esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to their robust antioxidant properties, phenolic acids and their analogs are extensively studied for their ability to activate cellular antioxidant pathways, including nuclear factor (erythroid-derived-2)-like 2 (Nrf2)-antioxidant response element (ARE) pathway. Caffeic, ferulic, and gallic acid are well-studied members of phenolic acids. Constant efforts are made to improve the pharmacological effects and bioavailability of phenolic acids by synthesizing their chemical derivatives. This study determines how modifications of the chemical structure of these phenolic acids affect their antioxidant and cytoprotective activities. We have selected six superior antioxidant compounds (**12**, **16**, **26**, **35**, **42**, and **44**) of the 48 caffeic acid phenethyl ester (CAPE) analogs based on their ability to scavenge free radicals in vitro using standard antioxidant assays. These compounds exhibited minimal toxicity as indicated by cell cycle and cytochrome C release assays. Among these compounds, **44**, the ketone analog of CAPE, exhibited the ability to increase p-Nrf2 (Ser40) levels in 293T cells (\(p<0.05\)). Further, **44**, exhibited its antioxidant effect in _Drosophila Melanogaster_ as indicated by an increase in mRNA levels of Nrf2 and GPx (\(p<0.05\)). Finally, the ability of **44** to activate the antioxidant pathway was abolished in the presence of extracellular signal-regulated kinase (ERK) inhibitor in 293T cells. Thus, we identify **44**, the ketone analog of CAPE, as a unique antioxidant molecule with the function of ERK-mediated Nrf2 activation.  
caffeic acid; phenolic acid; antioxidant; oxidative stress; cell signaling +
Footnote †: journal: Applied Sciences
# An Image-Based Fall Detection System for the Elderly
## 3 Design Requirements and Challenges  
An IFADS for the elderly should fulfill four design requirements: high accuracy, automation, low-cost, and real-time computing. To ensure high accuracy, there should be an accurate and comprehensive way to detect falls in the elderly and then help the elderly recover well. In addition, manually monitoring surveillance images is extremely inefficient and slow; so, an automated technique is needed. Third, the hardware cost should be low. Finally, to help the elderly receive medical care in time after a fall, the computation time for fall detection should be short. The challenges of designing such a system are described below.  
For image-based methods, the major technical difficulty lies in the fact that it is difficult to detect a falling person. When a person falls, the person may be obscured by other people or surrounding objects. As a result, the camera may not see the person or only see parts of the person. On the other hand, it is very difficult to identify human features when a person falls. Another challenge is recognizing the posture of the person. Most of the existing surveillance systems do not provide in-depth information, so it is difficult to recognize the posture from two-dimensional (2D) human shape information, especially in public areas. Therefore, the accurate detection of a falling person is a challenge. For this, we propose a method to track the state of the person continuously. For the situation where the person is invisible or partially visible, our method can determine a fall by backward tracking from the last few frames.  
Environmental obstacles and similarities in colors are also challenges in correctly determining that a fall event has occurred. In order to eliminate the effect of environmental obstacles on accuracy, we use status tracking to evaluate the relationship between the person and environment obstacles. If the person suddenly disappears from the scope of the surveillance system and his/her previous status is not leaving the edge of the surveillance area, we consider this situation as a fall event in which the person is obscured by obstacles. On the other hand, if the person is partially obscured by obstacles, but he keeps walking and his/her height does not change, we consider this situation as normal (Please see Section 4.4). In order to address the problem of similarities in colors, we adopted YOLO, a well-known object detection method, to detect human objects sufficiently. Since YOLO uses not only color information but also contour information to discover human objects, the effect of color similarity can be significantly reduced (Please see Section 4.2).
