esa-sceva/satcom-chunk-collection huggingface dataset:
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 5 Results and Discussion
### Ship Wake Verification Analysis  
ATI-SAR exhibits significant advantages in the observation of oceanic sub-mesoscale phenomena. In the inversion results (Figure 14), a multitude of wake features appear in the radial velocity distribution. This section focuses on analyzing the characteristics of ship wakes present in the ATI-derived surface current field. Although ship wakes in SAR images typically include Kelvin wakes, turbulent wakes, narrow-V wakes, and internal wave wakes [3; 52], Kelvin wakes, and turbulent wakes are the most frequently observed. As shown in Figure 15a, the divergent wave arm on the ship's left side moved faster and is more prominent than that on the right side. This phenomenon can be attributed to two factors: the propagation of divergent waves from the ship as it moved and the fact that these waves traveled in a direction perpendicular to the radar line of sight. Consequently, the left-side divergent wave gradually approached the radar's line of sight, while the right-side wave moved away, causing the left-side wave to have a higher velocity and more distinct features than the right-side one.  
As shown in Figure 15b, several ships are moving in opposite directions in this cropped image. Furthermore, the velocity of the left-side divergent Kelvin wave is greater than that of the right side, consistent with the conclusion we drew from Figure 15a. Additionally, the turbulent wakes of ships moving toward the scene's upper left corner exhibit higher velocities than those of ships moving toward the lower right. This is because the turbulent wakes are primarily influenced by the combined effects of the ship's jet stream and vortices generated during movement, with some whitecap foam being carried forward by the ship's motion, resulting in this phenomenon. Furthermore, the velocity of some turbulent wakes is greater than that of the Kelvin wakes (left or right divergent waves), as the propagation speed of divergent waves is typically slower than the ship's speed. However, the jet stream in the turbulent wake region moves at nearly the same speed as the ship, creating this velocity difference. From the above analysis, we can conclude that the velocity characteristics of the ship wake, as derived from the ATI current inversion results, are consistent with oceanographic and hydrodynamic principles. This consistency further validates the accuracy of the proposed ATI current inversion process and the results.  
Figure 13: A scatter diagram of the LuTan-1 ATI currents versus the CMEMS reference data. The color map shows the point density per pixel. The red line represents the reference line where \(y=x\).
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.6 Heterogeneous Data Transmission and Analysis  
A CPS used for autonomous monitoring of water distribution networks (for leaks and water quality monitoring) generates enormous amounts of heterogeneous data from the sensing devices, which need to be analyzed and processed in an efficient way. Depending on the specific application, advanced computational tools are needed to handle the huge data and to classify and interpret the data required for each application so as to make the optimal decision as regards the state of the water system. In most water system applications of CPS, energy issues become more significant during data transmission and processing. This creates a pressing need for computational algorithms that could offer a robust but computationally less complex with lower power consumption capability. In addition, the sensors installed on the water supply distribution networks are used to provide continuous data measurement at a specific time interval. For example, in the water quality applications [104; 26], this dataset needs to be synchronized and integrated with the hydraulic model in a real-time system for accurate prediction of the pollutant. Thus, accurate time synchronization of sensor data for further processing and transmission is also a major concern.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 3 Configuration of Simulator
### Google Earth for Gazebo  
In the simulator, we use a Google commercial satellite map as a base map. To create a satellite map model and integrate it into a simulation world using Gazebo, which is a free robot simulation software program that provides high-fidelity physical simulations, and to achieve a complete set of sensor models and a user-friendly interaction between users and programs, we utilize the \"Static Map\" world plugin provided by Gazebo ([[https://gazebosim.org/](https://gazebosim.org/)]([https://gazebosim.org/](https://gazebosim.org/)) accessed in 1 October 2023). This plugin allows us to download satellite images in real time using Google's \"Static Maps API\" and incorporate them into the simulation world. Please note that this plugin requires an internet connection as the satellite imagery is fetched during runtime. Since the \"Static Map\" world plugin generates the map model and inserts it into the simulation world at runtime, there is no need for a ground plane model. The satellite map model created using the plugin will serve as the terrain in the simulation environment. We insert some models from the Gazebo model database into the simulation world, as shown in Figure 7.  
The parameters of the plugin are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Description** \\ \hline image & width, height & Width and height of pixel \\  & format & Format of RGB \\ noise & mean & Mean of the noise \\  & stddev & Std of the noise \\ inner parameters & fx, fy & Focal length \\  & cx, cy & Pixel shifting \\  & k1, k2, k3 & Radial distortion \\ distortion & p1, p2 & Tangential distortion \\  & center & Distortion center \\ clipping & near, far clipping & Near and far clip planes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of monocular camera.  
