esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 1 Introduction
Meanwhile, the ResTA backbone, while adaptively correlating the global spatial features of spectra, also contributes to the Transformer's capture of local contextual information, achieving lightweight transformation of the Transformer. Finally, by utilizing the Hungarian algorithm [11], we transform signal detection into a set prediction problem, eliminating the need for reference box setting based on prior knowledge and the post-processing step of NMS. TATR is capable of directly and parallelly outputting the coordinates and signal types of predicted boxes, without the need for expert feature information and manual threshold setting. In conclusion, this paper makes the following key contributions:  
1. For signal detection challenges under restricted conditions with incomplete phase information. In contrast to traditional detection methods employing 1D signal sequences as inputs, we transform the 1D sequences into 2D spectrogram images, providing a visual representation of the distinctions between the amplitude envelopes of telemetry signal spectra and background signals. This conversion shifts the frequency prediction problem based on sequences into an object detection problem based on images. 2. We design a ResTA backbone based on residual structure and triplet attention, which can correlate the features of the channel, frequency, and amplitude dimensions within the spectrogram. ResTA enhances the capability of extracting spectral features almost without introducing additional parameters. 3. We propose a novel signal detection model TATR based on ResTA and Transformer. TATR combines the global attention capability of ResTA and the local attention mechanism of self-attention in the Transformer to capture both global and local features of the spectrogram. Furthermore, it reduces the number of parameters in the Transformer model while adaptively selecting optimal spectral features of TT&C signals. * Setting fixed anchor points in advance is not suitable for the dynamic nature of TT&C signals' electromagnetic environments. Therefore, we employ bipartite graph matching in the detection phase, eliminating the necessity for setting anchor points based on prior knowledge, which converts the signal detection problem into a set prediction problem, and the Hungarian algorithm is applied to achieve anchor-free signal detection.
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
###### Abstract  
Numerical study on flow separation control is conducted for a stalled airfoil with steady-blowing jet. Stall conditions relevant to a rotorcraft are of interest here. Both static and dynamic stalls are simulated with solving compressible Reynolds-averaged Navier-Stokes equations. It is expected that a jet flow, if it is applied properly, provides additional momentum in the boundary layer which is susceptible to flow separation at high angles of attack. The jet angle can influence on the augmentation of the flow momentum in the boundary layer which helps to delay or suppress the stall. Two distinct jet angles are selected to investigate the impact of the jet angle on the control authority. A tangential jet with a shallow jet angle to the surface is able to provide the additional momentum to the flow, whereas a chord-normal jet with a large jet angle simply averts the external flow. The tangential jet reduces the shape factor of the boundary layer, lowering the susceptibility to the flow separation and delaying both the static and dynamic stalls.  
Keywords:flow separation control; steady-blowing jet; jet angle; static stall; dynamic stall +
Footnote †: journal: Applied Physics Letters