## 3 Results
### Evaluating Objective Functions and Techniques  
Results for all techniques are shown in Tables 8-10. When comparing the OF results from each technique, some techniques such as CSA, PIO and GA-Swarm are immune to the influence of one technique over another as the results returned are within a narrow range regardless of the OF. However, PIO (along with NM and SA) and CSA will still be regarded as techniques that fail to meet the mission constraints on numerous occasions. GA-Swarm meets the mission constraints on each run.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**TWISP** & & & & & \multicolumn{3}{c}{**GG Cycle**} & \\
**OF** & **NM** & **SA** & **CSA** & **PSO** & **PIO** & **GA** & **GA-Swarm** \\ \hline F (kN) & 256 & 251 & 264 & 271 & 257 & 257 & 259 \\ \(\Delta\)_T_ (s) & 101.1 & 97.3 & 103.1 & 95.3 & 101.5 & 101.6 & 102.0 \\ \(p_{c}\) (bar) & 105.4 & 369.7 & 66.2 & 1149.5 & 95.2 & 94.6 & 84.2 \\ \(I_{sp}\) (s) & 272.6 & 302.2 & 262.3 & 332.9 & 270.3 & 270.2 & 267.6 \\ GLOW (tonnes) & 11.6 & 10.1 & 12.6 & 9.8 & 11.8 & 11.8 & 12.1 \\ T/W & 2.24 & 2.52 & 2.13 & 2.80 & 2.21 & 2.21 & 2.19 \\ \hline \hline \end{tabular}
\end{table}
Table 7: GG Cycle: Values of Variables at max \(I_{sp}\) and T/W for each technique.  
Using GLOW as an OF provides relatively few results which lie near the global minimum (as clusters are observed in local minima). However, when comparing the results for each technique, results from T/W provided a maxima at a relatively lower \(p_{c}\) when using GA-Swarm compared to the other OF. Based on experience, the end user prefers an OF with emphasis on low \(p_{c}\). Therefore, using T/W as an OF may be more feasible as compared to using GLOW.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
## 3 Simulation Results
### Performance Comparison
It was demonstrated that the change in the time axis utilized in the simulation and the change in the symbol length by the measured \(f_{d}\) produced the same outcome. As previously stated, the present Doppler bank detects Doppler in the \(-21.4\) Hz to \(21.4\) Hz range. However, our proposed method uses the peak-to-peak distance to obtain the approximate Doppler (\(\Delta\)) and the Doppler bank to find the precise \(f_{d}\) within the approximate Doppler error range. Figure 11: BER performance at the same bit rate. (**a**) is when there is no artificial Doppler shift, (**b**) is with Doppler and no compensation, (**c**) is when Doppler is compensated. Figure 10: BER performance at the same symbol rate. (**a**) is when there is no artificial Doppler shift, (**b**) is with Doppler and no compensation, (**c**) is when Doppler is compensated.
# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
###### Abstract  
Autonomous mobile robots (AMRs) require SLAM technology for positioning and mapping. Their accuracy and real-time performance are the keys to ensuring that the robot can safely and accurately complete the driving task. The visual SLAM systems based on feature points have high accuracy and robustness but poor real-time performance. A lightweight Visual Odometry (VO) based on Lucas-Kanade (LK) optical flow tracking is proposed. Firstly, a robust key point matching relationship between adjacent images is established by using a uniform motion model and a pyramid-based sparse optical flow tracking algorithm. Then, the grid-based motion statistics algorithm and the random sampling consensus algorithm are used to eliminate the mismatched points in turn. Finally, the proposed algorithm and the ORB-SLAM3 front-end are compared in a dataset to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm effectively improves the real-time performance of the system while ensuring its accuracy and robustness.  
mobile robot; visual SLAM; VO; LK optical flow method; motion model Article  
A Lightweight Visual Odometry Based on LK Optical Flow Tracking [PERSON], [PERSON], [PERSON], G.; 2023, [PERSON], [PERSON], and [PERSON] 1  
[PERSON], [PERSON]; [PERSON], Y.; [PERSON], [PERSON]; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (G.Y.)  
1
## 4 M2M Communication
### Autonomic M2M Communication  
Several projects have targeted autonomic network and communications. Some examples are DASADA [122], Autonomia [80], ACCORD [123], ANA [124] and the IoT-A [125]. The University of Bologna is building a framework [126] to support the design, implementation and evaluation of peer-to-peer Internet applications using Swarm Intelligence; the University College London is working on different projects inspired on bio-approaches for autonomous configuration of distributed systems. All those projects and research groups treat the Internet as an ecosystem and study the relations between the different network elements from a cross-disciplinary perspective. The main goals of every approach are to achieve efficiency in a self-CHOP manner in a large-scale heterogeneous communication infrastructure. The autonomic communication final objective is to provide an evolving network platform for communication between devices (including decision making and reacting) with a high degree of autonomy to decrease to the minimum the human intervention providing a high level of efficiency for both users and telecommunication industry.
