esa-sceva/satcom-chunk-collection huggingface dataset:
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 2 Methods
### Parallel Dual Attention Mechanism
#### 2.2.1 Position Attention Module (PAM)  
The position attention module is able to encode broader contextual information into local features so as to enhance the representation ability of iris features. Figure 3 shows the structure of the position attention module. The position attention module was calculated according to Equations (1) and (2).  
\[s_{ji}=\frac{\exp\left(B_{i}\cdot C_{j}\right)}{\sum\limits_{i=1}^{N}\exp \left(B_{i}\cdot C_{j}\right)} \tag{1}\]  
\[E_{j}=a\sum\limits_{i=1}^{N}\left(s_{ji}D_{i}\right)+A_{j} \tag{2}\]  
where \(A\) is the input of the module, which is fed into three convolution layers to generate three new feature maps \(B\), \(C\), and \(D\), with the same dimension \(C\times H\times W\). Then we reshaped \(B\) and \(C\) to dimension \(C\times N\), \(N=H\times W\). After that we performed a matrix multiplication between the transpose of \(B\) and \(C\) to obtain \(s\) with dimensions \(N\times N\), and applied a SoftMax operation. \(s_{ji}\) is the matrix element of the \(j\)th row and the \(i\)th column, representing the impact of the \(i\)th position on the \(j\)th position. \(D\) was reshaped to dimension \(C\times N\). We performed a matrix multiplication between \(D\) and \(s\), reshaping the result to the dimensions \(C\times H\times W\). A sum operation was added with the feature map \(A\) to get the output \(E\), where \(a\) is a scale parameter, initialized as 0 and learning to assign more weight gradually in the training process [29].  
It can be seen from Equations (1) and (2) that the resultant feature at each position was the weighted sum of features from all positions and original features. Therefore, the PAM had a global view of iris images and selectively fused the contexts based on the spatial attention maps. Similar semantic features were integrated and benefited each other, which promotes intra-class feature compactness and semantic consistency.
## 4 Discussion
The high spatial and temporal resolution, free availability, and the integrated and effective use of Sentinel-1 and Sentinel-2 datasets can contribute enormously to analyzing such events and their consequences. However, the quality of post-event DEMs derived from Sentinel-1 data depends on several factors. These include the SAR image pair selection considering the perpendicular baseline, high coherency, and atmospheric conditions, as well as the process for DEM generation, for example, precise coregistration, phase unwrapping, and conversion of the unwrapped phase into elevation values. These are still active research areas [42]. Further research needs to be conducted to systematically assess the accuracy of the DEMs generated using Sentinel-1. Conceptualization: Z.D., D.H. and L.A.; Methodology: Z.D., D.H., L.A. and D.T. ; Validation: Z.D., D.H., L.A. and J.K.H. ; Formal Analysis: Z.D. ; Investigation: Z.D., D.H., L.A., J.K.H. and P.S. ; Data Creation, Z.D., D.H. and L.A.; Writing--Original Draft Preparation: [PERSON] and D.H.; Writing--Review and Editing: [PERSON], D.H., L.A., D.T., J.K.H. and P.S. ; Visualization: Z.D., D.H., L.A.; Supervision: [PERSON]; Project Administration: [PERSON]; Funding Acquisition: [PERSON] and D.T. All authors have read and agreed to the published version of the manuscript. This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DK W 1237-N23), as well as by the Austrian Academy of Sciences (OAW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation). The authors would like to thank [PERSON] and [PERSON] for providing field photographs. The authors declare no conflict of interest. The research was supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DK W 1237-N23), as well as by the Austrian Academy of Sciences (OAW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation). The authors would like to thank [PERSON] and [PERSON] for providing field photographs. The authors declare no conflict of interest. The research was supported by the National Land Survey of Iceland (NLSI) vector layer, available online at [[https://www.lmi.is](https://www.lmi.is)]([https://www.lmi.is](https://www.lmi.is)) (accessed on 15 May 2020); (c) NLSI (license: [[https://www.lmi.is/en/licence-for-national-land-survey-of-iceland-free-data](https://www.lmi.is/en/licence-for-national-land-survey-of-iceland-free-data)]([https://www.lmi.is/en/licence-for-national-land-survey-of-iceland-free-data](https://www.lmi.is/en/licence-for-national-land-survey-of-iceland-free-data)) (accessed on 15 May 2020)).
