esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Application of Knowledge Representations
#### 2.3.1 Application of Observed Knowledge Representation  
The result of knowledge representations \(\mathbf{K_{u}}=\{\alpha_{i}|1\leq i\leq p,1\leq p\}\),  
\(\mathbf{K_{d}}=\{\beta_{i}|1\leq i\leq q,1\leq q\}\) include angle information between the separated targets in the signal and the axial direction. When combined with the Sobel operator, it can be used to calculate directional difference operators for gradient computation along the axis. For example, the difference operator along \(\alpha_{i}\) is denoted as \(\
abla_{\alpha_{i}}\) and defined as follows:  
\[\
abla_{\alpha_{i}}=cos\alpha_{i}\begin{bmatrix}1&0&-1\\ 2&0&-2\\ 1&0&-1\end{bmatrix}+sin\alpha_{i}\begin{bmatrix}1&2&1\\ 0&0&0\\ -1&-2&-1\end{bmatrix} \tag{12}\]  
With the mathematical definitions provided above, it becomes feasible to apply the results of knowledge representation to the task of signal separation:  
\[\begin{split}\Omega_{u}(\mathbf{K_{u}},\mathbf{\tilde{S}}_{u})& =\min\{\
abla_{\alpha_{i}}*\mathbf{\tilde{S}}_{u}|1\leq i\leq p,1 \leq p\}\\ \Omega_{d}(\mathbf{K_{d}},\mathbf{\tilde{S}}_{d})&=\min\{ \
abla_{\beta_{i}}*\mathbf{\tilde{S}}_{d}|1\leq i\leq q,1\leq q\}\end{split} \tag{13}\]  
where \(*\) denotes the convolution operation. The final result of applying knowledge is combined as follows:  
\[M(\mathbf{K_{u}},\mathbf{\tilde{S}}_{u},\mathbf{K_{d}},\mathbf{\tilde{S}}_{d}) =\|\Omega_{u}(\mathbf{K_{u}},\mathbf{\tilde{S}}_{u})\odot\Omega_{d}( \mathbf{K_{d}},\mathbf{\tilde{S}}_{d})\|_{F}^{2} \tag{14}\]  
where \(\odot\) denotes the Hadamard product. \(\Omega_{u}\) is a function that combines the predicted results of the upgoing wavefield with the observed knowledge of the upgoing wavefield. Similarly, \(\Omega_{d}\) is a function that integrates the predicted results of the downgoing wavefield with the observed knowledge of the downgoing wavefield.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 4 Validation of the Proposed Methodology via Case Study Application
### Proactive Analysis of Black Spots and Intervention Proposals
#### 4.3.2 Black Spot for Cyclists  
The second black spot (C) highlights a dangerous cyclist crossing at a square (see Figure 16).  
The crossing spans 18 m, traversing two lanes in each direction, separated by a narrow central island under two meters wide. Although equipped with a non-active traffic light, the location handles an average daily traffic of 10,000 vehicles in both directions, as per Origin-Destination (O-D) matrix data from the municipality. Between 2017 and 2023, 15 cyclist-related accidents, including one fatality, occurred near this crossing, underscoring its high risk.  
Despite entering the crossing's characteristics into the iRAP model, including a 40 km/h operational speed and the presence of a bike lane, the simulated star rating produced a negligible risk score (see Figure 17). This noticeably contrasted with the actual accident data, prompting further investigation into the model's limitations regarding cyclist crossings.  
The iRAP ViDA platform evaluates VRUs with attributes like pedestrian crossings and cycling infrastructure. However, it lacks specific metrics for cyclist crossings, a critical gap in assessing cycling safety. As a workaround, the analysis attempted to use pedestrian crossing ratings for cyclist crossings, as pedestrian evaluations often reflect crossing-related risks more accurately.  
This adaptation, however, introduced distortions in estimating fatalities and serious injuries. Consequently, critical point C was excluded from the proactive cost-benefit analysis. The study emphasizes the need for iRAP to refine its methodology to better address the safety of cyclist crossings, considering both the quality of cycling infrastructure and crossing safety.  