Figure 7: Google map in simulation world.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 4 Implementation
### Static Analysis  
To estimate the complexity of the function call sequences, we first extract the control flow graph and call graph of the instrumented functions using LLVM's static analysis passes. The CFG decomposes each function into basic blocks (single-entry, single-exit instruction sequences), while the CG captures inter-procedural call relationships. These graphs are exported and reconstructed in Python via the NetworkX library for efficient traversal. Next, we compute the complexities through a four-step process: (1) for each basic block in the CFG, Equation (2) calculates its complexity based on instructions, branches, and dependencies; (2) Equation (1) aggregates the block-level results into a function spatial complexity metric; (3) Equation (3) refines this into a holistic function-level complexity by incorporating cyclomatic complexity, nesting depth, and recursion; and (4) Equation (4) derives the cumulative complexity of call sequences by combining individual function metrics with invocation patterns from the CG.  
The static analysis results, including all function complexities, are stored in a dictionary structure for runtime access. This dictionary acts as a lookup table during dynamic scheduling, enabling the prioritization decision based on precomputed complexity metrics. By coupling LLVM's program analysis with structured mathematical modeling, our approach supports both static complexity quantification and dynamic performance tuning.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Disaster Management  
Drones can be used in disaster management for search and rescue operations, damage assessment, and delivering emergency supplies. They can reach areas that are difficult or dangerous for humans to access [22].  
Figure 4: Potential value of UAV-based solutions in key industries for global market [20].
## 3 Discussion of Experimental Result
### Antenna's Efficiency
These elements are fed using three power dividers in an SIW's corporate feeding network. The array formation cannot be lengthy along x and y due to the large feeding network, so the gains of these arrays are lower than 11.1 dBi. Furthermore, a large area is located around the propagating elements, increasing the structure's dimension. The radiation efficiency can be severely harmed by the surrounding boundary around the propagating element [55; 56; 57].
## Abstract  
With the spread of mobile devices and the improvement of the mobile service environment, the use of various Internet content providers (ICPs), including content services such as YouTube and video hosting services, has increased significantly. Video content shared in ICP is used for information delivery and issue checking based on accessibility. However, if the content registered and shared in ICP is manipulated through deepfakes and maliciously distributed to cause political attacks or social problems, it can cause a very large negative effect. This study aims to propose a deepfake detection system that detects manipulated video content distributed in video hosting services while ensuring the transparency and objectivity of the detection subject. The detection method of the proposed system is configured through a blockchain and is not dependent on a single ICP, establishing a cooperative system among multiple ICPs and achieving consensus for the common purpose of deepfake detection. In the proposed system, the deep-learning model for detecting deepfakes is independently driven by each ICP, and the results are ensembled through integrated voting. Furthermore, this study proposes a method to supplement the objectivity of integrated voting and the neutrality of the deep-learning model by ensembling collective intelligence-based voting through the participation of ICP users in the integrated voting process and ensuring high accuracy at the same time. Through the proposed system, the accuracy of the deep-learning model is supplemented by utilizing collective intelligence in the blockchain environment, and the creation of a consortium contract environment for common goals between companies with conflicting interests is illuminated.  
Keywords:blockchain; deepfake detection; collective intelligence; Hyperledger Fabric +
Footnote †: journal: Applied Sciences
## 5 Machine Learning Model Designs
### Clustering
#### 5.2.2 Density Based Clustering  
When using density-based clustering, the clusters that are produced are determined by the data points that have the highest density. Outliers are considered to be any points in the data that do not fit into any of the clusters. This sort of clustering suffers from the major shortcoming that it does not function well when there are data of different densities and large dimensions. Two types of density-based clustering algorithms, DBSCAN and OPTICS, are explained in this article.  
* Density-based spatial clustering of applications with noise (DBSCAN): DBSCAN starts with any object in the dataset and looks at its neighbors within a certain distance and is mostly denoted by eplison (Eps). If the points around it in that Eps are more than the minimum number of neighbors, then these points are marked as core points. The cluster is formed by core objects and non-core objects. The non-core points are part of the cluster, but they don't extend the cluster, which means they cannot include other non-core points in the cluster. The other objects that are not part of any clusters are marked as outliers. The distance between the points is usually the Euclidean distance. The of DBSCAN is presented in Figure 18. K-means is a centroid-based clustering algorithm, and it starts with the initialization of the number of clusters, followed by assigning a random centroid to each cluster. In the next step, we assign the points to the nearest centroid cluster, and once all the points are assigned, we update the centroid.  
Figure 16: GAN Architecture.  
