esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking of Moving Targets Through Asynchronous Measures
###### Abstract  
Unmanned Aerial Vehicles (UAVs) have progressively gained interest in recent years due to the wide range of related applications, from aerial communications and autonomous flight to agriculture and logistics. However, accurate 3D localization is crucial for enabling these kinds of applications, and commonly used tracking algorithms are often performing unsatisfactorily in critical scenarios like urban canyons and environments, characterized by dense multipath and line of sight obstruction. In this work we derive a novel 3D tracking algorithm which, despite its mathematical simplicity, can efficiently track moving targets handling asynchronous arrival of the anchor measurements or obstructions of line-of-sight links and outperforming commonly used algorithms like the Extended Kalman Filter (EKF) and the Particle Filter (PF). The proposed algorithm tracks the 3D position, velocity, and acceleration of a moving target through the combination of range measurements, between the target and different anchors, which become available in numbers and time instants not necessarily ordered as usually assumed in these applications. We denote this condition as asynchronous measurements, meaning that the ranging measurements are not available from all the anchors and they refer to different positions of the UAV during the tracking. We also show that our estimator is optimal among the linear ones, meaning that within this class, it minimizes the estimation error variance. Finally, we explore the accuracy that can be achieved in simulated scenarios defined by realistic UAV altitudes, velocities, and trajectories, as well as typical ranging errors of wideband localization systems.  
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Footnote †: journal: Medical Imaging  
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Footnote 1: email: [EMAIL_ADDRESS]  
**Keywords:** tracking; positioning; UAV; multilateration
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 6 Simulation and Results  
In this section, we present a comprehensive evaluation on the performance of MRIR, Route-Stability [17], and the Route-Closeness [18] algorithm using Matlab R2010a (7.10, MathWorks, Natick, MA, USA, 2010.9). The network topology region size was 2000 m \(\times\) 2000 m, and \(T_{R}\) = 50 m, \(I_{R}\) = 100 m. \(|V|\) = 60 represents the number of Secondary Users (SUs). Ten primary users (PUs) were randomly distributed in the area. The PUs randomly occupied and accessed the authorized channel. If the PUs needed to access the authorization channel again, then the SUs needed to quit the use of the authorized channel, which led to the failure of the wireless link to the SUs. In this simulation, the time and duration of the access channel of each PU node were randomly generated. The available channel number was \(|K|\) = 12, and the bandwidth of each channel was randomly generated in the range of 0-50 Mbps. The wireless request demand bandwidth \(b_{p}\) was also randomly generated in the range of 0-20 Mbps. The wireless requests randomly arrived and departed. In a duration time of 1800 s, the arrival wireless request was \(|\Delta|\) = 20. The following simulation results were the mean of 200 independent simulation results. The simulation was mainly based on the number of wireless requests, the number of available channels as parameters, the analysis and comparison of the average throughput, and the wireless request acceptance rate.
## 3 Results and Discussion
### Adopted Strategies for Adaptation and Mitigation
#### 3.4.1 Multi-Concept Approach  
[PERSON] et al. [87] proposed a multi-concept approach to climate mitigation, dividing strategies into three categories: monitoring, structural, and non-structural. Key examples include carbon-neutral production, which is becoming increasingly viable, and monitoring campaigns to assess environmental threats. Additionally, conservation agriculture, as the FAO promotes, and technologies, such as field and remote sensors for detailed mapping, are essential tools guiding these mitigation efforts. Conservation agriculture, in particular, is grounded in three core principles: minimal mechanical soil disturbance, permanent organic soil cover, and crop diversification.  
Minimal mechanical soil disturbance supports soil health by enhancing structure, increasing microbial activity and organic matter, aiding in erosion control, improving water retention, and reducing fuel and labour costs. Maintaining permanent organic soil cover through mulching and crop residues also protects against erosion, while crop diversification further boosts resilience to climate change. Diverse cropping systems, including nitrogen-fixing legumes, perennials, and deep-rooted plants, enhance carbon sequestration by contributing organic matter via roots and residues.  
In line with these strategies, [PERSON] et al. [111] investigated the roles of intraspecific and interspecific diversification within forage cropping systems. They examined three diversification approaches--cultivar diversity (intraspecific), crop species diversity (interspecific), and a combination of both (intra + inter). Comparisons with single-species monocultures revealed that species diversity substantially impacted system responses, more than cultivar diversity. Selecting traits to maximise functional and response diversity was beneficial, further emphasising the value of crop diversity in resilience strategies.  
