esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 4 Experimental Results
### The Performance of the Proposed Method
#### 4.1.2 Performance Comparison of Various \(\tau_{1}\)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multirow{2}{*}{**AMBTC**} & \multicolumn{4}{c}{**PSNR (dB)**} & \multicolumn{4}{c}{**Bitrate (bpp)**} \\ \cline{3-8}  & & **bpp = 2.0** & \(\tau_{1}\)** = 16** & \(\tau_{1}\) = 32 & \(\tau_{1}\) = 64 & \(\tau_{1}\) = 16 & \(\tau_{1}\) = 32 & \(\tau_{1}\) = 64 \\ \hline Lena & 33.24 & 36.05 & 34.62 & 32.85 & 1.68 & 1.46 & 1.33 \\ Jet & 31.97 & 35.98 & 34.79 & 32.50 & 1.56 & 1.39 & 1.24 \\ Baboon & 26.98 & 30.89 & 29.55 & 25.74 & 2.75 & 2.34 & 1.75 \\ Tiffany & 35.77 & 37.55 & 35.72 & 34.26 & 1.54 & 1.34 & 1.25 \\ Boat & 31.16 & 34.27 & 32.87 & 30.81 & 2.09 & 1.77 & 1.57 \\ Stream & 28.59 & 32.53 & 30.20 & 27.27 & 2.73 & 2.11 & 1.66 \\ Peppers & 33.42 & 35.71 & 34.49 & 32.80 & 1.77 & 1.59 & 1.49 \\ House & 30.89 & 34.82 & 32.00 & 30.42 & 2.00 & 1.65 & 1.38 \\ Average & 31.50 & 34.73 & 33.03 & 30.83 & 2.02 & 1.71 & 1.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PSNR and bitrate of the proposed method for various \(\tau_{1}\) (block size \(4\times 4\)). Figure 7: Bitrate–PSNR curves of each of the eight test images. It also can be seen from Figure 7 that the PSNR and bitrate vary as the threshold \(\tau_{1}\) changes. A larger \(\tau_{1}\) gives a lower bitrate with lower PSNR. In contrast, a smaller \(\tau_{1}\) offers a higher image quality, but the bitrate is also higher. Therefore, the selection of threshold \(\tau_{1}\) depends on real applications. For example, if an application requires higher image quantity, a smaller \(\tau_{1}\) is required. It is worth noting that, for most of the test images, the proposed method provides better performance than AMBTC, particularly for smooth images. For example, Lena, Jet, Tiffany, Boat, and Peppers are considered to be smooth. For these smooth images, regardless of the value of \(\tau_{1}\), the PSNR is always higher and the bitrate is always lower than those of the AMBTC method. In contrast, for the complex images such as Baboon or Stream, few blocks are classified as flat, which require only 8 bits to record them. Therefore, the reduction in bitrate is limited. Nevertheless, the proposed method either provides a better image quality or lower bitrate than those of AMBTC.
## 3 Model Description
### Motion Information Detection Channel
#### 3.1.2 Large Monopolar Cell (LMC)  
As presented in Figure 1b, the large monopolar cell (LMC) is a specialized type of neuron that receives information from the ommatidia cells and is highly sensitive to variations in light intensity [16; 17]. The dynamic movement of an object through a pixel in the image results in a progressive change in the pixel's brightness across time \(t\). In the proposed model, LMCs serve as information processors, performing time-frequency analysis on the output of the ommatidia cells to extract the brightness variation information that is crucial for the visual system (see Figure 4a). That is,  
\[L(x,y,t)=\int P(x,y,s)H(t-s)ds. \tag{3}\]  
\(L(x,y,t)\) represents the LMC response function and \(H(t)\) is expressed as  
\[H(t)=\Gamma_{n_{t},\tau_{1}}(t)-\Gamma_{n_{2},\tau_{2}}(t) \tag{4}\]\[\Gamma_{n,\tau}(t)=\begin{cases}(nt)^{n}\dfrac{e^{\frac{-nt}{\tau t}}}{(n-1)!\cdot \tau^{n+1}}&\text{for}\;\;t\geq 0\\ 0,&\text{for}\;\;t<0,\end{cases} \tag{5}\]  
where \(n\) and \(\tau\) denote two parameters of Gamma function [59].  
