esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 4 Results
### Ablation Study  
To validate the importance of canonical image editing, we conducted an ablation study. The results are shown in Figure 7. Figure 7a represents the effect of propagation after editing the canonical image, and Figure 7b,c,d respectively demonstrate the effects of propagation along the deformation field after editing the first frame, a random frame, and the last frame. Observations reveal that in Figure 7b,d, the eyes of the person are always open, and in Figure 7c, the eyes of the person are always closed. This indicates that the canonical image can not only store the static shape information of the video object but also represent the motion information of the video object. Analysis shows that by observing Figure 7b-d, there are significant artifacts and afterimages at the edges of the fingers and hair, and some parts are blurred.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MA \(\uparrow\)** & **AA \(\uparrow\)** & **EQ \(\uparrow\)** & **TC \(\uparrow\)** \\ \hline AnyV2V & 93.2 & 85.3 & 93.7 & 91.3 \\ StableVideo & 89.5 & 88.2 & 92.1 & 96.2 \\ MotionDirector & 95.6 & 91.4 & 90.6 & 92.4 \\ Ours & 98.8 & 95.8 & 94.5 & 98.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation.  
The above ablation experiments demonstrate that the canonical image can significantly support the local editing module in performing editing operations. Simultaneously, it ensures the consistency of video processing and the accuracy and consistency of motion information, further enhancing the quality of video editing.  
To further validate the effectiveness of the canonical image in supporting local editing, we conducted additional quantitative experiments. We evaluated the editing quality using LPIPS, PSNR, and FID scores on a dataset of videos. The results in Table 2 demonstrate that utilizing the canonical image significantly improves perceptual similarity and reduces artifacts in edited frames. We conducted 20 experiments to validate the contribution of the canonical image in supporting the local editing module. The results demonstrate that the canonical image effectively retains structural and semantic information, enabling more stable and accurate edits compared to direct frame-wise editing approaches.  
Figure 7: Compare the ablation experiments of propagation when editing different frames. (**a**) represents the effect of propagation after editing the canonical image, and (**b**,**c**,**d**) respectively demonstrate the effects of propagation along the deformation field after editing the first frame, a random frame, and the last frame.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 5 Dimensionality Reduction Techniques
### Singular Value Decomposition (SVD)  
Another approach that builds on PCA is singular value decomposition (SVD), which uses attribute elimination to cut down on overlap [58]. Its consequence is a smaller number of components than PCA but it retains the maximum variance of the signal features. The process is based on the factorization principle of real or complex matrices of linear algebra. This method performs the algebraic transformation of data and is regarded as a reliablemethod of orthogonal matrix decomposition. The SVD method can be employed to any matrix, making it more stable and robust. This method can be used for a dimension reduction in big data, thereby reducing the time for computing [76]. SVD is used for a dataset having linear relationships between the transformed vectors.  
The SVD algorithm is a powerful method for splitting a system into a set of linearly independent components where each component has its energy contribution. To understand the principle of SVD, let us consider a matrix A of dimensions \(M\times N\) and another matrix \(U\) of \(M\times M\) dimensions. The vectors of A and U are assumed to be orthogonal. Further, let us assume another matrix S, which has a dimension of \(M\times N\). Another orthogonal matrix, \(V^{T}\), having a dimension of \(N\times N\) is also assumed. Then, matrix \(A\) is represented by Equation (29) using the SVD method [77].  