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
###### Abstract  
Integrated radar and communications (IRC) technology has become very important for civil and military applications in recent years, and IRC waveform design is a major challenge for IRC development. In this paper, we focus on the IRC waveform design based on the multi-symbol orthogonal frequency division multiplexing (OFDM) technique. In view of the defects resulting from high peak-to-mean envelope power ratios (PMEPRs) and high range sidelobes in IRC systems, an intelligent and effective IRC waveform design method jointly optimized with the PMEPR and peak-to-sidelobe ratio (PSLR) is proposed. Firstly, a flexible tone reservation (TR)-based IRC waveform structure is applied in both temporal and frequency domains, i.e., multi-symbol OFDM waveform. Secondly, the optimization problem considering PMEPR and PSLR and extending them to the \(L_{p}\)-norm form is reformulated. Then, the conjugate gradient of the objective function is analytically derived and the conjugate gradient algorithm (CGA) is presented to simultaneously improve the PMEPR and PSLR. Finally, the simulation results show that the proposed algorithm can efficiently generate IRC waveforms with an excellent PMEPR, PSLR, radar signal-to-noise ratio (SNR), and bit error rate (BER) performance.  
t 2022144705 16 September 2022144705 16 2022144705 1  
Integrated radar and communications; orthogonal frequency division multiplexing; peak-to-mean envelope power ratio; peak-to-sidelobe ratio; conjugate gradient
# Impact Evaluation of DME Beacons on BeiDou B2a Signal Reception Performance
## 3 Interference Evaluation Model
### Effective Carrier-to-Noise Ratio Degradation
#### 3.2.2 Multiple DME Signal Interference  
Aviation users can receive signals from different DME beacons at a given position in real flight scenarios. Assuming that the arrival times of different DME pulse signals at the receiver are uniformly distributed and do not interact, the total \(PDC\) and \(Ri\) can be calculated as  
\[PDC=1-\prod(1-PDC_{i}), \tag{12}\]  
\[Ri=\sum_{i}Ri_{i}, \tag{13}\]  
where \(i\) denotes the number of DME interference signals received by the airborne receiver.
## 5 Discussion
### Test Results
In engineering applications, accurate TEC prediction assists engineers in proactively adjusting and optimizing communication system parameters, enhancing their reliability, improving the accuracy of navigation systems, and accurately forecasting adverse space weather.
###### Abstract  
Knowledge of turbulence distribution along an experimental path can help in effective turbulence compensation and mitigation. Although scintillometers are traditionally used to measure the strength of turbulence, they provide a path-integrated measurement and have limited operational ranges. A technique to profile turbulence using time-lapse imagery of a distant target from spatially separated cameras is presented here. The method uses the turbulence induced differential motion between pairs of point features on a target, sensed at a single camera and between cameras to extract turbulence distribution along the path. The method is successfully demonstrated on a 511 m almost horizontal path going over half concrete and half grass. An array of Light-Emitting Diodes (LEDs) of non-uniform separation is imaged by a pair of cameras, and the extracted turbulence profiles are validated against measurements from 3D sonic anemometers placed along the path. A short-range experiment with a heat source to create local turbulence spike gives good results as well. Because the method is phase-based, it does not suffer from saturation issues and can potentially be applied over long ranges. Although in the present work, a cooperative target has been used, the technique can be used with non-cooperative targets. Application of the technique to images collected over slant paths with elevated targets can aid in understanding the altitude dependence of turbulence in the surface layer.  
time-lapse imaging; turbulence; zernike tilt; profiling; sonic anemometry +
Footnote †: 2021
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Experimental Comparison and Analyses
#### 4.3.2 Experimental Comparison for HAM  
In order to prevent small objects from being submerged by noises, this paper enhances object features and suppresses background noises by introducing HAM. The experimental results using different attention mechanisms are shown in Table 2.  
As shown in Table 2, by including the channel attention mechanism, the method with SKNet achieves a 0.6% mAP improvement compared with the baseline. The method with pixel attention achieves a 0.81%/mAP improvement. They both suppress background noises to a certain extent, but the improvement is not significant. The method with the proposed HAM achieves a 2.48%/mAP improvement. By adding a hybrid attention module to simultaneously enhance the learning of positional information and effective channels, while suppressing non-object features more effectively, HAM behaves better than other methods, such as SKNet and pixel attention.  