Figure 16: Concentration map outputted by GIS for critical point C; (**a**) accidents involving at least one bicycle and (**b**) area magnified.  
Figure 17: Star rating option 0 (**a**) the first arm of the square and (**b**) the second arm of the square.
## 3 Operation Optimization
### The Proposed New Charging Topology  
As shown in Figure 4, the proposed new topology uses power diodes and DC/DC modules to build a bipolar DC bus on 690 V. It contains more DC/DC charging modules to achieve the charging strategy, namely fast-charging during operating hours and normal charging at night. For the modularization of the transformer and converters, it is easy for the capacity extension or reduction of PEB fast-charging stations. This topology uses a low turns-ratio transformer to increase the secondary side voltage of the transformer to AC 690 V. As the DC charging voltage of PEB is over 600 V, it can reduce the AC/DC voltage deviation in the low-voltage side. As is well known, the diode rectifiers have a higher conversion efficiency and lower price than the pulse width modulation (PWM) modules, and the three-level DC/DC modules also have a high conversion efficiency. For the new station design, the new topology can reduce the quantities of transformers and converters. Furthermore, it can decrease the energy conversion losses during the charging procedure. The station charging efficiency is increased with the construction investment reduction at the same time.
## 3 System Overview  
We present in this paper a set of tools so that a director can interface with an autonomous fleet of drone cinematographers and govern the system from an editorial point of view. The main contributions are a novel language for mission description in media production and a graphical tool to define those missions and interact with the autonomous system. The general architecture of the system is depicted in Figure 1.  
The director and the editorial team can specify a set of artistic shots and associate them with different events happening in time (e.g., a race start or racers reaching a relevant point). This is done through the _Dashboard_, which is a web-based graphical tool that allows her/him to interact with the rest of the system. The whole set of director shots, together with information of the events with which they are associated, constitutes the so-called _shooting mission_, which is saved in a database during the editing phase. After finishing this editorial phase, the shooting mission is encoded in an XML-based language and sent to the _Mission Controller_, which is the central module managing autonomous planning.  
The Mission Controller can understand the director's dialect and is in charge of interpreting the shooting mission, as sequential or parallel _shooting actions_ that will be assigned to the available drones in the team. A list of _tasks_ to be executed by the drone fleet is compiled, where each task has a starting position and a duration, extracted from the shooting actions' descriptions. Then, the Mission Controller can use a _Planner_ to obtain a feasible plan to accomplish that shooting mission.  
The Planner should take into account spatial and temporal constraints of the tasks, as well as drone battery levels and starting positions, in order to assign tasks to drones efficiently. The plan consists of a list of _drone actions_ for each drone, specifying where the drone should go at each instant and what to do. Basically, each drone gets assigned a sequence of shooting actions, so its list of actions is made up of single-drone shooting actions plus the required navigation actions in between. In general, this Planner module could be implemented by any standard planner for task allocation with temporal and resource constraints and it is not the focus of this paper. Some preliminary ideas can be seen in [19].  
Once the plan is computed, it is sent to the drones, which are able to execute it autonomously. During execution, the Mission Controller provides some feedback to the director through the Dashboard, reporting on the status of each drone and the mission itself.  
Section 4 describes the XML-based language to describe shooting missions and the process to translate them into plans made up of drone actions. Section 5 describes the design and functionalities of the Dashboard.
## 3 Design, Simulation and Implementation for Scattering Measurement System
### Modeling and Implementation for Surface Scattering Measurement System
Then, turn on the laser at 1064 nm and LED light; adjust the laser incident direction, using the aiming-and-positioning system; make it incident vertically from the beam inlet port to the center position of reference sample plate; and record the photocurrent value \(I_{1}\) received by the detector. The third step is to detach the standard reference sample plate and to replace it with the sample plate to be measured. Then, turn on the green laser at 532 nm and LED light, observe the spot position on the sample plate according to the image recorded by the CCD camera and adjust the position of the sample plate properly to make the spot coincide with the region to be measured on the coating surface. After completing the above operations, turn off the indicator laser at 532 nm and LED light, turn on the infrared laser at 1064 nm and record the photocurrent value \(I_{2}\) recorded by the infrared detector. At last, the surface reflectance \(s\) of the measured coating can be calculated as the following expression:  
\[s=\frac{I_{2}-I_{0}}{I_{1}-I_{0}}.\rho_{\text{ref}} \tag{13}\]  
where the \(\rho_{\text{ref}}\) is the surface reflectance of gold-plating standard reference plate.