Similarly, [PERSON] et al. [90] explored farmers' adaptation strategies in the face of climate change and increasing salinity along Bangladesh's south-central coast. Salinity was identified as a major environmental threat in the region, and the study highlighted that adaptation begins with understanding climate change consequences. Observing rising temperatures in summer and winter, increased rainfall variability, and salinity shifts, farmers have adapted with strategies such as adjusting planting times, adopting salt-tolerant rice varieties, altering cropping patterns, utilising freshwater resources, rainwater harvesting, relay cropping, deep tillage, mulching, and transforming agricultural practices to include livestock, shrimp, and fish culture.
# Research on Power Cyber-Physical Cross-Domain Attack Paths Based on Graph Knowledge
## 3 Method for Constructing Attack Graph of Electric Power Cyber-Physical Fusion System
### Define the Attack Graph  
Existing research lacks consideration of characteristics such as power system cyber-physical coupling degree and business hierarchical structure, and the applicability of quantitative assessment of attack path hazards in power cyber-physical fusion systems is not strong. By introducing quantitative index items, this paper proposes an attack path hazard quantification assessment method that takes into account the business characteristics of the power cyber-physical fusion system and comprehensively considers vertex vulnerability factors, geodesic distances, edge weights, and cyber-physical coupling relationships.  
First, define the attack graph based on graph theory knowledge and conduct the following discussion: Assume that in the attack graph G(V,A), V = {\(v\)|\(v\) \(\in\) leaf node or intermediate node or root node}. The directed edge set A = {a}, d\({}^{+}_{G}(v)\) and d\({}^{-}_{G}(v)\) are the out-degree and in-degree of the vertex v. dij is the geodesic distance between node i and node j, bi is the betweenness of node i, and bj is the betweenness of node j.  
Therefore, considering the vulnerability of vertices and the geodesic distance between vertices, the attack graph \(\overrightarrow{G}\) can be defined as Formula (1). Assume that the attack graph contains a total of m vertices: V is the set of all vertices, A is the set of all directed edges, the source set X contains all leaf nodes with in-degree 0, and the collection set Y contains all root nodes. C is the vulnerability factor of each vertex, represented by the set \(c_{i}\), where \(i\in[1,m]\).  
\[\overset{\rightarrow}{G}=(V,A,X,Y,C) \tag{1}\]  
The value of \(c_{i}\) represents the degree of vulnerability of the vertex. The value of \(c_{i}\) is determined by the out-degree, in-degree, betweenness, condition 1 and condition 2 of the vertex \(v_{i}\). The higher the frequency with which vertex \(v_{i}\) is exploited by the shortest attack path in attack graph \(\overset{\rightarrow}{G}\), the stronger the vulnerability of vertex \(v_{i}\) and the higher the \(c_{i}\) value.
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
## 3 Results
### Measurement Technique Comparisons  
#### 3.2.1 Isothermal Temperature  
To compare thermography control to thermocouple and pyrometer control, as well as, time dependent effects of degradation at temperature upon accuracy, a fifty-five hour 800 \({}^{\circ}\)C isothermal test was undertaken. A Nimonic 90 dog bone specimen was coated in HE23 and temperature was controlled by thermography averaging over an area. Measured values from the three techniques were compared at the top, centre and bottom locations of a 25 mm gauge section, Figure 12. Accuracy was found to be within \(\pm 2\)\({}^{\circ}\)C of when comparing all techniques at each location for the duration of the test with no indication of any adverse effects on the HE23 TP coating or accuracy of the non-invasive technique from oxidation, etc.