The visualization results with or without HAM are shown in Figure 7. Obviously, the feature map after adding HAM (shown in Figure 7c) illustrates clearer boundaries of small objects, as well as more distinctive object features than the baseline without HAM (shown in Figure 7b). For example, the ships in the first-row image and the ships in the second-row image can be seen in Figure 7c, but they are completely obscured in Figure 7b and submerged in the background. Actually, HAM suppresses background noises and enhances the object features, making small objects submerged by noises stand out.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mAP(\%)** \\ \hline Without attention mechanism & 72.21 \\ With SKNet & 72.81 \\ With Pixel Attention & 73.02 \\ With HAM & 74.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different attention mechanisms.  
Figure 6: Visualization comparison results without/with FEFN.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Vehicle Decision
#### 3.2.1 Distance  
On the premise of receiving the entire message, it is obvious that the best next hop is the vehicle that is farthest away from the current vehicle. Thus, to ensure the integrity of the message forwarded, we divided the radio range into five groups. Further, we evaluated the distance factor of a vehicle with a score \(D\) based on different places where the vehicle is located. The farther away from the current vehicle, the higher the score evaluated as presented previously. The fuzzy number \(D\) of the distance factor is shown in the following formula:  
\[D=\left\{\begin{array}{ll}\overline{2},&\text{if }\;0<\;\text{distance}\leq \frac{1}{4}r\\ \overline{4},&\text{if }\frac{1}{4}r<\text{distance}\leq\frac{1}{2}r\\ \overline{6},&\text{if }\frac{1}{4}r<\text{distance}\leq\frac{1}{4}r\\ \overline{8},&\text{if }\frac{3}{4}r<\text{distance}\leq r\end{array}\right. \tag{2}\]  
where \(r\) represents the radio radius.
# A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition
## 3 Similarity Measures
### Overview of FSIM: A Feature Similarity Measure  
The feature similarity measure (FSIM) is based on the fact that human visual perception recognizes an image according to its low-level features. FSIM is divided into two parts: the first is the phase congruency (PC), which is a dimensionless measure for the significance of a local structure, while the second is the gradient magnitude (GM). PC and GM play complementary roles in image characterization [13]. The FSIM index between two images \(f_{1}\) and \(f_{2}\) is defined as follows:  
\[FSIM\{f_{1}(x),f_{2}(x)\}=\Phi\{f_{1}(x),f_{2}(x)\}=\frac{\sum_{x\in\Omega}S_{ L}(x)\times PC(x)}{\sum_{x\in\Omega}PC_{m}(x)}, \tag{2}\]  
where \(\Omega\) represents the whole spatial domain for image pixels, \(PC\) is the phase congruency and \(S_{L}\) is a similarity resulting from combined similarity measure for phase congruency \(S_{PC}(x)\) and similarity measure for gradient \(S_{G}(x)\) given by the formula:  
\[S_{L}(x)=\left[S_{PC}(x)\right]^{\alpha}\times\left[S_{G}(x)\right]^{\beta}, \tag{3}\]  
where \(\alpha\) and \(\beta\) are parameters used to adjust the relative importance of phase congruency (PC) and gradient magnitude (GM) features, here taken as \(\alpha=\beta=1\); and  
\[S_{PC}(x)=\frac{2 PC_{1}(x)\times PC_{2}(x)+T_{1}}{PC_{1}^{2}(x)\times PC_{2}^{ 2}(x)+T_{1}}, \tag{4}\]  
where \(T_{1}\) is a positive constant, inserted to increase the stability of \(S_{PC}\) (such a consideration was also included in SSIM), and  
\[S_{G}(x)=\frac{2G_{1}(x)\times G_{2}(x)+T_{1}}{G_{1}{}^{2}(x)\times G_{2}{}^{2 }(x)+T_{2}}, \tag{5}\]  
is the gradient similarity, where \(G=\sqrt{G_{x}^{2}(x)+G_{y}^{2}(x)}\) is the gradient magnitude; \(G_{x}\) and \(G_{y}\) are partial derivatives of image \(f(x)\). The phase congruency PC is defined as follows:  
\[\mathrm{PC}(x)=\frac{E(x)}{\epsilon+\sum_{n}A_{n}(x)}, \tag{6}\]where \(\epsilon\) is a small positive constant, and  
\[E(\mathrm{x})=\sqrt{K^{2}(\mathrm{x})+H^{2}(\mathrm{x})}, \tag{7}\]  
where \(H(\mathrm{x})=\sum_{n}o_{n}(\mathrm{x})\) and \(K(\mathrm{x})=\sum_{n}e_{n}(\mathrm{x})\), \(o_{n}(\mathrm{x})=\xi(\mathrm{x})*M_{n}^{e}\); \(e_{n}(\mathrm{x})=\xi(\mathrm{x})*M_{n}^{o}\), noting that \(M_{n}^{e}\) and \(M_{n}^{o}\) are even and odd symmetric filters on scale \(n\), and \"*\" denotes convolution. The function \(\xi(\mathrm{x})\) is a 1D signal obtained after arranging pixels in different orientations. The local amplitudes \(A_{n}(\mathrm{x})\) are defined as:  
\[A_{n}(\mathrm{x})=\sqrt{e_{n}^{2}(\mathrm{x})+o_{n}^{2}(\mathrm{x})}\, \tag{8}\]  
where x is the position on scale \(n\).