# Design and Trajectory Tracking Control of a Magnetorheological Prosthetic Knee Joint
## 2 Design of MR Prosthetic Knee Joint
### Working Principle of MR Prosthetic Knee Joint  
Figure 3 shows the three-dimensional model of MR prosthesis, and Figure 4 shows the structural principle of MR prosthesis. The MR prosthesis is mainly composed of thigh link, MR prosthetic knee joint, connecting tube and a prosthetic foot, among which the MR prosthetic knee joint consists of a two-link mechanism and a MR damper. The two-link mechanism includes a knee joint link and a shell. The knee joint link is connected to the thigh link; the shell part is connected to the prosthetic foot through a connecting tube, and the knee joint link and the shell are connected through hinge C. The lifting lugs at both ends of the MR damper are respectively connected to the knee joint connecting rod and the shell through hinges B and D. The key structural parameters of MR prostheses are shown in Table 3.  
It can be seen from Figure 4 that the length of the MR damper can be expressed as:  
\[{l_{\text{MRD}}}^{2}={l_{\text{CD}}}^{2}+{l_{\text{CB}}}^{2}-2{l_{\text{CD}}}{l_{ \text{CB}}}\cos(\theta_{1}-\theta_{2}) \tag{3}\]  
where \({l_{\text{CD}}}\) is the length from hinge C to hinge D, and \({l_{\text{CB}}}\) is the length from hinge C to hinge B, whose values are 240 mm and 45 mm, respectively.  
Substituting Equations (1)-(3), the length of the MR damper is obtained and shown in Figure 5. It can be seen that the length of the MR damper reaches the maximum and minimum values at the gait cycle 72% and 100% respectively, which are 227 mm and 193.7 mm. Therefore, the stroke of the proposed MR damper should be greater than 33.3 mm.  
Figure 4: Structural principle of MR prosthesis.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Symbol** & **Value** \\ \hline Thigh mass & \(m_{1}\) & 1.02 kg \\ Calf mass & \(m_{2}\) & 2.36 kg \\ Thigh length & \(l_{1}\) & 460 mm \\ Calf length & \(l_{2}\) & 430 mm \\ Thigh mass center to hip joint length & \(r_{1}\) & 360 mm \\ Calf mass center to knee joint length & \(r_{2}\) & 165 mm \\ Moment of inertia of thigh & \(l_{1}\) & 0.356 kg\(\cdot\)m\({}^{2}\) \\ Calf moment of inertia & \(l_{2}\) & 0.078 kg\(\cdot\)m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 3: Key parameters of MR prosthesis.
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 5 Digital Systems
### Security Systems  
Security Systems protect and monitor electronically the Smart Infrastructure against threats, reducing risks and vulnerabilities and deterring potential attackers.  
Closed Circuit TeleVision (CCTV) or Video Surveillance System (VSS) [55; 56] capture images and video frames of the smart infrastructure for security purposes using video cameras. Video analytics enable automatic alarm generation that can be filtered according to its priority and event trace that retrieves frames with relevant properties, such as user faces. Usually video streams are recorded for crime evidential reasons. Once face recognition technology becomes widely adopted; this can enable the use of images as a biometric tool associated to other identification functionalities such as ticketing.  
Access Control [57] manages the user access to the Smart Infrastructure via smart cards, mobile app certificates or user biometrics. Security Zones are established with different priorities where physical access is limited to user credentials. Access Control also enables to track user's room location in case of emergency or evacuation.  
Intruder Detection [58] monitors unauthorized access to the Smart Infrastructure. The main sensors are based on physical properties such as infrared, sound, pressure and volumetric metrics inherent to any physical attacker. Usually, a combination of different sensors is applied to increase the defense on depth.
# Evaluation of a New-Concept Secure File Server Solution
## 2 Background
### Review of File System-Related Security Solutions and Research
While meeting the requirements placed on file servers, like ensuring data availability and accessibility in the presence of large amounts of data, is a complex task, the effective protection of data requires special support and attention to ward off the increasing number of more and more sophisticated ransomware attacks.