## 3 Results
### Simulation Study
#### 3.1.1 Simulation Study 1  
Tensors in the CC have small size, horizontal diffusion direction (i.e., the water diffuses between right and left hemisphere of the brain) and high FA. Regions nearby to the CC consist of other white matter (WM) tissues, grey matter (GM) and cerebrospinal fluid (CSF). Tensors in some WM regions have similar sizes and FA to that of the CC, whilst tensors in GM and CSF have larger sizes and smaller FA than that of the CC (since the diffusion is anisotropic in WM and isotropic in GM and CSF). Therefore, tensors are initially simulated from multiple regions, mimicking a real brain image, with differing FA and sizes of tensors around the CC (see Figure 1a).  
Then, three levels of noise \(sd((X_{j})_{ls})=0.3\times 10^{-5}\) (noise1a), \(sd((X_{j})_{ls})=0.4\times 10^{-5}\) (noise2a) and \(sd((X_{j})_{ls})=0.5\times 10^{-5}\) (noise3a) are added to the simulated (original) region (see Figure 1b-d). The signal-to-noise ratios (SNR) of the three level of noises are 21, 18, and 15. The results of segmentation of the three noisy regions are shown in Figure 2. The figures visibly demonstrate that sFCM improved the segmentation by reducing the background noise as compared to K-means.  
To provide more detailed comparisons, all of the performance measures considered for the six cases are shown in Figure 3. It can be seen that sCFM with each metric (Euclidean, log Euclidean, and root Euclidean) almost always outperformed K-means with the same metric for all performances measures; the only exceptions are the equality of sensitivity for log Euclidean at noise level 3a, and both root and log Euclidean at noise level 1a.  
Furthermore, sCFM with root Euclidean or log Euclidean generally outperform sCFM with Euclidean. In detail: (i) at noise level 1a, root and log Euclidean produce the same results, and thus yield equality for all performance measures, and their measures all outperform Euclidean except for the (equality of) sensitivity; (ii) at noise levels 2a and 3a, root Euclidean has the highest accuracy, sensitivity, F-measure, and Gmean, whilst log Euclidean has the highest specificity and precision. Euclidean has the lowest values for accuracy, F-measure, specificity, and precision, but the same sensitivity as root Euclidean, and a higher Gmean than log Euclidean.  
Figure 1: The original tensors in comparison with the tensors having added the three levels of noise.  
Figure 2: Segmentation after adding three levels of noises.
## 4 Numerical Example
The comparative analysis of Figures 6 and 9 reveals that the LTE amplitude obtained using the FEA method are slightly smaller than those obtained using the LTCA method, indicating that the variation of the LTE curve obtained using the two methods are basically the same. The comparative analysis of Figures 7 and 10 reveals that the fluctuation amplitude of MS obtained based on FEA is comparable with those obtained based on LTCA and that the variation of the MS curve obtained using the two methods are basically the same. In conclusion, the loaded meshing performance of the optimized gear transmission is obviously improved. The MIs of the gear pair were calculated when the input speed is 8000 rpm and load is 2300 N\(\cdot\)m and are listed in Figure 11. Comparative studies show the impact velocity of the preset gear is 0.3495 m/s, and that of the optimized gear is 0.2593 m/s, which is 25.81% lower than that of the preset gear. The impact force of the preset gear is 3687.0 N, and that of the optimized gear is 2690.7 N, which is 27.02% lower than that of the preset gear. After optimization, the MIs of gear are reduced, and their meshing quality is also improved. Based on the SBG dynamic model (Figure 2), the dynamic performance of gears are analysed, and their results are illustrated in Figures 12-17. A comparative analysis found that the RMA value of the preset SBG is 851.41 m/s\({}^{2}\), and the RMA value of the optimized gear is 196.57 m/s\({}^{2}\), which is 76.91% lower than that of the preset gear. The dynamic load factor of the preset gear is 1.19, while that of the optimized gear is only 1.060, which