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 2 Data Consumers in an SPS  
A smart parking system is a complete digital platform that manages city-wide parking resources in real-time and provides multiple services to end users [7, 8]. Starting from searching for a nearby available parking slot, booking a parking slot in advance, navigating to an available parking lot, charging electric vehicles at the booked slot, and predicting the availability for a specific time until payment for parking or charging is possible with a SPS. An smart parking lot utilizes various sensors for the accurate identification of empty slots, parking boundaries and the automated counting of the number of entries and exits. Apart from these sensors, it may have other IoT devices such as overhead LEDs as indicators for vacancy, LCD displays showing layout/availability statistics, buzzers or alarms for indicating wrong parking, automated gates that open after payment verification or license plate identification. These IoT devices need data from the sensors for their intended operations and are data consumers in a smart parking system.  
A cloud-based SPS collates availability data from the sensors and sends them to the cloud for storage and processing. The application(s) on cloud servers provide relevant decisions and inputs to the data consumers. In such a system, it is required that the data consumers are connected directly to the cloud. This may not be an economical solution, as a direct Internet connection is required for all the consumers. For instance, an overhead LED light, showing the occupancy of a specific parking lot, just needs input from the respective parking sensor. Such a local scope does not need a cloud SPS. A mesh with any-to-any traffic support is most suitable. Multi-hop mesh networks are a cost-effective way of connecting heterogeneous IoT devices and providing Internet connectivity to all nodes in a network. There is no gateway device involved in an IPv6 mesh as there are no protocol translations, and all the communication is IP based. In addition, local data consumers can be given instructions within the network by the border router itself in a scaled-down centralized approach, reducing the round-trip time taken by the data. Data consumers can be classified as on-site or over-the-Internet, depending on the scope of the data consumed by them.
# Factors Influencing the Action Point of the Collision Avoidance Manoeuvre
## 3 Results
### Stage II  
2.1 Influence of Relative Speed on the Distance between Ships and TCPA at the Start of the Manoeuvre  
In order to assess the influence of the relative speed on the distance between ships and the TCPA value at the start of the manoeuvre, the speed was divided into intervals, and then the average distance was determined for each of them. For such prepared data, trend lines were determined, and the value of the R2 coefficient was calculated. The results are shown in Figure 3. The values of R2 equals 0.81 for distance and 0.72 for TCPA.  
2.2 Influence of the Size of the Target Ship on the Distance between the Ships and the TCPA at the Start of the Manoeuvre  
The second examined factor influencing the starting point of the manoeuvre was the size of the target ship. The calculations were performed in the same way as for the  
Figure 3: Relation between relative speed, TCPA and the distance between ships at the start of the manoeuvre.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Distribution** & \multicolumn{3}{c}{**All Situations**} & \multicolumn{3}{c}{**Open Sea Area**} & \multicolumn{3}{c}{**Restricted Sea Are**} \\ \hline Log-normal & 0.932 & 0.718 & 3.17; 0.49 & 0.832 & 0.389 & 3.30; 0.45 & 0.406 & N/A & 2.88; 0.47 \\ Rayleigh & 0.356 & 0.560 & 21.27 & 0.363 & 0.024 & 23.71 & 0.266 & 0.071 & 15.04 \\ Weibull & 0.297 & 0.168 & 30.25; 2.05 & 0.408 & 0.252 & 34.15; 2.17 & 0.782 & N/A & 22.16; 2.60 \\ Normal & 0.079 & 0.009 & 26.7; 14.0 & 0.172 & 0.105 & 30.1; 14.9 & 0.631 & N/A & 19.67; 8.31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(p\)-values and distributions parameters calculated for the TCPA at the start of the anti-collision manoeuvre.  
evaluation of the influence of the relative speed. The obtained results are presented in the Figure 4, the values of R2 equals 0.61 for distance and 0 for TCPA.