## 2 System Model
### LED Characteristic  
We consider using the driving circuit for the LED as shown in Figure 2[10]. Consequently, the static nonlinear relationship between the normalized driving current and the  
Figure 1: VLC-OFDM in IM/DD systems.  
normalized output optical power of the LED can be expressed as a polynomial using a Taylor series expansion as follows:  
\[P_{out}(t)=\sum_{l=0}^{\infty}b_{l}[I(t)-I_{DC}]^{l}. \tag{4}\]  
where \(P_{out}(t)\) is output power, \(t\) is the time, and \(b_{l}\) is the coefficient of the l-th-order power of the transfer function. Since the majority of the nonlinear components are concentrated in terms up to the second order [10], we modeled the nonlinearity using the following quadratic equation:  
\[P_{out}(t)=b_{0}+b_{1}[I(t)-I_{DC}]+b_{2}[I(t)-I_{DC}]^{2}. \tag{5}\]  
Here, \(b_{0}=\xi\), \(b_{1}=1\), \(b_{2}=-4\xi+2\), and \(\xi\) represent the nonlinear coefficients and parameter. In addition, \(\xi\) exhibits distinct values corresponding to different LED types.  
* White LED: \(\xi=0.582\).
* IR LED: \(\xi=0.541\).
* Red LED: \(\xi=0.732\).  
The red LED has the most pronounced distortion, with its normalized output power \(P_{out}\) deviating to approximately 1.2-1.3 times the white and IR LED values near a normalized driving current \(I\) of 0.5. Due to the significant differences in nonlinear distortion depending on the type of LED, the BER performance varies considerably, especially in higher-order modulation schemes [10]. Additionally, we set the normalized DC current \(I_{DC}=0.5\) for simulations in this work.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 2 Problem Identification--Materials and Methods
### Consequences from the Results  
Based on the measurement and simulations, in the critical span, the electric field's peak can be above 8 kV/m, which is 60% higher than the limit value for the general public. This phenomenon is unacceptable from the point of view of safety and security, as well as from a point of view of legal aspects.  
Another critical question is how can these results be generalized for the whole length of the power line. For this purpose, the clearance values were calculated at the maximum operational temperature value of 60 \({}^{\circ}\)C in all the spans.  
Table 5 describes the clearance and electric field peak values at 60 \({}^{\circ}\)C, which is the maximum allowed temperature of the conductor. According to the analysis, more than 65% of the spans are affected by the phenomenon. In the calculation, the phase conductor's curve was described with a catenary curve represented in Equation (4) [30; 31].  
\[y=\frac{\sigma_{h}}{\gamma}\ \frac{e^{\frac{\pi}{P}}+e^{-\frac{\pi}{P}}}{2}= \frac{\sigma_{h}}{\gamma}\ ch\frac{x}{\sigma_{h}} \tag{4}\]  
where \(\sigma\) is the horizontal component of the tensile stress and \(\gamma\) is weight force for a cross-section of 1 mm\({}^{2}\) of a 1 m long conductor. For the sag calculation, Equation (5) served as a basis [30; 31].  
\[b_{h}=\frac{\sigma_{h}}{\gamma}\ \bigg{[}ch\frac{a}{2}\frac{\gamma}{\sigma_{h}} -1\bigg{]} \tag{5}\]  
where (_a_) is the distance of the suspension points of the phase conductors.  
Knowing the sag values at each distance, the clearance can be determined from the spread of the elevation profile and the conductor position. These results, however, do not consider that in some spans, the geometry may vary, and the error of the clearance simulation could be up to 15-20 cm. Although the limit values for safety distance and low-frequency electric field are clearly defined to ensure an adequate level of safety in the vicinity of the transmission lines individually, the specifications have not been harmonized in this case. There are clearly defined safety distance values at each voltage level in the standards applied for high voltage power lines, which does not guarantee compliance with the limit value for electric power fields. This situation needs to be avoided to comply with legal requirements that are in place.  
The situation becomes more complicated if there is an endeavor to apply the DLR method. In these cases, the power line always operates near the maximum allowed conductor temperature increasing the operational risk. In this case, a more complex risk analysis should be applied taking into consideration the extension of the critical parts of the span in question, the co-occurrences of unfavorable weather and load parameters, and the accessibility of essential elements by humans.
