27}\]  
Then, Delft3D implements the \(k-L\) closure that is a one-equation model, based on the same definition of \(L\) (Equation (27)) and on the transport equation for the turbulent kinetic energy \(k\) (Equation (12)). The third model implemented is the \(k-\epsilon\) closure. This is the usual two-equation model [29] based on the solution of the transport equation for the turbulent kinetic energy \(k\) and of the transport equation of the dissipation rate \(\epsilon\)  
\[\frac{\partial k}{\partial t}+U_{i}\frac{\partial k}{\partial x_{i}}=\frac{ \partial}{\partial z}\left(\frac{\
u_{tv}}{\sigma_{k}}\frac{\partial k}{ \partial z}\right)+P+B-\epsilon \tag{28}\]  
\[\frac{\partial\epsilon}{\partial t}+U_{i}\frac{\partial\epsilon}{\partial x_ {i}}=\frac{\partial}{\partial z}\left(\frac{\
u_{tv}}{\sigma_{e}}\frac{ \partial\epsilon}{\partial z}\right)+\frac{\epsilon}{k}\left(c_{1}P+c_{3}B-c _{2}\epsilon\right). \tag{29}\]  
In this case the vertical eddy viscosity \(\
u_{tv}\) is defined as follows:  
\[\
u_{tv}=c_{\mu}^{0}\frac{k^{2}}{\epsilon}. \tag{30}\]  
The boundary conditions for \(k\) and \(\epsilon\) are applied in Dirichlet form assuming that the hypothesis of equilibrium \(P=\epsilon\) is valid, for which [30]:  
\[k_{b}=\frac{{u_{b}^{*}}^{2}}{\sqrt{c_{\mu}^{0}}},\ \ \ \ k_{s}=\frac{{u_{s}^{*}}^{2}}{ \sqrt{c_{\mu}^{0}}},\ \ \ \ \epsilon_{b}=\frac{{u_{b}^{*}}^{3}}{kz_{ab}},\ \ \ \ \epsilon_{s}=\frac{{u_{s}^{*}}^{3}}{ \frac{1}{2}k\Delta z_{s}}. \tag{31}\]
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 5 Performance Evaluation
### Compared Algorithms
#### 5.2.3 Two Extended DASF Algorithms  
DASF cannot be directly applied to our problem, since nodes must be randomly deployed following a uniform distribution in a circular pattern under DASF. Therefore, we extended the idea of DASF to find the maximum duty cycle interval for the considered problem. In those algorithms, nodes randomly wake up once in a duty cycle interval, and transmitters select the first awake node among the PFCs for forwarding.  
First, the one-hop delay distribution of each group is obtained, as discussed in Section 4.2. Then, one representative one-hop delay distribution is determined using the one-hop delay distributions of the groups, and it is assumed that all groups have the same distribution (i.e., the representative one-hop delay distribution) to make them independently and identically distributed. Now, the E2E delay is the sum of the representative distributions and is approximated using the classical central limit theorem.  
We considered two variants to approximate the E2E delay distribution (specifically, to determine the representative one-hop distribution) as follows.  
* _Sum of representative one-hop delay using maximum mean (SROD-MM)_: For the representative distribution, we selected the one-hop delay distribution of a group that has the maximum mean value among the existing one-hop delay distributions. For example, with the Soda node deployment, the representative distribution chosen by SROD-MM is the one-hop delay distribution of group 4.
* _Sum of representative one-hop delay estimated by weighted average (SROD-WA)_: Unlike SROD-MM, instead of selecting the representative distribution from existing distributions, this method estimates a new distribution that can represent all distributions of the groups. This new distribution is determined as the weighted sum of the one-hop delays using a mixture model [37], in which weights are calculated according to the groups' EPT. Let \(v_{j}\) denote the EPT of the nodes in group \(j\). Thus, \(v_{j}=\sum_{k=1}^{N_{j}}v_{j}^{k}\). Let \(M\) denote the representative distribution, expressed as follows: \[M=\sum_{k=2}^{g}\pi_{k}f(\mathrm{h}_{k})\] (26) where \(\pi_{k}=\dfrac{v_{k}}{\sum_{k=2}^{g}v_{k}}\) and \(\sum_{k=2}^{g}\pi_{k}=1\).  
The mean and variance of mixture distribution \(M\) are calculated as follows:  
\[\begin{split}\mu_{M}&=\sum_{k=2}^{g}\pi_{k}\mu_{k} \\ \sigma_{M}^{2}&=\sum_{k=2}^{g}\pi_{k}(\mu_{k}^{2}+ \sigma_{k}^{2})-\mu_{M}^{2}\end{split} \tag{27}\]
## 4 Discussion
### Fine-Label Classification  
Across all models, it proved difficult to make a fine-label classification of winter crops based on their temporal profile alone (Figure 9). The fine-label classification resulted in confusion between different groups, notably between the grass group and the mixtures, which was expected because mixtures can also be a mix of two grass-like catch crops. The same can be said about confusion between leafy species and mixtures, as mixtures can consist of multiple leafy species. Theoretically, mixtures between grass-like and leafy groups could result in a unique canopy structure and, thus, a distinguishing feature from the other catch crop groups. However, in reality, competition between species makes the proportion of a single catch crop in the canopy unpredictable, resulting in canopy heterogeneity [58]. Factors influencing the canopy composition are management, soil type,and weather conditions rather than the ratio in the seed mix. The heterogeneity of the mixture group limits the separability of the catch crop groups.  