# Dynamic Routings in Satellite Networks: An Overview
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GEO & Geosynchronous earth orbit \\ SIN & Space information networks \\ LEO & Low-Earth orbit \\ STAG & Storage time aggregated graph \\ MEO & Medium-Earth orbit DTN \\ DTN & Disruption-tolerant networks \\ QoS & Quality of services \\ TNM & Time-domain grid model \\ TLR & Traffic light-based intelligent routing \\ SSNs & Small satellite networks \\ IP & Internet protocol \\ DRLR & Deep-reinforcement-learning-based routing algorithm \\ VANTs & Vehicular and ad hoc networks \\ FRA & Fast response anti-jamming algorithm \\ UAVs & Uncrewed aerial vehicles \\ DRSA & Dynamic routing algorithms \\ LZDR & Localized zone distributed routing \\ GCR & Contact graph routing \\ LCPR & Low-complexity probability routing algorithm \\ ERRA & Extended range routing algorithm \\ DRA & Driven routing algorithm \\ DRA & Distributed routing algorithm \\ SGGM & Satellite group and group management \\ PAR & Priority-based adjustable routing \\ DTN & Disruption-ant network \\ E-PAR & Enhanced Priority-based adjustable routing \\ DDRA & Data-driven routing algorithm \\ VN & Virtual node \\ DVTR & Dynamic virtual topology routing \\ VN-HO & Virtual node handover operating \\ FHRP & Footprint handover rerouting protocol \\ MSVN & Multi-state virtual network \\ LRES & Inter-satellite link congestion \\ DDRA & Dynamic detection routing algorithm \\ ISLs & Inter-satellite links \\ LCRA & Low complexity routing algorithm \\ SAGIN & Space-air-ground integrated network \\ DSP & [PERSON]'s shortest path \\ IM & Immersive media \\ DODR & Disruption-resistant on-demand routing \\ SCN & Service customized network \\ SAG-IM & SAGIN architecture for IM \\ LAOR & Location-assisted on-demand routing \\ AODV & Ad hoc on-demand distance vector \\ SAGSIN & Space-air-ground-sea integrated network \\ LDPC & Low-density parity check \\ \end{tabular}
## 3 Results and Discussion
### 3D Model  
Figure 26 displays the \(C_{L}\) of the 3D models compared to the reference 2D model. The 3D model without a wingtip shows good agreement with the experimental data and the 2D model. It is similar as the wing spans the entire domain. The model with a wingtip shows a significantly lower \(C_{L}\), as was predicted from wing theory. Figure 27a illustrates the velocity magnitude isosurface of the NACA 0012 with a wingtip, where the air is flowing in the \(+x\) direction. The same view without the isosurface is depicted in Figure 27b to show the position of the wing. At the right bottom of the image is the side of the airfoil with the wingtip. As can be seen, the wingtip has a substantially different profile compared to the side ending in a boundary. The velocity magnitude decreases towards the wingtip. Figure 27c shows the velocity magnitude isosurface viewed from the front of the wing and Figure 27d shows the front zoomed in on the wingtip. Here, it can be well seen how a trail develops behind the wingtip.  
Figure 23: Mach number profile.  
Figure 24: Inverted temperature profile.  
The difference in lift can be further explained by looking at the dynamic pressure profile around the airfoil. Figures 28 and 29 depict slices at two different positions along the span of the wing. First looking at the slices in the middle of the wing in Figures 28a and 29a, the pressure delta between the top and the bottom of the airfoil is lower with a wingtip. Next looking at Figures 28b and 29b, the wingtip has a significantly lower pressure delta. This pressure difference is generating lift, consequently the \(C_{L}\) is lower for the model with wingtip.  
Figure 30 depicts a \(yz\) plane pressure slice, it is positioned 0.15 chord lengths along the wing from the leading edge. On the left, the wing ends at the boundary of the domain, and on the right is the wingtip. The pressure on top increases towards the wingtip, and does so more rapidly close to the wingtip. The air can flow around the wingtip from the high  
Figure 26: Lift coefficient of the 3D models.  
Figure 25: Mach number profiles of the modified airfoil with a non-isothermal flow. The cut line starts at the thickest point at the top and bottom of the airfoil, extending vertically for 100 m.  
pressure region under the wing to the low pressure region on top. This flow around the wingtip is what creates wingtip vortices. As the fully bounded wing does not allow flow around the wing, it does not exhibit these vortices. Figure 31 displays the \(yz\) slice for the model without a wingtip. The air is unable to flow around the wingtip, and as a result, the pressure is constant along the \(y\)-axis across the wing.  
Figure 28: Pressure slices of the 3D NACA 0012 without a wingtip (\(xz\) plane).  
Figure 27: Velocity magnitude isosurface of NACA 0012 with a wingtip. The air is flowing in the \(+x\) direction.  
Figure 29: Pressure slices of the 3D NACA 0012 with a wingtip (\(xz\) plane).