## 5 Numerical Results
### The Influence of Bias Factor  
Figure 3 shows \(E(T_{total})\), \(P_{r}\) and \(P_{b}\) as a function of the bias factor \(B_{2}\). For this figure, \(B_{1}=5\) dB, and \(P_{m,k}=\{0.2,\,0.8\}\) are used. The curves in Figure 3 align with Equations (1) and (2), where \(B_{2}\) controls task distribution via Poisson point process modeling. Bias Factor \(B_{2}\) is chosen to model hierarchical MEC decision-making, as it quantifies the preference for offloading to second-tier MEC servers. Increasing \(B_{2}\) (i.e., \(x\)-axis in Figure 3) means more users offload tasks to second-tier MEC servers. We can see that \(E(T_{total})\) and \(P_{b}\) are relatively large, while \(P_{r}\) is quite small when \(B_{2}\) is small. This significant trend can be explained by the fact that a majority of the tasks are being offloaded to first-tier MEC servers. As a result, these MEC servers become overloaded, leading to an increase in waiting delay. As \(B_{2}\) increases, some of the tasks are being offloaded to the second-tier MEC servers, This change is reflected in the graph as a decrease in both \(E(T_{total})\) and \(P_{b}\), and an increase in \(P_{r}\). However, after certain points of \(B_{2}\), \(E(T_{total})\) and \(P_{b}\) increase, and \(P_{r}\) begins to fall. This is due to the second-tier servers becoming increasingly heavy-loaded, and unable to handle the incoming tasks efficiently.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 4 Experiments and Performance Analysis
### Security Analysis  
The security of the watermark is another issue that the watermarking scheme should be considered. Arnold transform also known as cat map is an effective encryption algorithm usually adopted in the watermarking scheme [20]. By Arnold transform, the image pixels are randomly permuted, but after a certain amount of permutations, the original image will reappear. In other words, it has good periodicity. The transform period is decided by the image size. For the image with a size of 256 \(\times\) 256, the Arnold transform period is 192. By using this feature, we can encrypt the watermark by selecting a scrambling time. At present, most of the watermarking algorithms only apply the Arnold transform to the watermark on the sending side [12]. To further improve the security of the proposed method, in this paper, not only the watermark but also the embedding positions are encrypted by using two different secret keys (\(k_{1}=75\) and \(k_{2}=32\)). Figure 10 shows the extracted binary texture images, which are obtained by using the correct and incorrect combined keys. We can see that, without correct keys, the embedded watermark shown in Figure 10d cannot be obtained. It proves that the proposed algorithm achieves good security.
# A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
## 5 Conclusions  
Wireless sensor networks are vulnerable to variety of attacks at different protocol layers. In the existing trust-based intrusion detection schemes, there is no unified standard to select trust factors, and cross-layer attacks are seldom considered. In order to identify malicious nodes more efficiently, we have proposed a protocol layer trust-based intrusion detection scheme for WSNs. In our scheme, the key parameters of different protocol layers are monitored and the trust values of sensor nodes can be calculated according to the deviations of parameters. By comparing the trust value with a predefined threshold, we can decide whether the sensor node is compromised or not. It can describe the trust values of sensor nodes more accurately by considering the deviations of parameters of multiple layers, hence our proposed scheme is effective for detecting cross-layer attacks. We utilized the t-distribution and simulation to analyze the detection probability and false positive probability of our scheme. The results indicate that there exists an optimal trust threshold at which both false positive and false negative probabilities are minimized. Our proposed scheme outperforms the NBETE scheme in terms of the detection probability and false positive probability. The weakness of our scheme is the communication overhead will increase with the increasing of the hop count to the CH. Our scheme is extendable, the selection of the trust factors at different protocol layers can be adjusted according to the requirements of a system, and it is applicable to both clustered WSNs and flat WSNs. As for future works, we will analyze the attacks initiated at the transport layer and application layer, as well as MAC-Transport cross-layer attack, Network-Application cross-layer attack and their influence on the protocol parameters to further optimize our scheme. In addition, we will perform experiments to test the performance of our scheme on a real WSN testbed to assess its real-life performance.  
All authors made significant contributions to this paper. [PERSON] designed the scheme and wrote the paper. [PERSON] performed the simulation and analyzed the performance of the scheme. [PERSON] provided valuable insights for the scheme and revised the manuscript.  
The authors declare no conflict of interest.