is lower than that of the preset gear. It can be seen from Figures 14-17 that the normal vibration velocity and displacement of the optimized gear transmission are considerably decreased. Therefore, based on the active precontrol strategy for shape and performance, the vibration of the SBG are reduced, and its dynamic characteristic is improved. Figure 11: MI of gear before and after optimization. Figure 10: The MS of gears based on FEA. Figure 9: The LTE of gears based on FEA. Figure 14: Dimensionless displacement with lumped mass method. Figure 12: Vibration acceleration with lumped mass method. Figure 4:0 Dynamic load factor with lumped mass method. Figure 4:1 Normal vibration phase diagram with lumped mass method. To further prove the strong points of the optimized gears in dynamic performance, the RMA of the gears are analysed based on the structural dynamics and shown in Figure 4:2 The comparative analysis of Figures 12 and 18 reveals that the RMA obtained by the structural dynamics are comparable to those obtained by the lumped mass method and that the RMA curve obtained by the two methods are basically the same. In conclusion, the dynamic performance of the optimized SBG transmission is obviously improved.
# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 5 Experiments and Analysis of Results
### Comparison of the Proposed Model with Existing Models
Taking the second original image as an example, the three comparison methods had different degrees of missed detection, among which the Crack-UNet network had the most missed detection, and the missed detection is marked with a red box in the figure. Although the DeepLabV3+ network and the RAO-UNet network were better than the Crack-UNet network, the processing effect in the detail part was still not satisfactory, and the connection was prone to missing segmentation. The segmentation effect based on the proposed method was better than the other three methods in terms of details and joints and was closest to the real label map. In order to verify the generalization and superiority of this method, experiments were conducted on the CFD dataset again to verify the superiority of this method. The CFD dataset used in this experiment was not used for training, and it contains images taken under different conditions. The segmentation effect diagram is shown in Figure 20.  
The experimental results are as follows:  
It can be seen from Table 7 that the evaluation results of the mean intersection over union, accuracy, F1-score, and the recall rate of the proposed model were 82.07%, 93.29%, 89.64%, and 86.26%, respectively, which were better results than the other models. However, compared with the Crack500 dataset, the accuracy was much lower, and the mean intersection over union and F1-score were reduced by 1.73% and 2.97%, respectively. The  
Figure 19: The segmentation effect diagram of the proposed model and the comparison models. segmentation performance of the four models was lower than that of the Crack500 dataset. The Crack-UNet could generally divide the outline of the cracks, but the performance in the detailed part of the cracks was very poor, resulting in much loss of the detailed part. The DeepLabV3+ model was better than the Crack-UNet model in the detail part, but it was still not satisfactory, much different from the real label image, and in the segmentation, there was a mis-segmentation situation, with the background showing as a crack for segmentation. The RAO-UNet model had a fracture when splitting the cracks, and the cracks could not be completely split out. The model proposed in this paper was closer to the labeled image in terms of the segmentation effect, and the overall effect was better than the other three models, while the other three methods did not perform as well as they should on the CFD dataset. It was also shown that the proposed model in this paper can be widely adaptable in different situations. Therefore, we can say that the method used in this paper is more accurate and robust than the other three methods and has wider adaptability to situations that did not arise during the training and validation process.