## 2 Linear Dispersion Code  
The linear discrete code was first proposed by [34; 35; 36], which maximizes the mutual information of transceiver ends. The LDC encoder uses a linear combination of a series of base matrices to construct space-time codewords [37; 38]. Supposing that \(\{S_{n}\}_{n=0}^{N-1}\) is a set of the scalar symbols to be sent selected from a specific complex constellation set \(\mathbb{C}\), \(\{\mathbf{M}_{n}\}_{n=0}^{N-1}\) is a \(M\times T\) base matrix codeword, and the base matrix set is stored at both ends of the transceiver. In it, \(M\) is an actual number of transmitting antennae, and \(T\) is the duration time of the codeword. Supposing that the mean and variance of \(\mathbf{S}_{n}\) satisfy: \(\varepsilon\{S_{n}\}=0\), \(\varepsilon\{\left|S_{n}\right|^{2}\}=1\), and the formula \(\varepsilon\{\cdot\}\) is the expected value. Then, the power limitation condition should be met by the basis matrices:  
\[tr\{M_{n}M_{n}^{H}\}=\frac{T}{N}\;n=0,\;1,\;\ldots,\;N-1 \tag{1}\]  
The corresponding transmitter code corresponding to \(\{s_{n}\}_{n=0}^{N-1}\) can be constructed by a linear combination of the base matrix and \(s_{n}\) weighting coefficients.  
\[\mathbf{S}(s_{0},s_{1},\cdots,s_{N-1})=\sum\limits_{n=0}^{N-1}[\mathbf{M}_{n} s_{n}]. \tag{2}\]  
As for the MIMO system of \(M\) transmitting antennae \(M_{r}\) receiving antennae [39]. After matched filtering and symbol level sampling, the received signal matrix in the receiving antenna array is:  
\[\begin{array}{rcl}\mathbf{Y}&=&\sqrt{E_{s}}\mathbf{H}_{s}\mathbf{S}(s_{0},s_ {1},\cdots,s_{N-1})+\mathbf{V}\\ &&=&\sqrt{E_{s}}\mathbf{H}_{s}\sum\limits_{n=0}^{N-1}\left(\mathbf{M}_{n}s_{n} \right)+\mathbf{V}\end{array} \tag{3}\]  
In it, the received antenna signal matrix in Formula (1) is \(Y\), \(\mathbf{H}_{s}\) is the MIMO channel coefficient matrix [40; 41], and the complex AWGN (additive white Gaussian noise) \(M_{r}\times T\) matrix is \(\mathbf{V}\), in which all elements are not dependent and identically distributed complex Gaussian random variables in a complex domain with a distribution is \(CN(0,N_{0})\). \(E_{s}\) is the total power transmitted on the \(\mathbf{M}\) transmitting antenna. The SNR (signal to noise ratio) of those receiving antennae is written by \(SNR:=E_{s}/N_{0}\). At this point, the spectrum efficiency of the system is defined as:  
\[R=\frac{log_{2}|\mathbb{C}|\cdot N}{T}\;b/s/Hz. \tag{4}\]In above, \(|\mathbb{C}|\) indicates the potential of the constellation set \(\mathbb{C}\), which is the number of all possible constellations [42; 43].
