esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SkyDRL for Multi-Cloud Service Composition Problem
### Preliminary
#### 4.1.3 Skyline Algorithm  
The skyline algorithm is a well-known approach for tackling multi-objective optimization problems. Its main objective is to select a set of possible optimal solutions based on the dominance relationship. In the context of cloud service composition problems, the skyline algorithm utilizes the dominance relationship to choose services from a candidate set that is not dominated by other cloud services. All these non-dominated cloud services form the skyline set.  
Assuming that there are two cloud services, \(CS_{1}\) and \(CS_{2}\), with \(Q\) QoS attributes. If \(\forall i\in[1,Q]:q_{i}(CS_{1})\geq q_{i}(CS_{2})\) and \(\exists k\in[1,N]:q_{k}(CS_{1})>q_{k}(CS_{2})\), then we can say that the cloud service \(CS_{1}\) dominates \(CS_{2}\), denoted as \(CS_{1}\succ CS_{2}\).  
\(q_{i}(CS_{1})\) represents the value of the \(i\)-th QoS attribute of \(CS_{1}\). To better illustrate the meaning of the dominance relationship, we use the example shown in Figure 4. As shown in Figure 4, we describe each service using response time and cost attributes, and thus, the services are represented as points in space. From the figure, we can observe that cloud service A is not dominated by any other cloud service, so cloud service A belongs to the skyline set. Similarly, it can be seen that cloud services I and J both belong to the skyline set. On the other hand, for example, cloud service C is dominated by cloud service I, so it does not belong to the skyline set. In the same vein, cloud services B, D, E, F, G, H, and K do not belong to the skyline set.  
Utilizing the skyline algorithm to process the candidate cloud service set can significantly improve computing efficiency when compared to performing calculations on the original candidate cloud service set. Deep reinforcement learning is a learning approach that relies on training with a large amount of data. By reducing the search space with skyline, the model prioritizes higher-quality cloud services, which enhances the training process, simplifies optimization, and reduces computational costs. Currently, several variants of skyline algorithms are available, such as the bitmap method, nearest neighbor method, and branch and bound method. Among them, the branch and bound skyline algorithm is an incremental approach that leverages R-trees to index data points based on nearest neighbor search [52]. In this study, we adopt the branch and bound skyline algorithm to optimize the search space and reduce the computing time required for deep reinforcement learning, thereby improving the training speed. A detailed description of this algorithm is presented in Algorithm 1.
# Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects
## 6 User Study
### Experiment 3--Virtual Lifting vs. Real Lifting
#### 6.6.4 Exert Force with EMG Sensors  
In this experiment, an EMG sensor was used to measure the exerted force applied by the user to lift the object. The EMG signal is an electrical signal that appears according to the depolarization phenomenon inside and outside the muscle during muscle contraction and relaxation. We used the iEMG (integrated EMG), which is electromyography that can determine muscle activity through the cumulative result of muscle activity. The EMG Envelope can check information, such as muscle activity time, muscle activity period, instantaneous muscle activity, and muscle control pattern, through patterns that reflect changes in the state of muscle contraction [31].  
Physiolab's PSL-IEMG2 module was used in the experiment. The output range is 0-3.3 V, and the EMG envelope can be output by changing the RawEMG signal. Figure 18 shows the output graph of the EMG value through the participant's biceps during the virtual lifting process. Because the magnitude of the force that participants can generate is different, the measured EMG sensor values of the participants were converted into values through normalization based on the minimum value of 0 and the maximum value of 1.  
Figure 18: EMG sensor output while a virtual lifting process.  
Exert Force  
Table 11 shows the average force used by the test participants measured during sets of 10 reps. When lifting a virtual object using FCGGUI, an average force of 0.1646 was used, and when lifting with actual weight, it was 0.22381, which is about 35.9% more than the virtual force. In addition, the standard deviation of the virtual force to lift a virtual object is 0.036225, which is more deviated than the standard deviation of the actual force required to lift a real object, 0.021803. This means that it is difficult to standardize because the deviation of the virtual force is larger than the actual force, even though the participants could perceive the weight of virtual dumbbells.  