* Ordering Points To Identify Cluster Structure(OPTICS): OPTICS is an extended version of DBSCAN. In OPTICS two more hyperparameters are added. (1) Core Distance (2) Reachability Distance. Core distance is defined as the minimum radius value in order to mark the point as a core point, which means it will see if there is any sub-cluster within a cluster that satisfies the minimum neighbor condition. Reachability is the maxima of the core distance and the Euclidean distance between the points. Reachability and core distance are not defined for non-core points. The OPTICS algorithm works well compared to its predecessor, DBSCAN, as it is able to handle data with varying densities.
# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 5 Discussion  
In the process of real-time positioning, poor position results may be obtained by the BLE fingerprint method. The heading of the device is affected by different motion states of people. Then we get rid of the bad results of the BLE fingerprint method when the scanning number of RSSI is smaller than 5. We correct the heading based on different motion states. Even if the BLE fingerprint method and PDR are integrated, the gross error is difficult to be suppressed. Compared with the adaptive and robust filter proposed in the research of [PERSON] et al. [27], the robust filter proposed in this paper considering the error distribution in more conditions and provides a robust vector instead of a numerical correction. The experimental results conducted on the true position scenario show that the proposed method can detect, suppress the gross errors and make the results smoother. The mean position accuracy of the proposed robust filter was 0.844 m and RMSE was 0.745. The experiments are in line with expectations. From Figure 14, we can find that the green points obtained by the robust filter are far away from the true position. The reason for this is the bad position results caused by the BLE fingerprint method at the previous few steps. The jump points in blue color have occurred continuously. The robust filter can only suppress gross errors but not eliminate them. If gross errors occur continuously, then the results will deviate from the true trajectory and it would take some time to converge. We will improve the method by considering the situation of continuous gross errors to get better results in future research work.
## 2 Methods
### Remote Sensing Data  
A Pleiades tri-stereoscopic satellite image (2015-08-09) was acquired for the densely built-up part of the city of Kigali (Figure 1). The two satellites of the Pleiades mission (1A and 1B) were launched in 2011 and 2012. The satellite constellation provides images with a spatial resolution of 70 cm for the panchromatic channel and 2.8 m for the 4 multispectral channels. The physical resolution is resampled to a spatial resolution of 50 cm and 2 m respectively [6]. For further processing, the multispectral information was pansharpened to the resolution of the panchromatic channel [7]. The Pleiades image was orthorectified using a digital surface model that was derived of the tri-stereoscopic image acquisition (see Section 2.7). The 2008-2009 dataset is the product of the last aerial photography mission that was conducted in 2008 and 2009 and which resulted in orthoimages covering Rwanda [8]. The spatial resolution of the images is 25 cm. Only visual spectral bands (blue, green, and red) were taken, and the radiometric information of the images was adjusted with unknown parameters. This limits the possibilities of an automatic analysis of the 2008-2009 data.
## 2 Communication Architecture
#### 2.2.2 Edge Computing  
The large-scale construction of data center racks has caused problems such as high data processing pressure on the cloud platform of the data center management system [15], untimely response to load resources, and poor service quality. As shown in Figure 2, edge computing technology realizes edge intelligent services for the data center management system, enables the differentiated load management service to operate locally, and relieves the pressure of cloud platform data processing by establishing an open platform with core capabilities such as storage and computing in the area close to the edge data source [16].  
Compared to traditional load management systems, data center systems based on cloud edge collaboration have the advantages of low latency and low bandwidth usage [13].  
Figure 1: Communication architecture of the data center management system.  
This improves the response speed of load resources while saving network deployment costs, achieving full utilization of demand side resources, ensuring efficient consumption of new energy, and improving the overall efficiency of energy utilization [17].
## 5 Conclusions  
Traditional MIMO radar has adopted many proposed ideas to optimize its array structure or the parameter calculations, which have mostly originated from sparse arrays using the genetic algorithm or the particle swarm algorithm to transform the array arrangement into an optimal distribution of the extreme value functions. Starting from the theory of antenna and communication, this paper discussed the new concept of single RF link MIMO radar to solve the problem of the bulky size and large amount of information processing in wireless systems. The theory of single RF link was introduced and the mathematical principle and realization form of the CFAR algorithms were explained. The advantage of single RF link technology is that it reduces the number of links by passing two messages through one single link. The single RF link MIMO radar simulation platform was established in SystemVue software and its performance was compared with traditional MIMO radar. The results show that the single RF link MIMO radar system exhibits almost the same performance of the traditional MIMO radar system.  
Based on the research in this paper, the single RF link technology can be applied to military or civilian radar, especially in civilian small radar. Due to the limitation of radar size and performance requirements in some conditions, the single RF link MIMO radar has a unique advantage in this field. However, the channel described in this paper is based on the ideal case and has no fading. However, in different applications, channel-fading is also a very important factor in complex environments. Therefore, it is worth further studying channel-fading for single radio link MIMO radar system and its derived signal compensation algorithm.  