\[A\ =\ U\times\Sigma\times V^{T} \tag{29}\]  
where the columns of \(U\) are called left singular vectors, and those of \(V\) are called right singular vectors.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 3 Advanced Oxidation Processes and Photocatalysis
### Principles of Solar Photocatalysis
#### 3.1.1 Heterogeneous Photocatalysis
The use of doping has been extensively investigated with the goal of increasing the catalyst absorption in the visible range, among which modifications of TiO\({}_{2}\) by W [91], Pt [92], N, V and V-N [93], Cu and N [94], Co and B [95], and Ag [96] showed promising results for the photocatalytic degradation of organic compounds. Many studies have also been devoted to the design of other metal oxide photocatalyst materials, such as perovskite-type oxides, which can work under visible light illumination [18; 97; 98]. Other catalysts investigated for wastewater purification with sunlight are Bi\({}_{2}\)WO\({}_{6}\) and Ag-BiVO\({}_{4}\), for their absorption closer to the visible range (420 nm) [99]. In a theoretical work, density functional theory (DFT) calculations explored the modification of a \(\beta\)-Bi\({}_{2}\)O\({}_{3}\) photocatalyst with 32 elements to design visible-light-responsive photocatalysts. Based on this, a series of photocatalysts were identified as good candidates for the reduction of chlorinated organic compounds in water [100]. Recently, Co\({}_{3}\)O\({}_{4}\) hierarchical urchin-like structures were produced by PLD and tested on model dyes, raising great interest in the synthesis of hierarchical 3D nanostructures for applications in water purification [85]. Iron oxide-based nanostructures have also been fabricated by PLD and studied for photocatalytic water purification [101]. Other catalysts such as ZnO, SnO\({}_{2}\), WO\({}_{3}\), and ZnS have also been reported which can operate either via the reductive pathway (ZnS), the oxidative one (SnO\({}_{2}\) and WO\({}_{3}\)) or both (ZnO) [102; 103]. An interesting study employed volcanic ashes in a solar photocatalytic reactor. The volcanic ashes are rich in titanomagnetic, labradorite, augite and ferrous magnesium hornblende capable of performing photocatalysis in the visible-NIR ranges [104]. Reports of novel materials focusing on visible light response catalysts are rapidly emerging and are increasingly successful. Their application to solar concentration for wastewater purification appears to represent an interesting opportunity to advance the field. Table 2 below shows a selection of references implementing the design strategies discussed above. Figure 9: General mechanism of the heterogeneous photocatalysis [8].
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Experimental Results  
We evaluate the bandwidth sharing performance without and with ABC in three different cases that were investigated by simulation in [6].
## 5 Conclusions and Future Directions
Our upcoming research aim is to extend the detection range by utilizing a more efficient and economical calculation process. Future studies may explore progressive global model aggregation methods to improve efficiency and convergence in the FL environment. Other advanced ML models, such as XGBoost and LightGBM, can advance identification accuracy while upholding computational efficacy. Escalating the FL-DSA to cover a wider variety of IoT routing attacks and directing scalability in varied IoT systems can be explored. Furthermore, employing and testing this technique in real-world IoT scenarios will be critical to authenticating its efficiency and practicality under operative circumstances. Conceptualization, N.T. and R.K.; Methodology, N.T. and R.K.; Software, R.K.; Validation, F.A.K., A.A. and M.A. ; Formal Analysis, N.T., F.A.K. and A.A.; Investigation, R.K.; Resources, F.A.K. ; Data Curation, M.A., S.S. and A.A.; Writing--Original Draft Preparation, N.T., S.S. and R.K.; Writing--Review and Editing, F.A.K. and M.A. ; Visualization, R.K.; Supervision, N.T. ; Project Administration, N.T. ; Funding Acquisition, F.A.K. All authors have read and agreed to the published version of the manuscript. This work was supported by King Saud University, Riyadh, Saudi Arabia, under Researchers Supporting Project number RSFD2024R1062. Not applicable. Data availability Statement: Data are contained within the article. The authors extend their appreciation to King Saud University, Riyadh, Saudi Arabia, for funding this research under Researchers Supporting Project number RSFD2024R1062. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} IoT & Internet of Things \\ RPL & Routing Protocol for Low-Power and Lossy Networks \\ FL-DSA & Federated Learning-based Detection Mechanism for Selective Forwarding Attack \\ IRAD & IoT Routing Attack Dataset \\ HF & Hello Flood \\ DR & Decreased Rank \\ VN & Version Number \\ LR & Logistic regression \\ KNN & K-Nearest Neighbors \\ SVM & Support Vector Machine \\ NB & Naive Bayes \\ DoS & Denial of Service \\ DDoS & Distributed Denial of Service \\ CNN & Convolutional Neural Network \\ \end{tabular}
# Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine
###### Abstract  
Multi-rotor system (MRS) wind turbines can provide a competitive alternative to large-scale wind turbines due to their significant advantages in reducing capital, transportation, and operating costs. The main challenges of MRS wind turbines include the complexity of the supporting structure, mathematical modeling of the aerodynamic interaction between the rotors, and the yaw control mechanism. In this work, MATLAB 2018b/Simulink(r) software was used to model and simulate a twin-rotor wind turbine (TRWT), and an NEEL 5 MW wind turbine was used to verify the model outputs. Different random signals of wind velocities and directions were used as inputs to each rotor to generate different thrust loads, inducing twisting moments on the main tower. A yaw controller system was adapted to ensure that the turbine constantly faced the wind to maximize the power output. A DC motor was used as the mechanism's actuator. The goal was to achieve a compromise between aligning the rotors with the wind direction and [PERSON] the torque induced on the main tower. A comparison between linear and nonlinear controllers was performed to test the yaw system actuator's response at different wind speeds and directions. Sliding mode control (SMC) was chosen, as it was suitable for the nonlinearity of the system, and its performance showed a faster response compared with the PID controller, with a settling time of 0.17 sec and a very low overshoot. The controller used the transfer function of the motor to generate a sliding surface. The dynamic responses of the controlled angle are shown and discussed. The controller showed promising results, with a suitable response and low chattering signals.  
multi-rotor systems; renewable energy; sliding mode control; wind energy; yaw control 1  
[PERSON], [PERSON], [PERSON], M.A.; [PERSON], [PERSON], A. Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine. _Appl. Sci._**2024**, 14, 9810. [[https[PERSON]doi.org/10.3390/app1429810](https://doi.org/10.3390/app1429810)]([https[PERSON]doi.org/10.3390/app1429810](https://doi.org/10.3390/app1429810))
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 4 Hardware-Based Countermeasures for Memory and Cache Contention DoS Attack
### Our Proposed Mitigation Method  
#### 4.2.1 Instruction Fetch Bandwidth Throttling  
Once we detect the memory and cache contention DoS attack in the system, we should apply a mitigation method that can neutralize the consequences of the attack. Because the memory and cache contention DoS attack is mainly caused by resource contentions between the normal and malicious programs, restricting malicious program's accesses to the shared resources would be an effective solution for mitigation. In this paper, we propose reducing instruction fetch bandwidth when the malicious program is detected by our detection mechanism. We employ the instruction fetch throttling to reduce instruction fetch bandwidth, which dynamically adjusts the fetch width of a certain processing core. When our detection mechanism sends the mitigation mechanism trigger signal, it reduces the instruction fetch width from 3 to 0 by controlling the MUX selection signal, as shown in Figure 7. When the instruction fetch width of a certain core becomes 0, the core cannot fetch the instruction, fully restricting malicious program's accesses to the shared resources. In terms of hardware overhead, our mitigation mechanism can be implemented with one multiplexor and the fetch width control unit, as already shown in Figure 7. The fetch width control unit can be implemented by temporarily halting (e.g., clock gating) the instruction fetch units. Please note that the clock gating scheme is very widely used in digital circuits.  
Figure 7: Our dynamic instruction fetch width throttling technique.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Regulatory Standards and Guidelines  
Grounding systems must comply with several international and national standards to ensure they are both effective and safe. These standards outline the design, construction, and maintenance of grounding systems to reduce hazards.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## Materials and Methods
### Seasonal Threshold Approach  
To estimate the FT state, a seasonal threshold approach was used. This approach examines the timeseries progression of the remote sensing measurements relative to signatures acquired during seasonal reference frozen and thawed states. The key parameter in this approach is the seasonal scale factor \(\Delta(t)\) (see Equation (1)), which can be calculated from \(\sigma(t)\), the measurement acquired at time \(t\), for which an FT classification is pursued, and \(\sigma_{ft}\), and \(\sigma_{th}\), the radar backscatter measurements corresponding to frozen and thawed reference states, respectively. After defining the seasonal scale factor \(\Delta(t)\) at time \(t\), this factor is then compared with a certain threshold to define the thawed and frozen landscape states (Equation (2)).  