Figure 4: (a) \(\mathcal{CI}\)STMD model working diagram. Visual information is processed through a cascade of different types of neurons in the nervous system, ultimately leading to visual perception. (**b**) The left  
image shows the motion of a small object and a small-target-like object in a complicated background. The contrast between the small target and the background exhibits temporal variation, while the small-target-like object, being hidden in the background, maintains almost constant contrast with the background. The upper right figure displays that the STMD neurons generate different contrast response curves to time-varying and time-fixed contrast features. The lower right figure displays the variances of different contrast responses, in which the variance curve of the time-varying contrast target is higher, and the variance curve of the time-fixed contrast target is lower. Therefore, we can select contrast response variance as a criterion to help us distinguish the small target and false positives.
## 7 Simulation Results and Discussion
### Experimental Scenario (3): The Impact of Transmission Power  
This scenario was conducted for various transmission powers of 1, 2, 3, 4 and 5 mW that can cover the transmission ranges of 170, 270, 300, 353 and 390 m, respectively. The network density was set with 50 nodes (i.e., small scale network) and each node mobility speed at 10 mps. The QoS metrics in terms of PDR, throughput, and NRL were also estimated and analyzed to examine the performance of AFB-GPSR in contrast to conventional GPSR at fixed beacon times of 1 s and 2 s.  
As shown in Figures 16 and 17, the PDR and throughput are increased dramatically as the transmission power increases. Increasing transmission power indicates that nodes can communicate over longer distances and form stronger links with neighbor nodes, which enhance transmission efficiency. In other words, increasing transmission power improves network reliability, allowing nodes to maintain a wider geographical area for data communication. This will definitely allow the nodes to quickly find their positions and make more efficient routes.  
As noted above, AFB-GPSR significantly outperforms the GPSR-1s and GPSR-2s since it depends on dynamic beaconing time that updates the neighbors' table information properly with node movements with different transmission power values. Furthermore, in Figure 18, the proposed AFB strategy is able to reduce NRL as the transmission power increases. In this case, it means that the neighbors' connections become more robust and reliable when the transmission power is increased. Thus, longer link lifetime minimizes the chances of link failures, route interruptions and frequent topology changes. As a result, the network nodes can send more reliable data through optimal routes that were generated due to optimal broadcasted beacons at the higher transmission power value.  
Figure 14: End-to-end delay vs. node speed.  
Figure 15: Throughput vs. node speed.  
On the other hand, the NRL cost in AFB-GPSR has registered a lower value than the corresponding values of GPSR-1s and GPSR-2s when transmission power increases. This means the AFB dynamic beaconing strategy can allow the nodes to send their beacons only when necessary, such as when the topology changes or when a node has to update its neighbor information. Consequently, the control overhead associated with broadcasting beacons is reduced because the nodes are able to adapt to the updated beacons instead of fixed beaconing intervals. It is noticed that the GPSR with a 1 s beaconing time achieves a higher cost in NRL, where more frequent beacon messages by the nodes need more processing and forwarding times as the control packets increase.  
Figure 16: Packet-delivery ratio vs. transmission power.  
Figure 17: Throughput vs. Transmission Power.  
Figure 18: Normalized routing load vs. transmission power.
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 4 Experimental Validation
### Features Extraction
**SMS-Quantity (user level feature: dynamic)** This feature describes the number of sending SMS by the Trojan and its injected applications. It checks whether the Trojan is sending messages or not in the background, which gets the user's information without their knowledge. * **Email-sent (user-level feature: dynamic)** This feature describes that the Trojan sends the email in the background and gets the users' information without their knowledge. It also monitors the number of sent Emails. The value of this feature is in Boolean. * **Email-Quantity: (user level feature: dynamic)** This feature describes the number of sent emails and the injected applications of Trojans. It also checks that the email is sent in the background and gets information without the user's knowledge. The value of this feature is also in numbers. * **Sys-Call-Invoked (package level feature: dynamic)** This feature checks the system and whether the application invokes the system calls, e.g., turning off the ringtone, putting the phone into airplane mode, turning on the user location, etc. It checks whether or not the event in the background is getting the users' information without their knowledge. The value of this feature is in Boolean. * **Sys-Call (application-level feature: static)** This feature describes system calls invoked by the application.