Table 12 describes the result of the ANOVA test on the force required for the user to lift. The virtual force of lifting the virtual object differs from the exertion force used to lift the actual object, and the gap between the virtual force and actual force was statistically significant (\(p=0.05\)).
## 3 Proposed Approach
### Robot Odometry Using a Kinematic Motion Model  
As the motion of the camera mounted on the robot depends on the robot's movement, we apply the motion vector of the ground plane image to a kinematic model of the robot to estimate the robot's odometry. In this step, the motion vector of the ortho-rectified GPI is regarded as the measurement of a virtual front wheel. Thus, we employ the bicycle motion model and estimate the velocity V of the virtual front wheel and the steering angle \(\delta\) using Equations (25) and (26).  
\[\mathrm{V}=\frac{\sqrt{\overline{dx^{2}+\overline{dy}^{2}}}}{dt}, \tag{25}\]  
\[\delta=tan^{-1}\left(\frac{\overline{dy}}{\overline{dx}}\right)-\frac{\pi}{2}, \tag{26}\]  
where \(\overline{dx}\) and \(\overline{dy}\) are the components of the median motion vector, and \(dt\) is the interval time between image frames.  
Figure 14 shows the bicycle motion model we employ in this study. The bicycle motion model rotates and moves around IC, the center of motion in a 2D plane. We use the median motion vector \(\overline{m}\) of the GPI to measure the velocity V of the virtual front wheel and the steering angle \(\delta\). As a result, the 2D odometry of the robot is estimated, and it is named Ground Plane Odometry (GPO). The bicycle motion follows three assumptions:  
* The wheel-based mobile robot moves in a 2D plane;
* The center of mass of the mobile robot is located in the center of the robot body;
* Non-slip, no longitudinal/lateral slip occurs on the wheels.  
Following these assumptions, the robot's pose is estimated by applying the velocity and the steering angle of the virtual wheel to the bicycle model of the robot as follows. All parameters of the bicycle model in Figure 14 are calculated using the following equations:  
\[\mathrm{S}=\frac{L}{\tan(\delta)}, \tag{27}\]  
\[\beta=atan\left(\frac{lr}{S}\right)=atan\left(lr\frac{\tan(\delta)}{L}\right), \tag{28}\]  
\[\mathrm{R}=\frac{S}{\cos(\beta)}, \tag{29}\]  
\[\dot{\theta}=\omega=\frac{V}{\mathrm{R}}=\mathrm{V}\frac{\tan(\delta)\cos( \beta)}{L}, \tag{30}\]  
Figure 14: Motion vector on the ground plane image applying to the bicycle motion model.  
\[\theta=\ \theta+\hat{\theta}dt, \tag{31}\]  
where \(lr\) is the distance between the rear wheel axis center and the mass center of the robot, \(L\) is the distance between the rear wheel axis center and the front wheel axis center, and \(\omega\) is the angular velocity of the robot that rotates around the IC. Using these parameters, the velocity of the robot is determined as shown in Equations (32) and (33).  
\[\dot{x}=\mathrm{V}\mathrm{cos}(\theta+\beta), \tag{32}\]  
\[\dot{y}=\mathrm{V}\mathrm{sin}(\theta+\beta). \tag{33}\]  
Finally, the visual odometry of the mobile robot is estimated using Equation (34).  
\[\begin{bmatrix}x(t+1)\\ y(t+1)\\ \theta(t+1)\end{bmatrix}=\begin{bmatrix}x(t)\\ y(t)\\ \theta(t)\end{bmatrix}+\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{bmatrix}dt. \tag{34}\]
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 2 Related Works
### Optical Flow  
Estimating the movement between two pictures has been a subject of research for a long time, as it has numerous applications. For video coding, it is necessary for finding motion vectors, and is often done through computationally expensive methods that check the error for each possible motion vector, with more recent methods improving the search algorithms to keep the encoding time reasonable. In those cases, only the cost for the whole block is considered, so the movement estimation is often not accurate at a more granular level.  
However, in many applications, the movement for each pixel is desired. This is typically referred to as optical flow. One of the most famous and popular methods for estimating optical flow is the Lucas-Kanade method [12]. It has been used a lot and gives satisfying results for simple movements. It is quite fast, which is one of the reasons for its popularity. Because it is included in the OpenCV library, it is also very easy to use, while many methods do not release their code, which adds the additional burden of implementation to potential users.  