## 1 Introduction
In [23], recursive sub-image histogram equalization (RSIHE) chooses to separate the image's histograms based on gray level with cumulative probability density equal to 0.5. This method yields better image compensation and provides improving image visual quality. These two recursive methods have improved results compared with previous methods. The mean brightness of the output was similar to that of the input in RMSHE and RSIHE, but the equalization effect was reduced. In some applications of medical image analysis, local details may be more important than global contrast. For these applications, the global histogram equalization method is inadequate, because it cannot adapt to the local brightness features of the input image. To overcome this drawback, some local histogram equalization methods have been developed. In [24], the dynamic histogram equalization (DHE) technique separates the image's histograms based on local minima. It assigns specific gray level ranges for sub-histograms before equalizing them separately. These sub-histograms further go through a repartitioning test to ensure the absence of any dominating portions. In [25], the brightness-preserving dynamic histogram equalization (BPDHE) method can produce an output image with the mean intensity almost equal to the mean intensity of the input. Therefore, it fulfills the requirement of preserving the mean brightness of the image. The RMSHE, RSIHE and BPDHE methods divide the input image's histograms into more than two sub-histograms. Most of these methods are good at preserving the mean brightness, but not much enhancement could be obtained from most of these methods. Furthermore, these methods require more computational power in order to select the separating point properly [22; 23; 24; 25; 26]. In this paper, a new multi-histogram equalization technique is proposed to enhance the contrast on palm bone X-ray radiographs. The remaining sections of this paper are organized as follows. The proposed multi-histogram equalization scheme is described in Section 2. The experiment results and discussion are represented in Section 3. Finally, the conclusion is provided in Section 4.
# A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning
## 3 Preliminaries
### Multitask Learning  
**Definition 1** (Multitask Learning).: _Given \(N\) learning tasks \(\{\mathcal{T}_{i}\}_{i=1}^{n}\), where all the tasks or a subset of them are related, multitask learning aims to learn the \(n\) tasks together to improve the learning of a model for each task \(\mathcal{T}_{i}\) by using the knowledge contained in all or some of the other tasks._  
Based on the definition of MTL, we take supervised learning tasks as an example; then, a task \(\mathcal{T}_{i}\) is usually accompanied by a training dataset \(\mathcal{D}_{i}\) consisting of \(n_{i}\) training samples, i.e., \(\mathcal{D}_{i}=\{\mathbf{x}_{i}^{i},y_{j}^{i}\}_{j=1}^{n_{i}}\), where \(\mathbf{x}_{j}^{i}\in\mathbb{R}^{d_{i}}\) is the \(j\)th training instance in \(\mathcal{T}_{i}\) and \(y_{j}^{i}\) is its label. We denote by \(\mathbf{X}^{i}\) the training data matrix for \(\mathcal{T}_{i}\), i.e., \(\mathbf{X}^{i}=(\mathbf{x}_{1}^{i},\ldots,\mathbf{x}_{n}^{i})\).  
According to the definition of MTL, there are three problems to be addressed: _when to share, what to share,_ and _how to share._ The _when to share_ issue is to make choices between singlettask and multitask models for a multitask problem. Currently, such a decision is made by human experts, and there are few works to study it. _What to share_ needs to determine the form through which knowledge sharing among all the tasks could occur. Existing MTL studies mainly focus on feature-based and parameter-based methods, and only a few works belong to the instance-based method. After establishing _what to share_, the _how to share_ aspect delineates the specific strategies for knowledge sharing among tasks. In feature-based MTL, the primary method is the feature learning approach, which focuses on learning a common feature representation for multiple tasks using shallow or deep models.
## 3 Methods  
Our simulations average over sets of 5000 pulses. We calculate the appropriate measured trace for each pulse using Equation (6) for SPIDER and Equation (8) for SHG FROG. To create the double pulses, we use flat-phase Gaussian pulses with a temporal full width at half maximum (FWHM) of 150 fs. The temporal grid comprises 4096 points with a spacing of 20 fs. The simulation's resulting spectral resolution is 0.077 rad/ps. For the SPIDER measurement, the spectral shear was chosen to be 12 frequency steps (0.9 rad/ps) and the delay was chosen to be 4 ps. The temporal support is 6.8 ps for a spectral shear of 0.9 rad/ps. The average separation of the double pulses in the pulse trains varies somewhat between the different cases considered, but is approximately 700 fs overall. Because the average pulse separation (0.7 ps) was much smaller than the temporal range (6.8 ps), our calculations suggest that there should be little fringe cancellation and good fringe visibility in the simulated SPIDER measurements.  