## 6 Development Length of a gPTT Fluid  
### Simulations  
We performed a large number of simulations by considering \(Wi=0.1\), \(0.2\), \(0.3\), \(0.4\), \(0.5\), \(0.6\), \(0.7\), \(0.8\), \(0.9\), \(1\), creeping flow (\(Re=0.001\)), and the following combination of \(\alpha\) and \(\beta\) parameters:  
* \((\alpha,\beta)=(0.5;0.5)\)-Meshes \(M_{1}\), \(M_{2}\), \(M_{3}\), \(M_{r}\)-\(Wi=0.1,0.2,0.3,0.4\)
* \((\alpha,\beta)=(0.5;1.5)\)-Meshes \(M_{1}\), \(M_{2}\), \(M_{3}\), \(M_{r}\)-\(Wi=0.1,0.2,0.3,0.4\)
* \((\alpha,\beta)=(1.5;1.5)\)-Meshes \(M_{1}\), \(M_{2}\), \(M_{3}\), \(M_{r}\)-\(Wi=0.1,0.2,0.3,0.4\)
* \((\alpha,\beta)=(1.0;1.0)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.1,0.2,0.3,0.4\)  
Figure 7: (**a**) Comparison between the development length obtained with the gPTT model (with \(Wi=0.001\)), a Newtonian fluid, and the correlation proposed by [PERSON] et al. [4], for three different meshes \(M_{1}\), \(M_{2}\), and \(M_{3}\). (**b**) Velocity profiles in the fully developed region for mesh \(M_{3}\), considering the gPTT and Newtonian models, for \(Re=0.001\) and \(Re=100\).  
\((\alpha,\beta)=(0.5;0.5)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.5,0.6,0.7,0.8,0.9,1\)  
\((\alpha,\beta)=(0.5;1.5)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.5,0.6,0.7,0.8,0.9,1\)  
\((\alpha,\beta)=(1.5;0.5)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.5,0.6,0.7,0.8,0.9,1\)  
\((\alpha,\beta)=(1.5;1.5)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.5,0.6,0.7,0.8,0.9,1\)  
\((\alpha,\beta)=(1.0;1.0)\)-Meshes \(M_{2}\), \(M_{r}\)-\(Wi=0.5,0.6,0.7,0.8,0.9,1\)  
This gives a total number of 132 simulations. The simulations with the finest mesh took about 15 h each.  
The first set of 72 simulations allowed conclusions to be drawn about the convergence of the numerical method and the error in calculating the development length using the Richardson extrapolation technique. Based on the results of these simulations, a second set of simulations was performed for higher values of \(Wi\) using meshes \(M_{2}\) (see Figure 4) and \(M_{r}\) (see Figure 5). These meshes were chosen based on a trade-off between accuracy and computational time.
## 8 Vulnerability Assessment of NRF to API Injection Tests
### General Security Considerations Drawn from API Injection Tests  
Several key observations about common weaknesses emerged from the tests carried out in the two testbeds.  
Both OAI and Open5 GS have issues related to input sanitization, allowing for the acceptance of invalid or malicious parameters. Moreover, the inconsistencies in API responses and handling of HTTP methods point to a lack of proper API specification adherence and input validation: this absence of standardization in API responses could lead to interoperability issues when integrating NFs from different vendors. Despite the 3 GPP specification's requirement for messages that are not valid according to the protocol to be rejected or discarded by the NF, both testbeds exhibited weaknesses in this area. Finally, the absence of proper TLS encryption within the SBI of the testbeds exposes vulnerabilities that could be exploited for various attacks.  
In conclusion, while the open-source 5G projects Open5 GS and OAI offer promising features for core network deployment, the API injection tests revealed security vulnerabilities that require attention. The results underscore the need for robust input validation, proper handling of invalid inputs, and adherence to standardized API responses to ensure the security and stability of 5G networks.
## 3 Architecture of the Satellite-Enabled IoRT Network  
Because satellites can acquire observations with global coverage under all weather conditions, satellite-based IoRT networks could revolutionize the rapid retrieval of in situ data in remote areas of interest. The proposed IoRT network comprises three main parts: ground IoT terminals, an IoT satellite constellation, and a data center. The physical architecture of the proposed satellite-enabled IoRT network is depicted in Figure 1.  