## 4 Experiments and Results
### Benchmark Dataset  
Similar to the related works [29; 30; 31], the remote sensing images used in this paper were collected from Google Earth with a resolution of \(500\times 375\) pixels and a spatial resolution of 0.5 m to 2 m. Compare with the targets in nature scene images, like ImageNet [35], MS COCO [36], Pascal VOC [37], the targets in remote sensing images (such as aircraft, ship, and oiltanker) usually have complex backgrounds. In addition, the remote sensing images used in this paper are different from the existing remote sensing images databases, such as the NWPU VHR-10 dataset [38], and (Aerial Image Dataset)AID dataset [39], which include more small targets (the area for small targets: area \(<32^{2}\) pixels) and medium targets (the area for medium targets: \(32^{2}<\) area \(<96^{2}\) pixels), which further increases the difficulty of target detection in remote sensing images.  
We collected 1500 remote sensing images to construct a dataset for tiny target detection, each image is required to be marked with detection targets: aircraft, ship, oiltanker, and the manually pixel-wise annotated in Pascal VOC format. Each detection object is marked with a ground truth box as shown in Figure 5.  
Table 1 gives more details about this data set and Figure 6 shows the aspect ratio and area size distribution. It is obvious that the area of bounding boxes is generally small in Figure 6, which means that the architecture of our model should pay more attention to small detection targets and the set size of the default box can be further reduced. It is noteworthy that the aspect ratio is not symmetrically distributed around 1 and its distribution is also relatively concentrated as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Images**} & \multirow{2}{*}{**Total Instances**} & \multicolumn{3}{c}{**Target**} \\ \cline{5-8}  & & & & **Small** & **Medium** & **Large** \\ \hline \multirow{4}{*}{Train} & aircraft & 374 & 4281 & 3191 & 1090 & 0 \\  & oiltanker & 320 & 2725 & 1225 & 1500 & 0 \\  & ship & 526 & 1418 & 1100 & 315 & 3 \\ \hline \multirow{4}{*}{Test} & aircraft & 87 & 918 & 698 & 220 & 0 \\  & oiltanker & 76 & 886 & 401 & 485 & 0 \\ \cline{1-1}  & ship & 137 & 408 & 300 & 108 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The remote sensing image dataset.  
Figure 5: Sample images from the constructed remote sensing image dataset. The detection targets are annotated with ground truth boxes.
# Vehicle-Following Control Based on Deep Reinforcement Learning
## 3 Problem Formulation
### Reward Function  
The reward function \(R:S\times A\times S^{\prime}\rightarrow\mathbb{R}\) in reinforcement learning is an incentive mechanism that enables the agent to learn a behavioral strategy to meet the ultimate goal. Two policy networks are used in this paper but need to maintain a consistent estimate of the advantage function, so a reward function is used uniformly: \(R=\gamma_{1}\times\alpha-\gamma_{2}\times\beta-\eta\times acc_{r}\)  
* where \(\alpha=\left\{\begin{array}{c}dist,\text{if}\ speedX_{-r}\times T_{mth}\leq \ dist\leq speedX_{-r}\times T_{mth}+2\\ 0,\text{others}\end{array}\right.\), \(\gamma_{1}=1\). A positive reward is given when the vehicle maintains a safe spacing \(\left[speedX_{-r}\times T_{mth},\ speedX_{-r}\times T_{mth}+2\right]\), and \(T_{mth}=2\) s is the Minimum Time Headway.
* \(\beta=\left|\ speedX_{-r}-speedX_{-p}\ \right|,\gamma_{2}=0.2\). On vehicle-intensive roads, the vehicle speed is generally less than 30 km/h. In this paper, the maximum speed of the preceding vehicle is set as \(speedX_{-p}=25\) km/h \(\approx\) 7 m/s. Since the vehicle uses the sensor to obtain the current state information, the accuracy and speed are much higher than that of a human driver. Maintaining a similar speed can stably follow the distance to ensure safety. Therefore, the speed of the vehicle is kept as consistent as possible in the experiment.
* \(acc_{-r}\) is the vehicle acceleration, \(\eta=0.05\).