We cropped the FROG traces and sampled them to reduce the trace size from 4096 \(\times\) 4096 to 512 \(\times\) 512. This trace size is still fairly large compared to experimentally common sizes, so the resulting G errors (rms differences or FROG errors), normalized by the number of points, are smaller than those usually observed. FROG errors for experimental data should usually be less than 0.01, and correct retrievals of noiseless simulated traces should have errors much lower than 0.01.  
For the first three cases, we use double pulses with equal energy. We consider double pulses in which only the relative phase varies and a set of pulses in which both the relative phase and the separation vary. For each of these cases, we also generate a plot of the averaged intensity autocorrelation and the average spectrum. In addition, we compute a polar plot of the temporal pulse separation as a function of relative phase between the pulses.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 2 Traditional Method of In-Motion Coarse Alignment and Related Work
### Related Work  
To calculate the matrix \(C_{b(0)}^{n(0)}\), ref. [18] proposes an algorithm using vector observation. Using the traditional vector observation method, we can acquire the measurement vectors and the reference vectors based on the velocity measurement. The specific force equation under the navigation frame is given by [18]:  
\[\dot{v}^{n}=C_{b}^{n}\dot{v}^{b}-(2\omega_{ie}^{n}+\omega_{en}^{n})\times v^{ n}+g^{n} \tag{4}\]  
Combining Equations (1)-(3), the following equation can be obtained:  
\[C_{b(0)}^{n(0)}C_{b(t)}^{b(0)}\dot{v}^{b}=C_{n(t)}^{n(0)}\dot{v}^{n}+C_{n(t)}^ {n(0)}(2\omega_{ie}^{n}+\omega_{en}^{n})\times v^{n}-C_{n(t)}^{n(0)}g^{n} \tag{5}\]Then, the reference vectors and the measurement vectors can be acquired, and the initial alignment problem is transformed into an attitude determination problem.  
\[C_{b(0)}^{n(0)}\alpha_{v}=\beta_{v} \tag{6}\]  
where  
\[\alpha_{v}=\int_{0}^{t}C_{b(\tau)}^{b(0)}\dot{\rho}d\tau \tag{7}\]  
\[\beta_{v}=C_{n(t)}^{n(0)}v^{n}-v^{n}(0)+\int_{0}^{t}C_{n(\tau)}^{n(0)}\omega_{ le}^{n}\times v^{n}d\tau-\int_{0}^{t}C_{n(\tau)}^{n(0)}\theta^{n}d\tau \tag{8}\]  
From Equation (8), it can be seen that the measurement vector \(\beta_{v}\) is calculated based on the ground velocity \(v^{n}\); this requires that the GPS receiver can output the ground velocity. This construction method for the observation vector is the method reported in many current alignment studies [12; 13; 14]. However, some GPS receivers can only output the position of the vehicle, and the traditional construction method of vector observation is not suitable for in-motion coarse alignment in this situation. Furthermore, the noise contained in the GPS and inertial sensor measurements inevitably affects the accuracy of observation. Thus, it is necessary to change the construction method of vector observation and expand the application scope of the traditional OBA algorithms.
# Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model
## 3 Results and Discussion
### Discussion  
The U-net model proposed reconstructs the segmented image and protects the original image shape characteristics by storing the grouping indices of the max-pooling layer, a process that is not done in the FCN model.  
The training and testing accuracy is the percentage of pixels in the image that are classified correctly and cannot be taken as indicators of how similar the predicted PV plants and the original mask are [65]. For the purpose of comparing the similarity in the results, the _IoU_ metric was used. This metric varies from 0 to 1 (0-100%) with 0 meaning no similarity and 1 meaning total similarity between original and predicted masks [63].  