(1) Ground IoT terminals, each of which comprises monitoring devices and a satellite-based transceiver, are installed in remote areas of interest for real-time monitoring and rapid data transmission. A traditional environmental observation device is employed to monitor the environment, and the monitoring data are transmitted via a satellite-based transceiver.  
Figure 1: Implementation architecture of the Xingyun satellite-enabled Internet of Remote Things network for data retrieval from the Tibetan Plateau (TP). Five satellite-enabled ground IoT terminal devices are installed in the far-most areas of the TP, i.e., the Mugagangqiong glacier area, Jialabalei glacier area, Dadongshuyakou snow-covered area, and Dayekou forest. These devices monitor the local environment and upload remotely sensed data to IoT satellites during overpass. The satellites then transmit the uploaded data to the TP scientific data center via a gateway station and the Internet. The scientific data center finally publishes a monitoring dataset on an open-access web platform.  
Notably, these ground IoT terminals can integrate a diverse range of monitoring devices, such as automatic weather stations (AWSs).  
(2) China's first IoT satellite constellation is used to establish the satellite-based IoRT network for field data transmission from the ground IoT terminals to the data center. The satellite communication links include an uplink from the ground IoT terminal to a satellite, an intersatellite link within the satellite constellation, and a downlink from the satellite to the data center. The monitoring data cached in the ground IoT terminals are transferred to the IoT satellite via uplink and then transferred to the data center via a ground gateway station and the Internet.  
(3) A data center is established as a platform to receive, manage, analyze, and publish the monitoring data obtained from the most remote areas of the TP.
# The Online Identification of the Behaviour of Pollutants inside the Tunnel Tube
## 3 Methods of Recursive Identification
### Recursive Least Squares Method with Directional Forgetting  
A recursive least squares method with directional forgetting (DF), which forgets only in the direction of new information, was derived to ensure stability. This method has better convergence properties. The estimate forgets old information and ensures convergence of the estimates while preventing significant parameter changes. It is suitable for identifying systems with parameters that do not change abruptly.  
\[\begin{split}\hat{\theta}(k+1)&=\hat{\theta}(k)+P(k )w(k)\varepsilon(k)\\ \varepsilon(k+1)&=y(k)-w^{T}(k)\hat{\theta}(k)\\ P(k+1)&=P(k)-\frac{P(k)w(k)w^{T}(k)P(k)}{\tau^{-1}(k)+w^{ T}(k)P(k)w(k)}.\end{split} \tag{8}\]  
Figure 5: The influence of the disturbance variable on the output for the exponential forgetting method.  
The equation gives the directional forgetting factor:  
\[\tau(k)=\lambda^{\prime}-\frac{1-\lambda^{\prime}}{w^{T}(k)P(k)w(k)}, \tag{9}\]  
where \(\lambda^{\prime}\) can be chosen as in the algorithm with exponential forgetting [19].  
Figure 6 shows the parameter estimate trajectories obtained with the recursive least squares method with directional forgetting.  
The analysed behaviour exhibits more minor oscillations for small parameter changes, which is the main criterion for accurately tracking parameter changes in a system over time for proper model updating. For comparison, the plots also depict the behaviour for significant parameter changes in the system. Compared to the previous algorithm, the estimates are smoother with less oscillation, but it takes longer for the parameters to be accurately estimated.  
The influence of the disturbance quantity on the output is shown in Figure 7.  
The influence of disturbances acting on the output quantity of the identified system will cause a slight fluctuation in the values of the estimated parameters.  
Figure 6: Estimating system parameters using the method with directional forgetting (when changing parameters).  
Figure 7: The influence of the disturbance on estimated parameters.
# How to Optimize the Implementation of ITIL through a Process Ordering Algorithm
## 2 State of Art
### Non-Fixed Optimal Ordering Algorithm  
As it has been shown before, there is no any specific approach offering a methodology, neither an algorithm to define the complete order of processes when implementing the ITIL. This means: (a) No algorithm for non-fixed sequences has been published; and (b) no algorithm for an optimal sequence specifically adapted for each company has been published.  
