esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### Reward System for the DQN Agent
#### 5.3.1 Reward Based on Heading Alignment  
The orientation-based reward mechanism focuses on the alignment between the robot and its goal, as illustrated in Figure 9. The heading, representing the angle between the robot's current orientation and the desired goal direction, plays a crucial role in determining the reward. A small heading value indicates that the robot is closely aligned with the goal orientation, resulting in a positive reward for the agent. Conversely, as the heading value increases, indicating a deviation from the desired direction, a negative reward is assigned to discourage the robot from moving further away from the intended path.  
By incorporating this orientation-based reward into the reinforcement learning framework, the agent is encouraged to continually adjust its heading towards the goal, improving its ability to navigate effectively and converge to the desired orientation.  
The heading reward plays a crucial role in evaluating the alignment between the mobile robot and its intended goal. It is an essential component of the reinforcement learning framework employed by the agent. The reward calculation takes into account two significant factors: the angle between the robot's orientation and the desired heading, and the number of available actions that the robot can perform.  
The primary purpose of the heading reward is to guide the agent towards making correct decisions and actions. By considering the angle in radians, the reward function assigns a higher value when the robot's orientation is closer to the desired heading (near zero degrees). In such cases, the agent receives a positive reward, reinforcing its successful  
Figure 9: Visual depiction of heading relationship between robot and goal.  
alignment with the goal. Conversely, as the angle increases, indicating a deviation from the desired direction, the reward decreases proportionally. This negative reward signals to the agent that it needs to adjust its heading to achieve better alignment.  
By incorporating the angle parameter and accounting for the number of actions, the heading reward provides an effective mechanism for training the mobile robot to navigate towards its goal with improved accuracy and precision.  
\[R_{\theta}=5/\theta \tag{10}\]  
For the calculation of the heading reward, the restrictions shown in (11)-(13) are defined.  
\[if\ -\frac{1}{2}\pi<\theta<\frac{1}{2}\pi, \tag{11}\]  
\[R_{\theta}\geq 0 \tag{12}\]  
\[else\ R_{\theta}<0 \tag{13}\]  
where \(\theta\) represents the heading; if this value is within the 180 degrees involved within the 5 actions established in Figure 8, the reward will be greater than or equal to zero, otherwise it would mean that the robot is moving away, so the reward would be negative or less than zero.
# Explainable AI Assisted IoMT Security in Future 6G Networks
## 5 Experiments and Results
### Implementation of Explainable AI Techniques: SHAP, LIME, and DiCE
#### 5.4.4 DiCE--Local Behavior Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Full Form** & **Value** & **Reason for Attack Prediction** \\ \hline SrcBytes & Source Bytes & 2306.7 & High number, possibly indicating abnormal data sent from the source, suggesting a DoS attack or data exfiltration attempt. \\ \hline DstBytes & Destination Bytes & 307.63 & Relatively high value could indicate excessive data received by the destination, which is often linked to malicious activity like data stealing or flooding. \\ \hline SrcLoad & Source Load & 14947.4 & High load on the source system, likely a result of malicious processes running, contributing to the attack classification. \\ \hline DstLoad & Destination Load & 1886.1 & High load on the destination system, indicating abnormal resource usage typical of an attack scenario. \\ \hline SrcJitter & Source Jitter & 229.99 & High jitter suggesting instability in the network, which is common during a network flooding attack. \\ \hline DstJitter & Destination Jitter & 298.94 & High jitter, indicative of network instability due to an attack, like a DDoS or data breach. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: List of features identified via DiCE that contribute to attack prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Full Form** & **Value 1** & **Value 2** & **Value 3** & **Reason for Normal Prediction** \\ \hline SrcBytes & Source Bytes & 1674.0 & 1664.0 & 1675.0 & Reduced value of SrcBytes indicates normal data sent from the source, reducing the likelihood of data exfiltration or DoS attack. \\ \hline DstBytes & Destination Bytes & 272.0 & 272.0 & 272.0 \\  & & & & & Lower DstBytes suggest that the data received by the destination are within the expected ranges, reflecting normal network behavior. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: List of features identified via DiCE that contribute to normal prediction. By analyzing DiCE features, key indicators such as traffic volume, system load, and network stability effectively distinguish between normal and attack scenarios. In 6G networks, with higher data rates and extensive device connectivity, these factors become critical for identifying security threats. A reduction in abnormal traffic volumes and network instability--indicated by lower SrcBytes and DstBytes and more stable SrcJitter and DstJitter--signals a stable, non-malicious network state, ensuring secure communication in 6G's high-speed environment. This analysis supports proactive risk monitoring, enhances response times, and optimizes resource allocation during security events.