## 5 Interpretation of the Solution and Discussion
Curve \(1\) corresponds to (41) and curve \(2\) to (40). Indeed, in this case the shear strain rate may approach infinity at one of the contact surfaces, as it is seen from (39). The models that do not satisfy (17) have not been considered in the present paper. However, such models are widely used in applications, for example, the Herschel-Bulkley model [34]. It has been shown in [35] that it is always possible to find a solution satisfying the regime of sticking for such models. Therefore, the qualitative behavior of solution for the models that do not satisfy (17) is similar to that for the models satisfying (18) if the inequality in (40) is valid. It is believed that this qualitative difference provides a means for testing different models experimentally. However, it is necessary to design a special experimental program. The function introduced in (18) does not affect the behavior of solutions outside a very narrow layer near the friction surface and this function cannot be determined from standard tests. This is because the strain rate is bounded in any test. Let \(\xi_{\text{max}}\) be the maximum possible shear strain rate in experiment. Then, Equation (15) can be rewritten as  
\[\frac{\sigma_{\theta\varphi}}{k_{0}}=\left\{ \begin{array}{ll}\Phi\Big{(} \frac{\xi_{\theta\varphi}}{\xi_{0}}\Big{)}\,\text{if }\xi_{\theta\varphi}\geq\xi_{\text{max}}\\ \Phi_{0}\Big{(}\frac{\xi_{\theta\varphi}}{\xi_{0}}\Big{)}\,\text{if }\xi_{ \theta\varphi}\leq\xi_{\text{max}}\end{array} \right. \tag{48}\]  
where \(\Phi_{0}\big{(}\xi_{\theta\varphi}/\xi_{0}\big{)}\) is an arbitrary function of its argument. This function can be chosen to approximate the actual experimental data in the range \(0<\xi_{\theta\varphi}/\xi_{0}<\xi_{\text{max}}/\xi_{0}\) with any desirable/optimal accuracy. Then, the function \(\Phi\big{(}\xi_{\theta\varphi}/\xi_{0}\big{)}\) can be chosen to satisfy (18) with any value of \(\alpha\). It is also possible to choose this function such that (17) is not satisfied. A requirement is that \(\Phi(\xi_{\text{max}}/\xi_{0})=\Phi_{0}(\xi_{\text{max}}/\xi_{0})\) and \(\Phi^{\prime}(\xi_{\text{max}}/\xi_{0})=\Phi^{\prime}_{0}(\xi_{\text{max}}/ \xi_{0})\). It is also possible to require that higher derivatives are continuous. The choice of the function \(\Phi\big{(}\xi_{\theta\varphi}/\xi_{0}\big{)}\) does not affect the accuracy of approximation of the actual experimental data but affects the qualitative features of the theoretical solution. Some of these features are feasible for experimental verification using indirect methods. In metal plasticity, it has been already recognized that standard tests cannot be used to identify the constitutive equations in a narrow layer near frictional interfaces [36].
## 3 Overview of the Solution
### Ros-2-Connected Mode  
In the ROS-connected mode, the simulation does not save the cameras' data nor the ground-truth annotations to disk. Instead, ROS 2 for the Unity [44] library connects the application to the ROS system. It enables the simulator to receive and publish information simultaneously, emulating sensors and effectors from a real-world rover platform and accepting commands sent by ROS to control the rover.  
ROS 2 provides a modular and distributed architecture that integrates different components and sensors, making it a flexible platform for building complex robotic systems. It offers a rich set of libraries, tools, and algorithms for various tasks, such as navigation, perception, manipulation, and planning, which can significantly reduce development time.  
The simulation has been integrated with ROS 2 Humble, but there are currently no obstacles to upgrading it to newer versions in the future. This distribution was selected as the most recent version supporting modern Linux operating systems. It is also the basis of the recently proposed Space ROS system dedicated to space applications [15].  
The rover accepts steering information as standard geometry_msgs.msg_Twistmessages and, therefore, can be controlled using any capable ROS nodes. The publishing system sends the IMU (inertial measurement unit), stereo camera, depth sensor, and ground truth pose information with a configurable frequency, allowing the development and testing of perception and control algorithms. To make integration with other nodes straightforward, common ROS message types--such as sensor_msgs.msg.Imu, sensor_msgs.msg.Image or sensor_msgs.msg.CompressedImage, and geometry_msgs.msg.PoseStamped--are used. As mentioned before, the number, placement, and parameters of the sensors can be freely configured.
## 5 Experiment and Evaluation
### Experimental Setup
#### 5.1.2 Depth Map-Based Methods  
A depth map-based method is generally based on the normal vector in the depth image rather than the gradient in the color image to better describe the geometric features of the research object. In this study, we selected a representative method, Histogram of Oriented 4D Normals (HON4D), by computing the histogram of oriented 4D normals for activity recognition from depth sequences. The open-source code is the MATLAB version of the code, with the depth map of the NPU RGB+D dataset as the input. The HON4D descriptor is calculated as the feature, and SVM is used to classify the actions.