The U-net model proposed in this work aimed to obtain a value closer to 1 in the _IoU_ metric. The iteration times show the model used is faster and therefore reliable for the training and processing stages obtaining results virtually in real time [66]. The _DC_ is the other metric used in this work. This metric also ranges from 0 to 1, with 1 signifying the greatest similarity between the predicted and original masks [63]. Both metrics were used to determine if the U-net model was better than the FCN model in the validation and test stages. The values of the _IoU_ and Dice metrics in Table 3 showed the U-net model had a better performance when compared to the FCN model. This work was implemented with VGG16 as an encoder because it was the encoder used by [PERSON] [11], which is a comparison work, but in future work, it is possible to use other encoders like ResNet, AlexNet, etc. [37].  
Finally, the results obtained with the TIP and FCN model agree with the results obtained by other authors [11; 13]. The authors mentioned they did not use the standard  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **TIP Method** & **FCN Model [PERSON] [11]** & **U-Net Proposed** \\ \hline Metrics & N/A & N/A & _IoU_, F1 scor \\ \hline Acc train & N/A & 97.99\% & 97.07\% \\ \hline Acc test & N/A & 94.16\% & 95.44\% \\ \hline _IoU_ metric & N/A & 94.13\% & 93.57\% \\ \hline Dice coef metric & N/A & 95.10\% & 94.03\% \\ \hline val _IoU_ metric & N/A & 90.91\% & 93.51\% \\ \hline val Dice coef & N/A & 92.96\% & 94.44\% \\ \hline test _IoU_ metric & 71.62\% & 87.47\% & 90.42\% \\ \hline test Dice coef metric & 71.62\% & 89.61\% & 91.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between three techniques.  
metrics for these kinds of problems and the bias in the results were expected. On the contrary, this work did take these metrics into account and found satisfactory results. The U-net network increased the processing speed, veracity in the segmentation process, and the overall performance of the model.
## 2 System Model
### Position Estimation
#### 2.2.3 Propagation Distance  
The positions of the balloon (transmitter) and the ship (receiver) are denoted as \((x_{s}(t),y_{s}(t),z_{s}(t))\) and \((x_{r}(t),y_{r}(t),z_{r}(t))\), respectively. The propagation distance \(d(t)\) between them at time \(t\) is given by the following equation [16].  
\[d(t)=\sqrt{(x_{s}(t)-x_{r}(t))^{2}+(y_{s}(t)-y_{r}(t))^{2}+(z_{s}(t)-z_{r}(t)) ^{2}}, \tag{7}\]  
The azimuth and zenith angles of the ship as observed from the balloon are expressed by the following equations.  
Figure 1: Maritime wireless communication spatial model.  
\[\theta(t)=\tan^{-1}\biggl{(}\frac{y_{r}(t)-y_{s}(t)}{x_{r}(t)-x_{s}(t)}\biggr{)}, \tag{8}\]  
\[\phi(t)=\cos^{-1}\biggl{(}\frac{z_{r}(t)-z_{s}(t)}{d(t)}\biggr{)}. \tag{9}\]  
In this context, \(\theta(t)\) and \(\phi(t)\) represent the azimuth and zenith angles, respectively. By substituting Equations (1) to (6), Equations (8) and (9) can be expressed as follows.  
\[\theta(t)=\tan^{-1}\biggl{(}\frac{y_{r0}+V_{ry}t-(y_{s0}+u_{y}t+\sigma_{y}W_{y }(t))}{x_{r0}+V_{rx}t-(x_{s0}+u_{x}t+\sigma_{x}W_{x}(t))}\biggr{)}, \tag{10}\]  
\[\phi(t)=\cos^{-1}\biggl{(}\frac{A_{1}\sin(\omega_{1}t+\phi_{1})+A_{2}\sin( \omega_{2}t+\phi_{2})+A_{3}\sin(\omega_{3}t+\phi_{3})-(x_{s0}+u_{z}t+\sigma_{z }W_{z}(t))}{\sqrt{(x_{s}(t)-x_{r}(t))^{2}+(y_{s}(t)-y_{r}(t))^{2}+(z_{s}(t)-z_ {r}(t))^{2}}}\biggr{)}. \tag{11}\]