## 3 ATM Security Concerns and Trends
### The Trends
Although, in several recent hacker conferences (DefCon and Black Hat), technicians in the field of network security have used inexpensive, generic software radio platforms to demonstrate cyber attacks on ATM surveillance systems, clearly believing that cyber security will pose an important threat to flight safety. However, ATM professionals are not fully aware of the changes in the ATM cyber security threat model and the serious threat that cyber security poses to flight safety. ATM professionals have a weak and vague understanding of the \"important impact of cyber security on flight safety\" and usually believe that the traditional approach based on backup and emergency proceduresis sufficient to ensure flight safety in the event of a cyber attack, thus placing cyber security at a lower priority for development. Therefore, the important impact of cyber security on flight safety in the field of ATM needs to be further explored and studied. The study of the propagation path of cyber attack impact in ATM and the quantitative analysis of the impact of cyber attack on core ATM performance (e.g., the impact on flight interval control and anti-collision algorithm) can complete an effective and credible cyber security assessment of ATM, thus drawing the attention of professionals in ATM field to cyber security and promoting the future development of ATM security. (d) ATM has special cyber security needs, and it is urgent to build a complete networked ATM cyber security defense solution to ensure the operational efficiency and flight safety of the system. At present, security solutions for different components of ATM are built separately, lacking effective interaction among them, and security solutions of different component bring fragmentation and incompatibility of system security. Therefore, it is difficult to meet the security needs of networked ATM. By building a complete networked ATM security solution, integrating the security protection of different components, and deploying a defense-in-depth solution, we can realize the basic reinforcement, accurate detection, and fast response capability of ATM security, avoid the spread of damage caused by attacks, and ensure the overall security of ATM. In summary, the development trend of networked ATM information security is to establish a systematic and deep defense system. Relying only on the engineering security protection mechanism of the physical system and the information security technology of the cyber system, it is difficult to achieve the requirements of comprehensive security of ATM-CPS. In turn, it is necessary to study new ATM-CPS information security solutions from ATM service data fusion, system model fusion and system defense ideas.
# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 5 Discussion
### Effect of Part Alignment  
The analysis is performed on DukeMTMC-ReID dataset [9]. The proposed network is compared with two networks, 34-layer Resnet network and four-branch parts fusion network without DropParts. The architectures are as follows:  
(1) BASE network architecture: Branch01 in Table 1, including input, conv1, res2, res3, res4, res5, pool5, F_fuse and Softmax layers.  
(2) ALIGN network architecture: same as architecture in Table 1 but without scaled_pool5 layer; when meeting part occlusion, a zero patch is used to replace it.  
(3) ALIGN +DROP network architecture: same as architecture in Table 1.  
In this experiment, we report the loss curve during training and CMC curve to evaluate the performances of three networks above.  
From Figure 5(a), we can see that after 13000 iterations of training, the losses of ALIGN and ALIGN +DROP network reach the very low level (<0.02) while the loss of BASE network is above 0.12, which signifies under-fitting of BASE network. As a result, seen from Figure 5(b), rank-1 accuracy of BASE network is lower than of ALIGN and ALIGN +DROP by almost 19%, and CMC curves of ALIGN and ALIGN +DROP networks are above the CMC curve of BASE network all the time. As seen from the loss curves and CMC curves, the addition of part alignment and feature fusion result in significant improvement in the person re-identification performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline Basel. + LSRO [9] & 0.6768 & - & - & 0.4713 \\ PAN [44] & 0.7159 & 0.8389 & - & 0.5151 \\ SVDNet [43] & 0.7670 & 0.8640 & 0.8990 & 0.5680 \\ AACN [29] & 0.7684 & - & - & 0.5925 \\ PSE [11] & 0.7980 & **0.8970** & 0.9220 & 0.6200 \\ ATWL(2-stram) [45] & 0.7980 & - & - & 0.6340 \\ HA-CNN [28] & 0.8050 & - & - & 0.638 \\ \hline PADP (VGG19+BN) & 0.8134 & 0.8720 & 0.9264 & 0.6444 \\ PADP (Resnet18) & 0.8076 & 0.8726 & 0.9221 & 0.6407 \\ PADP (Resnet34) & 0.8156 & 0.8742 & 0.9286 & **0.6455** \\ PADP (Resnet50) & 0.8150 & **0.8755** & 0.9223 & 0.6452 \\ PADP (Desnet121) & **0.8191** & 0.8752 & **0.9287** & **0.6455** \\ PADP (Inception\_v3) & 0.8128 & 0.8748 & 0.9285 & 0.6424 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Rank-1, rank-5, rank-10 and mAP of various methods on the DukeMTMC-ReID dataset.