[PERSON] set up experiments, then she wrote the draft of this paper. [PERSON] helped to construct the experiments and to get the results, and he also put forward to the idea. [PERSON] and [PERSON] helped to analyze the results and improved the English of this paper. All the authors wrote this paper together and they have read and approved the final manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 61671180, No. 61501275, No. 61571155 and No. 61371044), the National Key Research and Development Program of China (2016 YFE0111100) and the Science and Technology Innovative Talents Foundation of Harbin (2016 RAXXJ044).  
The authors declare no conflict of interest.
## 3 Results  
### Scattering Indicatrix  
We have recorded photodiode readings for milk samples with a nominal fat percentage of 0.05%, 0.5%, 1.5%, 3.2%, 6%, and 10% under normal conditions (temperature 20 \({}^{\circ}\)C). By calculating the photocurrent medians, we obtained the actual values of the scattered light intensity at the corresponding angles, where the photodiodes are located as shown in Figure 1. In this way, the sensor measures the angular distribution of the scattered light, which is called the scattering indicatrix. At the beginning, the scattering indicatrix for milk samples was measured in the static case, i.e., at zero flow velocity in the tube (Figure 4). Milk filling a tube with a diameter of \(\sim\)1 cm is a multiple scattering medium, since its optical thickness, estimated from the data on the scattering and absorption coefficients of milk in the visible range [24], exceeds 10, which means that the condition for the prevalence of multiple scattering is fulfilled [25; 26]. Consequently, the scattering indicatrices shown in Figure 4 have a typical form for a multiple scattering medium; specifically, their values increase from the forward-scattering angles to the backscattering ones, which is consistent with the results of theoretical modeling for multiple scattering media [27].  
Figure 4: Scattering indicatrix measured for milk samples with a nominal fat percentage of 0.05%, 0.5%, 1.5%, 3.2%, 6%, and 10% at zero flow velocity in the tube at a temperature of 20 \({}^{\circ}\)C. Laser wavelength is 532 nm.  
The graph (Figure 4) manifests a monotonic dependence of the scattering intensity, \(I\), on the fat content at each scattering angle, \(\theta\). It can also be seen that the scattering intensities at backscattering angles of 144\({}^{\circ}\) and 162\({}^{\circ}\) are practically the same for all fat percentages of the measured milk samples. This fact allows us to introduce a dimensionless parameter, which is a single-valued function of the fat percentage of milk and independent of the laser power, such as the ratio of the intensity at the side-scattering angles to the intensity, for example, at the backscattering angle 144\({}^{\circ}\). Since it is more practical to handle the decimal logarithm of this ratio, the device operates with a scatterometric parameter, which we have defined as follows:  
\[X_{i}=-\log\biggl{(}\frac{I(\theta_{i})}{I(144^{\circ})}\biggr{)},\ \ \theta_{i}=\{72^{\circ},90^{\circ},\ 108^{\circ}\}. \tag{1}\]  
Our experimental tests have shown that it is not advisable to use the forward-scattering angles \(\ \theta_{i}=\{0^{\circ},18^{\circ},\ 36^{\circ}\}\) available in the sensor scheme (Figure 1) for measuring fat content due to the fact that the scattering intensity at these angles is more influenced by large impurity particles; this effect is further shown in Section 3.4. Thereby, fat content measurements are more stable at the side-scatter angles, \(\ \theta_{i}=\{72^{\circ},90^{\circ},\ 108^{\circ}\}\).
## 4 Experimental Results
### Objective Evaluation Parameters  
This paper uses two objective evaluation parameters: Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The MSE is an estimator, which means the average squared difference between the experiment values and the original value. The PSNR is the image quality assessment metric. \(H\) and \(W\) denote the height and weight of images. The secret image, the restored image, cover images, and share images are the same size. The MSE and PSNR are defined in the following. Because the MSE is a risk function, corresponding to the expected value of the squared error loss, it is always strictly positive and unlimited. The PSNR ranges from 0 to 50 and is expected to be as high as possible. Normally, if the PSNR is greater than 30, the encrypted traces on the shares cannot be detected with the human eyes. For color images with RGB values per pixel, the MSE is the sum over all squared R, G, and B value differences and the PSNR is the MSE divided by the image size and by three. For two images \(A\), \(B\) (a secret image and a restored image; or the cover image and share), the PSNR is defined as  
\[\text{MSE}=\frac{\sum_{i=1}^{H}\sum_{j=1}^{W}(A(i,\ j)-\ B(i,j))^{2}}{H\ \times\ W} \tag{1}\]  
\[\text{PSNR}=10\ \times\ \text{log}\Big{(}\frac{255^{2}}{MSE}\Big{)}\ (\text{dB}) \tag{2}\]