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 3 Agent-Based Model and Simulation
### ABM of Selfish Mining
#### 3.1.3 Honest and Selfish Mining  
Each block in the chain is of a certain height \(h_{b}\) (number of blocks directly chained together between block \(b\) and the genesis block). Further, at any point in time, \(t\) each miner has a local copy of the blockchain \(B_{i}(t)\). Importantly, honest miners deem the chain valid that has cumulatively spent most Proof-of-Work, which in the ABM, for simplicity, always corresponds to the longest chain. Thus, at time \(t\), the height of miner \(i\)'s local copy of a blockchain is defined as  
\[H_{i}(t)=\max_{b\in B_{i}(t)}(h_{b}) \tag{2}\]  
According to the longest chain rule, honest miner \(i\) will accept block \(b\) and add it to her local copy of the blockchain if the height of her local copy of the blockchain is less than the height of the block just received, namely if \(H_{i}(t)<h_{b}\). As a result \(H_{i}(t)\) increases to match block \(b\)'s height \(h_{b}\). Similarly, if miner \(i\) mines a block at time \(t\) (instead of receiving a new block), this also increases \(H_{i}(t)\). In addition, honest miners naively share blocks that they either just accepted or mined with all of their peers immediately. On the contrary, a selfish miner does not necessarily share a block with honest miners immediately. To decide when to broadcast which block, selfish miners keep track of the difference in height between the public and private chain, as well as the number of blocks they have attached to the private chain. We defined an algorithm that is executed by selfish miners following the logic of [PERSON]'s selfish mining specification in [2]. The full _pseudo-code_ was shown in our previous conference paper [7].
## 2 Disturbance Torque Model of the Sun-Tracking Solar Array
### Disturbance Torque Model  
The SADA discussed in this study is composed of a two-phase hybrid stepper motor in series with a harmonic reducer. A sketch of the SADA structure is shown in Figure 2.  
It is assumed that (1) the transmission efficiency of the harmonic reducer is 100%; (2) the stepper motor rotor, the wave generator, and the flexible spline of the harmonic reducer only have the rotational degree of freedom around the rotation axis.  
Based on the above assumptions, the dynamics model of SADA is [26]:  
\[\Big{[}f_{flex}-h^{2}(J_{r}+J_{avg})\Big{]}\ddot{\theta}_{l}=-hT_{e}-T_{l}-T_{f} \tag{17}\]  
where \(J_{flex}\), \(J_{r}\), and \(J_{avg}\) are respectively the moment of inertia along the rotation axis of the flex wheel, stepper motor rotor, and wave generator along the rotation axis; \(\theta_{l}\) is the rotational angular displacement of the solar array; and \(h\) is the reduction ratio of the harmonic reducer.  
The force analysis of each sub-structure in the SADA is carried out separately, as shown in Figure 3. The dynamic equations of the three subsystems of the rotor and wave generator assembly, the flexible wheel, and the SADA housing can be obtained according to the force analysis as follows:  
\[\left\{\begin{array}{c}\big{(}J_{r}+J_{avg}\big{)}\ddot{\theta}_{m}=T_{e}-T_{ 12}\\ J_{flex}\ddot{\theta}_{l}=T_{23}-T_{12}-T_{l}-T_{f}\\ J_{shell}\ddot{\theta}_{shell}=T_{e}-T_{23}+T_{f}-T_{res}\end{array}\right. \tag{18}\]  
where \(J_{shell}\) is the moment of inertia of the SADA shell; \(\theta_{shell}\) is the angular displacement of the SADA shell along the rotation axis, and its value is 0 because the SADA shell is fixed; \(T_{12}\) is the value of the equivalent action moment between the wave generator and the flexible wheel; \(T_{23}\) is the value of the equivalent action moment between the flexible wheel and the rigid wheel; \(T_{res}\) is the restrain moment given to the SADA by the spacecraft.  