# High-Performance Microwave Photonic Transmission Enabled by an Adapter for Fundamental Mode in MMFs
## 3 Experimental Measurement and RF Transmission Performance
The difference between the power value of FF and the noise floor is considered to be SFDR, in which the power value is obtained from the FF line based on the horizontal coordinate of the intersection of the IMD3 line and the noise floor. The formula for SFDR calculation is \(\frac{2}{3}(OIP_{3}-NF)\), in which OIP\({}_{3}\) is the intersection of the FF and IMD3 lines and NF stands for noise floor [16; 17]. The IMD3 grows with the  
Figure 4: (**a**) The output optical power vs. input optical power. (**b**) Detected FF and IMD3 powers in SMF photonic links when input optical power is 8 dBm, as an example of measuring SFDR. (**c**) SFDR vs. input optical power. normalized optical input power and the optical spectral components that result in IMD3 include not only the optical carrier but also the modulation sidebands and FWM sidebands. Despite the large A\({}_{\text{eff}}\) of the MMF and the Kerr effect is relatively weak, IMD3 in the MMF link is stronger at high input powers This is because, compared with the SMF, the MMF has a weaker SBS, which means the transmitted power can be stronger. With decreased IMD3 power and higher FF power, we obtain an improvement of 14.5-dB in SFDR based on MMFs, as presented in Figure 4c, which is measured at the input optical power of 17 dBm, a power value that is widely used in MPLs.
## 3 Results and Discussion
### Effect of NP Number Density on the Oil-Water IFT  
Core flooding of Berea sandstone was conducted using brine and silica NP dispersions of different diameters (7 nm CSNP, 12 nm CSNP and FSNP, and 22 nm CSNP). All NPs used were identically surface-modified with PSS-\(\omega\)-MA and a zwitterionic surfactant. Oil recovery after the injection of silica NPs was greater than the recovery afforded by brine flooding. Oil recovery increased with decreasing particle size; the recovery factor was 78.2% for the 7 nm CSNP dispersion, 75.1% for the 12 nm CSNP dispersion, and 74.2% for the 22 nm CSNP dispersion (Figure 9). These findings may reflect the increase in total NP surface area with decreasing particle size at a fixed particle concentration, such that the NP area in contact with surfactant increases. NP size affected the IFT reduction and the contact angle (Figures 6 and 7). Injection of a 12 nm FSNP dispersion afforded poorer recovery, compared with the CSNP dispersions, thereby reflecting FSNP aggregation that compromised oil recovery. The results of the two FSNP injection experiments showed a significant difference in the recovery factor, although it was performed under the same conditions; the recovery factor of the first experiment is much lower than the second experiment. This may be because of the difference in oil trapping caused by the occurrence of FSNP aggregation. Once massive pore blockage occurs and permeability declines, solid particles can no longer penetrate the formation due to narrowed pores and a buildup of external porous media [42]. In some cases, excessive NP aggregation may cause maximized permeability reduction and it can cause oil to be trapped inside the pore. Based on all the results of the experiments, NP size and type (CSNP or FSNP) affected the oil/water IFT and rock surface wettability, which influenced ultimate oil recovery.  
Figure 8: Apparent viscosities of (**a**) NPs and (**b**) NP-stabilized emulsions as a function of shear rate.
###### Abstract  
In this paper, we explore the benefits of using a magnetostrictive component in a variable reluctance energy harvester. The intrinsic magnetic field bias and the possibility to utilize magnetic force to achieve pre-stress leads to a synergetic combination between this type of energy harvester and magnetostriction. The proposed energy harvester system, to evaluate the concept, consists of a magnetostrictive cantilever beam with a cubic magnet as proof mass. Galfenol, Fe\({}_{81.6}\)Ga\({}_{18.4}\), is used to implement magnetostriction. Variable reluctance is achieved by fixing the beam parallel to an iron core, with some margin to create an air gap between the tip magnet and core. The mechanical forces of the beam and the magnetic forces lead to a displaced equilibrium position of the beam and thus a pre-stress. Two configurations of the energy harvester were evaluated and compared. The initial configuration uses a simple beam of aluminum substrate and a layer of galfenol with an additional magnet fixing the beam to the core. The modified design reduces the magnetic field bias in the galfenol by replacing approximately half of the length of galfenol with aluminum and adds a layer of soft magnetic material above the galfenol to further reduce the magnetic field bias. The initial system was found to magnetically saturate the galfenol at equilibrium. This provided the opportunity to compare two equivalent systems, with and without a significant magnetostrictive effect on the output voltage. The resonance frequency tuning capability, from modifying the initial distance of the air gap, is shown to be maintained for the modified configuration (140 Hz/mm), while achieving RMS open-circuit coil voltages larger by a factor of two (2.4 V compared to 1.1 V). For a theoretically optimal load, the RMS power was simulated to be 5.1 mW. Given the size of the energy harvester (18.5 cm\({}^{3}\)) and the excitation acceleration (0.5 g), this results in a performance metric of 1.1 mW/cm\({}^{3}\)g\({}^{2}\).  