Our results are similar to those found by [3], who also had difficulty distinguishing between catch crop species based on a time series-based classification with RF. Further, [59] similarly performed a time series-based classification with RF to distinguish two cover crop species: cereal rye and red clover. In particular, they noted that rye was often misclassified as fallow, which they attributed to (1) the highly variable biomass production depending on the planting date, causing fields with low biomass to resemble fallow fields with weeds, and (2) the interseeding of rye with crop residue, resulting in fields with low biomass production to appear similar to fields with maize stubble. These observations of high biomass variability and interseeding with crop residue were also noted in our fieldwork for the grass group (including rye), which also resulted in variability in spectral signals within this group for a single winter cropping year (Appendix C). The high variability within the grass group could, therefore, explain the high confusion of this group with the other classes.
## 4 Key Challenges and Development Trends
### Future Prospects  
Future prospects in regard to BCI-based language decoding cover multiple dimensions of advancement, including technological innovation, application expansion, and systematic implementation frameworks.  
The field of BCI-based language decoding stands at a pivotal juncture in terms of its technological evolution, poised for significant advancement through emerging technologies and expanding applications. Recent innovations in neural signal processing have demonstrated remarkable progress, particularly through the integration of triboelectric sensors with deep learning algorithms, substantially enhancing decoding precision and reliability [142]. The incorporation of large pre-trained language models presents a promising direction for improving contextual comprehension and system adaptability in regard to language interpretation tasks [143].  
The expansion of application domains represents another significant development trajectory. Advanced implementations, including lip-reading technologies and multimodal decoding interfaces, are establishing new paradigms in assistive technologies, human-computer interactions, and educational applications [142; 144]. This diversification of applications underscores the versatility of language processing systems across multiple sectors, from clinical interventions to interactive entertainment platforms.  
The transition from research protocols to commercial implementation necessitates scalable frameworks and standardized methodologies. Recent comprehensive surveys of BCI technologies emphasize the importance of establishing robust standardization protocols and scalable architectural designs to facilitate this transition [15]. Furthermore, the optimization of production processes and economic viability analysis remain crucial considerations for successful commercialization [128].  
These technological trajectories and implementation strategies indicate a promising future for BCI-based language decoding systems, suggesting potential breakthroughs in human-machine communication and societal applications. The convergence of advanced signal processing, expanding use cases, and standardized deployment frameworks, positions this field in readiness to have a significant impact in regard to enhancing human communication capabilities.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 3 Results and Discussion
### Evaluation Metrics  
To conduct a comprehensive evaluation, three widely used metrics are adopted. The Intersection over Union (IoU) is used to conduct a pixel-level evaluation. Detection Rate (DR) and False Alarm (FA) are computed to describe the location ability of the method.  
Intersection over Union (IoU): The IoU computes the ratio of the intersection (A\({}_{\text{Inter}}\)) and union area (A\({}_{\text{All}}\)) between the predictions and labels. It evaluates the spatial detection performance of the model, which is defined as follows.  
\[IoU=\frac{A_{Inter}}{A_{All}} \tag{8}\]  
\begin{table}
\begin{tabular}{c|c c c c} \hline \hline \multicolumn{1}{c}{**Image Examples**} & **Resolution** & **SNR** & **Frame Number** & **Scene Description** \\ \hline Dataset 1 & 640 \(\times\) 512 & 1.06 & 1500 & Sky background \\ \hline Dataset 2 & 640 \(\times\) 512 & 1.75 & 1000 & Asphalt road and grassy background \\ \hline Dataset 3 & 640 \(\times\) 512 & 2.09 & 4000 & Buildings Background \\ \hline Dataset 4 & 256 \(\times\) 256 & 5.45 & 3000 & Ground background \\ \hline Dataset 5 & 256 \(\times\) 256 & 3.42 & 750 & Field background \\ \hline Dataset 6 & 256 \(\times\) 256 & 2.20 & 500 & Vegetation background \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details about 6 representative datasets.  
Detection Rate (DR): DR computes the ratio of the correctly predicted target (T\({}_{\text{Correct}}\)) number over all target numbers (T\({}_{\text{All}}\)), and it is defined as follows.  
\[DR=\frac{T_{Correct}}{T_{All}} \tag{9}\]  
when the prediction is higher than the IoU threshold, we consider it as a correct prediction. The IoU threshold is set as 0.5 in this paper.  
False-Alarm Rate (FA): FA evaluates the false alarm rate of the detection. It computes the ratio of false pixels detected (P\({}_{\text{False}}\)) over total pixels (P\({}_{\text{All}}\)) in the image, and it is defined as follows:  
\[FA=\frac{P_{False}}{P_{All}} \tag{10}\]