\[\Delta(t)=\frac{\sigma(t)-\sigma_{ft}}{\sigma_{th}-\sigma_{fr}} \tag{1}\]  
\[\Delta(t)>T \tag{2}\]  
\[\Delta(t)\leq T\]  
To set the reference values of frozen and thawed states \(\sigma_{ft}\) and \(\sigma_{th}\), two reference seasons of frozen (January + February) and thawed (August) states were defined. The frozen and thawed references were then computed for each grid cell of interest, individually, as an average of all existing S1 measurements acquired from their reference seasons during the period April 2014 to August 2020.  
The FT algorithm is run on a grid cell-by-cell basis. To reduce the accidental bias of a single cell, the satellite observation data were preprocessed with a spatial filter (Section 2.2), and the test sites were carefully selected by considering the surrounding environment (Section 2.3). The output from Equation (2) is a dimensionless binary state variable that designates either frozen or thawed state for each cell of interest [16].
## 3 Materials and Methods
### Proposed ATM System Design and Implementation
#### 3.3.3 Vehicle Image Processing Module in ITM  
This model first identifies the motor vehicles via images and electronic sensors integrated into the road surface. A webcam will also be positioned along with the traffic signal and sensors. This will capture patterns of image data. Object detection is the best alternative to regulate the state change of the traffic signal. It can reduce road-traffic congestion and minimize wasted time with a green light over an empty highway. Furthermore, it is more accurate in predicting vehicle presence because it utilizes actual traffic-image data. It analyzes the usefulness and processes much better than all those models that depend on identifying the vehicular surface material [55]. Algorithm 1 represents the working of Image processing in the intelligent transport system.  
```
Step-1. Image data collection: using a camera and sensor installed over the road. Step-2 Preprocessing phase: To process the images as follows- 2.1 Images are converted into a standard size (i.e., 450 \(\times\) 450 pixels) 2.2 Convert all the captured RGB images into grayscale images. Step-3. Edge detection phase: Canny edge detection method Step-4. Pixel match technique: The output of step 3 is compared by using pixel to pixel (P.P.M.) matching techniques Step-5. Timing allocation: It depends on the result of step 4; the percentage of image matching criteria is as follows: 5.1 If the image matched \(<\) = 40%, then on a green light for 90 s 5.2 If the 40% \(>\) image matched \(<\) = 70% then on green light for 60 s 5.3 If the 70% \(>\) image matched \(<\) = 90% then on green light for 30 s 5.4 If the 90% \(>\) image matched \(<\) = 100% then on Red light for 90 s 5.5 Repeat steps 3-5
```  
**Algorithm 1**Image processing in the intelligent transport system
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### O-AODV Topologies and Testbed Design
#### 4.1.2 Hardware and Software Utilized for the O-AODV Testbed  
The Nordic Semiconductor nRF52840 development boards are the BLE nodes selected for the testbed. The boards are embedded with four LEDs and four user-programmable buttons, as well as various input and output interfaces; Segger J-Link OB support; an integrated printed circuit board antenna; a battery (coin-cell) and micro-USB connectivity  
Figure 4: Scenario 2: multipath topology with 6 nodes.  
Figure 5: Scenario 3: multipath partial mesh topology with 10 nodes.  
support used for programming, as well as for powering, and a UART serial port communication. A 32 bit ARM Cortex M4F processor is embedded with 512 kB of flash memory storage, and the board is equipped with 64 kB of RAM.  
The only element in the mesh network are the BLE nodes. They were programmed with a BLE stack and the proposed mesh-based protocol that was designed and developed mainly in the application and network layer of the Zephyr RTOS (real-time operating system). All 10 nRF52840 DKs were connected via a USB connection to a laptop for data collection purposes by utilizing the PuTTY software.  