This conclusion leads us to the define the algorithm presented in the next paragraph, where we present how to optimally order the ITIL processes for a specific company depending on: (a) The level of implementation of the ITIL it has already reached; and (b) on the characteristics of the company.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 1 Introduction  
### Motivation  
Jobs in the service sector are gradually being occupied by robots because of their superior capabilities over humans, such as tireless work, low cost, robust and high precision. In order for robots to be flexible and meet work needs, service robots are often equipped with a movable platform. Due to many objective factors in the robot workspace, such as obstacle-rich workspace, environments with human presence, their mobility in indoor or outdoor workspaces is greatly limited without a matching navigation system. Navigation systems instruct a robot to go to a desired location safely, as they are capable of helping the robot respond to changes in the operating environment during travel. With the requirements of increasingly capable robots, navigation systems must also be complete and more accurate. Therefore, many navigation schemes have been rapidly developed depending on a particular environment and the structure of the robot. Currently, navigation systems for robots that operate indoors can navigate the robot move to a target with a given map. However, robots do not even have the ability to react to dynamic environments or in the presence of humans. Developing an autonomous robot that works in dynamic environments requires navigation systems that are able tocollect information from the environment surrounding the robot, simultaneously process the given map and continuously re-plan motion for the robot in order to safely reach the target. Most currently used navigation systems for service robots can be roughly categorized into two main types: global path planning and obstacle avoidance, which are introduced in detail in this study [1]. With a given map, path planning traces a trajectory from the robot to the desired position that meets the safety and kinematic constraints of the robot. In addition, the path-planning method needs to be optimized to some criteria. However, in the presence of unforeseen obstacles, the robot may not be able to reach the desired target. On the other hand, obstacle-avoidance methods help a robot identify obstacles by using sensor information and updates the direction of movement to avoid obstacles and safely reach the target. In complex environments with many obstacles, an obstacle-avoidance method will be ineffective if the target is far away from the robot. This study proposes a navigation solution for a service robot that works in our laboratory. The robot has a two-wheel differential drive platform for movement, light detection and ranging sensor (LiDAR) mounted in the platform for vision.
###### Abstract  
In the last 10 years, development in robotics, computer vision, and sensor technology has provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and high spectral resolution with unmanned aerial vehicles (UAVs). This development has led to a revolution in geospatial data collection in which not only few specialist data providers collect and deliver remotely sensed data, but a whole diverse community is potentially able to gather geospatial data that fit their needs. However, the diversification of sensing systems and user applications challenges the common application of good practice procedures that ensure the quality of the data. This challenge can only be met by establishing and communicating common procedures that have had demonstrated success in scientific experiments and operational demonstrations. In this review, we evaluate the state-of-the-art methods in UAV spectral remote sensing and discuss sensor technology, measurement procedures, geometric processing, and radiometric calibration based on the literature and more than a decade of experimentation. We follow the 'journey' of the reflected energy from the particle in the environment to its representation as a pixel in a 2D or 2.5D map, or 3D spectral point cloud. Additionally, we reflect on the current revolution in remote sensing, and identify trends, potential opportunities, and limitations.  
imaging spectroscopy; spectral; unmanned aerial vehicles; unmanned aerial systems (UAS); Remotely Piloted Aircraft Systems (RPAS); drone; calibration; hyperspectral; multispectral; low-altitude; remote sensing; sensors; 2D imager; pushbroom; snapshot; spectroradiometers +
Footnote †: journal: Remote Sensing, 10, 1091; doi:10.3390/rs10071091
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
###### Abstract  
Power technology has long been the main problem that has plagued the realization of ocean exploration by autonomous underwater vehicles (AUVs). This paper introduces a new wave energy conversion device for AUV, which is sealed inside a closed floating body to avoid interaction with the marine environment. The system uses the gyroscopic effect to continuously convert the pitching motion of waves into electrical energy through flywheel rotation, and thus theoretically extend the endurance time of AUVs. In this paper, a mathematical model of the power generation device is established, and the effects caused by different parameters on the system behavior and energy output are analyzed. In order to reduce the cost of experiments, the energy conversion device is installed on an experimental platform that can simulate wave motion to observe its energy generation performance. The experimental results show that the established mathematical model can accurately reflect the real behavior of the power generation device on the platform under different wave conditions, and the energy output error is only 9.91%.  
marie energy; wave energy converter; experimental validation; gyroscope; AUV