The relationships between \(\theta_{m}\) and \(\theta_{l}\), \(\theta_{l}\), and \(\omega_{l}\) are given by  
\[\theta_{m}=-h\theta_{l},\ \omega_{l}=\dot{\theta}_{l}. \tag{19}\]  
It is obvious that the disturbance torque generated by the SADA on the spacecraft body is the counter-torque of the SADA's restrain torque. Therefore, according to Equations (17) and (18), the disturbance torque can be obtained as follows:  
\[T_{d}=-T_{res}=\Big{[}f_{flex}-h\big{(}J_{r}+J_{avg}\big{)}\Big{]}\ddot{\theta }_{l}+T_{l}=-hT_{e}+h(h-1)\big{(}J_{r}+J_{avg}\big{)}\ddot{\theta}_{l}-T_{f}. \tag{20}\]  
Figure 2: Structure sketch of the SADA.
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 1 Introduction
### Main Findings  
Image DNA techniques have been studied across three categories: bitmap-based, image processing-based, and deep learning-based methods. Each category demonstrates distinct trade-offs between performance and detection accuracy, making the choice of technique dependent on factors like service scale, user base, and detection accuracy requirements. However, no comprehensive quantitative comparison of these techniques has been conducted. We formalize the notion of image equivalence and define the requirements for image DNA techniques, particularly the real-time verification of uploaded images on large-scale social media and content service platforms. From this perspective, the originality of this study lies in its comprehensive, quantitative comparison of three existing categories, thoroughly analyzing their performance across varying datasets and conditions while evaluating their ability to meet these requirements.  
The main findings of this study are as follows:  
1. Each DNA technique is sensitive to threshold settings, which significantly impact its performance in detecting equivalent images, necessitating careful optimization of threshold values.
2. Bitmap-based methods offer fast DNA extraction and search with minimal computational requirements, demonstrating practical advantages for field deployment.
3. Image processing-based methods, particularly ORB (Oriented FAST and Rotated BRIEF) [11], provide superior detection accuracy due to their use of multiple feature vectors as DNA while maintaining reasonable overhead for DNA generation and search.
4. Deep learning-based methods perform poorly in both speed and detection accuracy, highlighting their limitations in addressing image equivalence compared to their strengths in classification and similarity tasks.
5. Our findings indicate that DNA DB build and search times depend heavily on DNA extraction time, emphasizing the need to prioritize extraction efficiency in practical large-scale filtering systems with high processing demands and frequent DB updates.  
These findings provide valuable insights into the strengths and limitations of current techniques and contribute to the design of effective image filtering systems for large-scale content platforms.
###### Abstract  
Background: The Cancer Genome Atlas (TCGA) Research Network revealed numerous clear cell renal cell carcinoma (ccRCC) gene mutations among which titin (TTN). The link between excessive amounts of visceral adipose tissue (VAT) and ccRCC pathogenesis is known. A relationship between VHL and TTN gene mutations and a CT-derived estimation of body composition in ccRCC patients has been evaluated. Methods: We retrospectively assessed patients from the TCGA-kidney renal clear cell carcinoma (KIRC) database for evaluation of clinicopathological and body composition analysis of ccRCC VHL and TTN gene mutations. Results: Gene expression levels and survival were assessed on a large cohort of 483 patients and 533 tumor samples. A statistically significant difference of VHL expression reduction in primary tumor (\(p<0.0001\)) and a TTN expression increase in primary tumor (\(p<0.0001\)) was shown. TTN high expression levels was associated with statistically significant lower KIRC patient survival at eight years (\(p<0.05\)). For body composition analysis, we included 54 male patients divided into two groups: ccRCC-VHL (\(n=41\)) and ccRCC-TTN (\(n=13\)) groups. Statistically significant differences between the two groups were obtained for total adipose tissue (TAT) (\(p<0.01\)), VAT (\(p<0.05\)), subcutaneous adipose tissue (SAT) (\(p<0.05\)) and total abdominal muscle (TAM) (\(p<0.05\)) areas. Conclusion: This study demonstrates a link between ccRCC TTN gene mutation and shorter patient survival. The reduction of the analyzed tissues might be a risk of cancer cachexia in ccRCC patients with TTN gene mutation.  
Keywords: adipose tissue; clear cell renal cell carcinoma; kidney cancer; muscle mass; TTN; VHL +
Footnote †: journal: Applied Sciences