vibration energy harvesting electromagnetic; magnetostriction; galfenol; variable reluctance; non-linear dynamics +
Footnote †: journal: Applied Sciences
## 2 Classical P&O and dp-P&O  
The classical P&O algorithm is presented in Figure 1. This method has been widely employed due to its simplicity, relatively good performance, and generic implementation. The actions are executed for every discrete k-sampling period (usually at a given fixed interval \(T_{\text{MPPT}}\)) to detect the PV-curve slope and determine the MPP's location. In what follows, subindex k points out the specific value for each iteration period, e.g., \(v_{k}\) and \(p_{k}\) are the voltage and power of the PV module at the k-th iteration. P&O detects the slope by measuring the power difference (\(\Delta p\) = \(p_{k}-p_{k-1}\)) after adding or subtracting a fixed size perturbation (\(\delta\)) to the operating point (usually in voltage). If \(\Delta p\) is positive, it is deduced that the perturbation moved the operating point closer to the MPP (uphill move) and that \(\delta\) has to have the same sign for the next one, i.e., \(\Delta p/\delta>0\) on the left side of the MPP and \(\Delta p/\delta<0\) on the right side of the MPP.
## 4 Experiment and Analysis
### Frequency and the Bright Stripe's Duty-Ratio Detection Error Analysis  
When the modulation of the frequency of the LEDs increases, the stripe will become dense at the same distance, which means the error of duty ratio of bright stripe is large. If the duty ratio of the strip can be correctly extracted, the number of the strip can be accurately obtained. However, accurate identification of the number of bright stripes does not guarantee the exact duty cycle. Therefore, in the case of a duty-ratio of 50%, we swept the frequency from 0.1 to 10 kHz in 500 Hz steps and evaluated the ability of the duty-ratio regarding bright stripe detection.  
On the other hand, the performance of different modulation frequencies varied greatly at different distances. In practical applications, distance should match the appropriate frequency as far as possible. Therefore, it is necessary to explore the relationship between distance, frequency, and recognition rate.  
The relationship between the transmission frequency and the absolute value of the bright stripe's duty-ratio recognition is shown in Figure 15.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The focal length (mm) & 4.25 \\ Aperture & F1.7 \\ Resolution & 4032 \(\times\) 2448 \\ Exposure time (s) & 1/28,310 \\ ISO & 100 \\ The diameter of the LED downlight (cm) & 6 \\ The power of each LED (W) & 9 \\ Current of each LED (mA) & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter of the Experiment.  
Figure 15: The recognition rate at different distances with different frequencies.  
As can be seen from Figure 15, when the system is used in practice, the transmitter with a relatively low frequency should be preferred when the receiver is close to the transmitter, and the transmitter with a relatively high frequency should be preferred when the receiver is far away from the transmitter. Moreover, it can be seen that 8 kHz is a high frequency critical point; higher than 8 kHz, and the recognition rate has dropped dramatically, and the system will fail.
###### Abstract  
Regions of temperate oceanic climate have historically represented a challenge for the application of satellite-based multi-temporal SAR interferometry. The landscapes of such regions are commonly characterized by extensive, seasonally-variable vegetation coverage that can cause low temporal coherence and limit the detection capabilities of SAR imagery as acquired, for instance, by previous ERS-1/2 and ENVISAT missions. In this work, we exploited the enhanced resolution in space and time of the recently deployed Sentinel-1A/B SAR satellites to detect and monitor ground motions occurring in two study areas in the Republic of Ireland. The first, is a \(\sim\)1800 km\({}^{2}\) area spanning the upland karst of the Clare Burren and the adjacent mantled lowland karst of east Galway. The second, is an area of 100 km\({}^{2}\) in Co. Meath spanning an active mine site. The available datasets, consisting of more than 100 images acquired in both ascending and descending orbits from April 2015 to March 2018, were processed by using the Permanent Scatterer approach. The obtained results highlight the presence of small-scale ground motions in both urban and natural environments with displacement rates along the satellite line of sight up to \(-\)17 mm/year. Localized subsidence was detected in recently built areas, along the infrastructure (both roads and railways), and over the mine site, while zones of subsidence, uplift, or both, have been recorded in a number of peatland areas. Furthermore, several measured target points indicate the presence of unstable areas along the coastline. Many of the detected movements were previously unknown. These results demonstrate the feasibility of adopting multi-temporal interferometry based on Sentinel-1 data for the detection and monitoring of mm-scale ground movements even over small areas (\(<\)100 m\({}^{2}\)) in environments influenced by temperate oceanic climate.  