The proposed protocol was embedded with the device-provisioning feature for the mesh network security, which means that every device wishing to join the network must first go through the provisioning process. Thus, the built-in provisioning APIs of Zephyr RTOS were utilized to enable the provisioning feature in the proposed protocol's application layer. Furthermore, for physical provisioning, a BLE mesh Android application was required on a smartphone to grant permission to the mesh-enabled nodes to enter the mesh network.  
Moreover, the developed protocol was hardcoded with the network, application, and device keys for testing purposes. However, to enhance the security of the protocol, developers could dynamically give the security keys and utilize the available BLE security features as discussed in Appendix B.2.  
Moreover, the operating system used to develop the protocol was Zephyr RTOS [35]. Zephyr OS is based on a very small footprint kernel for embedded devices that are resource constrained, which includes anything from simple embedded environmental sensors and LED wristbands to smart IoT apps and comprehensive integrated controllers. More explanation is given in Appendix B.
# Using Statistical Shape Models to Optimize TKA Implant Design
## 3 Results
### Generation of the SSM
#### 3.1.2 Selection of Input Data, Impact of Gender and Ethnicity  
The global SSM was able to rebuild the subgroups models with a maximum error of 4.4 mm to 6.5 mm. The subgroup SSMs only managed to recreate the other subgroup's anatomy with an error between 9.4 and 15.7 mm. Comparing the global SSM with the subgroup SSMs allowed to draw conclusions about the main differences between the anatomies of each subgroups: the Caucasian SSMs were broad in ML, the male Asian SSM were large in AP, and the intercondylar notch of the female Asian SSM proved to be narrow. It was concluded that gender and ethnicity have an important influence on the SSM and thus are distinguishable features in the populations.
## 4 Case Study Results
### The Core Elements of AeroLogic's Business Model
#### 4.4.3 AeroLogic's Route Network
There are, however, two key disadvantages with combination airlines: the timing of the flights is geared around passenger requirements. Notwithstanding,  
Figure 4: The AeroLogic route network: 2017. Airports Legend: BAH–Bahrain; BKK–Suvarmabhumi Airport; BOM–Mumbai; BRU–Brussels Airport; CVG–Cincinnati/Northern Kentucky International Airport DXB–Dubai; EMA–East Midlands, FRA–Frankfurt, HKG–Hong Kong; LAX–Los Angeles International Airport; LEJ–Leipzig/Halle Airport; MAA–Chennai Airport; MXP–Milan–Malpensa Airport; ORD–Chicago, PVG Pudong International Airport (Shanghai); SEA–Seattle–Tacoma International Airport; SGN–Tan Son Nhat International Airport (Vietnam); SIN–Singapore; TAS–Tashkent. Source: [166]. on long-haul intercontinental flights they may also suit cargo shippers [4] (p. 132). Additionally, some passenger destinations are not major air cargo markets and, therefore, will not attract much cargo [4; 120]. As noted, the AerLogic route network is customised to meet the JV partners' transportation requirements. Being an all-cargo airline, AerLogic does not suffer from the disadvantages with combination airline services. Furthermore, line-haul operators transport air cargo on an airport-to-airport basis and rely on international air freight forwarders to deal directly with shippers [87; 167]. AerLogic, in contrast, is not reliant upon air freight forwarders for its traffic. It is also important to note that world air cargo traffic is concentrated on several key trade flows between regional centres of production and consumption. This concentration is especially pronounced in air cargo than for airline passenger flows, which are more diffuse in nature [168]. Inter-continental freighter routes are designed to link up the major centres of world trade. Such networks consist of long-haul flights to and from the airlines' major hub airport, where long-haul shipments are often broken down and uplifted on subsequent flights to their final destinations. The major types of freighter aircraft operated on long-haul inter-continental routes are the Boeing B777-200 LRF, B747-400F, B747-8F, and MD11 aircraft [169]. AerLogic's network, like the other dedicated air cargo carriers and the combination airlines operating freighter aircraft, follows the world's key trade lanes. As has been noted throughout this paper, the AerLogic route network is customised to satisfy the two joint venture partners' requirements.