Permanent Scatterer; Sentinel-1; InSAR; Ireland; peatland; temperate oceanic climate +
Footnote †: journal: Remote Sensing, 11, 348
## 4 Analysis of the Factors Affecting on the Gathering Process and Self-Preservation of the STACER  
For the FE modelling, the effects of friction coefficient, radius of the top rod, and acceleration on the mechanical characteristics of STACERs have been studied during its packaging process. A plane test method was carried out for the friction coefficient measurement. It was to contact two measured surfaces in a horizontal plane. A certain pressure was applied and the surface was dragged with a constant speed in the horizontal direction. Then the friction coefficient was obtained from a calculation. From the measurements, the friction coefficient ranged from 0.08-0.12. Considering the influences of the environment, such as high temperature or low temperature, in space the friction coefficient is selected as 0.1. Due to the layout requirement, only a maximum radius of 9.3 mm is allowed for the top rod application in a STACER. The only physical hardware of top rod is at a radius of 9.3 mm currently. In the simulation, the extended radius range was selected from 8.7 to 9.3 mm for the top rod investigation. Other specifications still need to be developed in further studies. This study provides a guidance for the effect of top rod radius on its application performance. It also can be used to evaluate the design rationality for the top rod with different radius. The acceleration data of 100 G with different directions in this work is also based on a comprehensive consideration of the rocket's ability, vibrations, and the severe dynamic impact on the STACER with a large safety factor during the launching process from engineering experience. During the gathering process, the spring reel is drawn downwards by the top rod, so that the reel can be completely stored. According to the design requirements, there are three options for the radius size of top rod. It is necessary to study the influence of the top rods size on the gathering process of the spring reel so as to determine the optimum size. In order to investigate the factors affecting on the self-preservation of a gathered STACER, the acceleration and friction coefficient between layers of the spring reel are studied to explore the stress distribution and topography of STACER under different boundary conditions.
## 2 Named Data Networking: Nuts and Bolts
### Security Principles in NDN
#### 2.1.1 Digital Signatures  
Digital signatures in NDN represent the basic building blocks of object security. They enable securing data regardless of their location or transmission channel. The signature field is present in every _Data_ packet and serves to bind the content of the packet to its name [14]. It consists of two components, namely SignatureInfo and SignatureValue. The former contains the producer's public key name and the cryptographic algorithm used to sign the _Data_, while the latter represents the signature's generated bits. The SignatureInfo field may also include KeyLocator, which includes information regarding the location of parent certificate(s). The signed hash of a _Data_ packet covers all its fields except the SignatureValue, as illustrated in Figure 1.  
While NDN does not mandate the use of signatures in _Interest_ packets, they may be required in some scenarios where their authenticity is necessary, such as sending a new route announcement or a command packet to an IoT device. Compared to the signature field in _Data_ packets, the _Interest_ packet's signature field includes additional components, such as SignatureNonce, SignatureTime, and SignatureSeqNum, to add uniqueness to the signature.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 2 Related Work
### Task Offloading in UAV Networks
The decisions are made based on the agent's projected behavior, which suggests whether or not edge cloud is advantageous to the incoming tasks. To make a prediction about future device load, they either use a model from the time-series class or a model from the class of ML regressors. [PERSON] et al. [21] propose a task offloading framework with light user mobility prediction utilizing machine learning techniques. Based on past data, they predict the user's future location and may decide on the task offloading at an edge server which is close to the location instead of local task processing. They compare their task offloading algorithm with two other mobility based algorithms from the literature.
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 1 Introduction
Compared to pixel-domain-based algorithms, compressed-domain methods generally require less computational resources because analyzing input information is already possible in the bitstream. In this study, we propose a compressed-domain-based moving object detection that applies a data clustering and an intersection over union (IoU) rate-based object tracking technique of computer vision. Using only MVs, which are provided by video encoders in a compressed bitstream, our approach is able to efficiently detect moving objects. Furthermore, an experimental evaluation of our method is  
Figure 1: Overview of the cloud-based video analytics server. provided to compare it with the state-of-the-art compressed-domain tracking methods in terms of processing time and demonstrate its functionalities to evaluate the efficiency of the proposed edge device with a cloud video analytics server in a real-world scenario. In summary, this paper makes the following contributions. * Introduction of a compressed-domain moving object detection method that can be applied in numerous surveillance applications. * Design of an edge-to-cloud computing system for surveillance video analytics that applies the proposed method at edge devices to minimize the transfer of video data from surveillance camera feeds to the cloud. * Implementation and evaluation of the proposed method with an edge-cloud system. The implementation is lightweight and easy to deploy at any edge devices. The remainder of this paper is organized as follows. Section 2 describes the background knowledge of video coding MV and the related state-of-the-art algorithms for moving object detection in both the compressed-domain and pixel-domain. Section 3 describes the proposed system based on the video coding MV analysis method for moving object detection. Section 4 presents the implementation and evaluated results. We summarize the contribution of this study and present our future directions in Section 5.