A recent application that also shows potential for video coding is frame interpolation, where by computing the movement for each pixel between the two frames, it is possible to estimate the missing frame with remarkable accuracy, which was demonstrated in EpicFlow [13]. To speed up the process, it is possible to use the motion vectors that are used for encoding the frames as estimators of the motion for a given block, then refine the optical flow to a pixel level, as was proposed in HEVC-Epic [14], which offers a good increase in speed compared to EpicFlow, but is still very slow, taking several seconds for estimating a single frame.  
While the computed interpolated frame could be used in encoding with a new kind of prediction, it would make decoding too slow. Decoding needs to be possible on inexpensive hardware to see any large scale adoption.  
While the state of the art optical flow methods achieve impressive accuracy, this comes at the cost of increased computation, and depending on the methods the time required varies depending on the picture. When considering hardware implementations and real time constraints, as is the case in encoding, it is important to ensure that the computations will always be bounded as to avoid the need for additional circuitry that will be used only in few cases. In this paper, the optical flow method from [PERSON] [15] was considered because the computation cost varies solely on the size of the input picture and the parameters for the number of iterations.  
It also offers very nice properties for hardware implementation, as all the operations are highly parallel in nature, which makes them very easy to implement in hardware. While the software implementation is not parallelized, it would be possible to improve the speed relatively easily.
## 4 Summary and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Tunnel** & **Length** & **Slope** & **CO** & **PM\({}_{10}\)** & **PM\({}_{2.5}\)** \\  & & **(m)** & **\%** & **(ppm)** & **(ppm)** & **(ppm)** \\ \hline This study & Lowari Tunnel & 8509 & 1.8 & 5.80 & 0.40 & 0.11 \\ \hline [PERSON] et al., 2011 & Hsuehshan Tunnel & 12,900 & 1.3 & 5.21 & - & - \\ \hline [PERSON] et al., 2019 & Xihan ESA1 Tunnel & 6102 & - & 5.10 & - & - \\ \hline  & Calle Hidalgo Tunnel & 1100 & 2.1 & 0.01 & 0.08 & 0.08 \\  & Ponciano Aguilar Tunnel & 1100 & - & 0.02 & - & - \\  & Tiburcio Tunnel & 0950 & 1.7 & 0.02 & - & - \\ [PERSON] et al., 2021 & Barretero Tunnel & 0800 & 2.6 & 0.03 & 0.10 & 0.09 \\  & Galerena Tunnel & 0700 & 6.7 & 0.02 & 0.08 & 0.08 \\  & Los angeles Tunnel & 0600 & 6.9 & 0.07 & 0.18 & 0.16 \\  & Santa F Tunnel & 0500 & 1.6 & 0.03 & 0.08 & 0.08 \\  & Tamazuca Tunnel & 0110 & 2.1 & 0.02 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the tunnels’ pollutant concentrations during the operational stage. wind direction was mostly from the north to the south, with variable velocity. Lowari Tunnel was compared with other tunnels across the globe, and it was discovered that its length and vehicle conditions have resulted in a greater concentration of CO, PM\({}_{10}\), and PM\({}_{2.5}\) than in other tunnels.
# Alginate Silver Nanoparticles and Their Effect on Sperm Parameters of the Domestic Rabbit
## 1 Introduction
In the ejaculate, sperm naturally produce small amounts of reactive oxygen species, which play an important role in several processes, including capacitation or the acrosome reaction [48]. A higher amount of ROS in the ejaculate can lead to the induction of oxidative stress, causing damage to DNA strands in sperm [48]. Damage to the sperm's nuclear DNA or changes in its structure is a cause of infertility in men, and the presence of these disorders is also an important reason for failures in treatments using artificial insemination methods [48]. Replacing or supplementing antibiotics with AgNPs in animal breeding practices could offer numerous advantages. These include reducing the risk of bacterial antibiotic resistance development [49; 50], improving wound healing and preventing infections in animals [50; 51], enhancing animals' immune systems and natural defenses [51; 52], and providing a cost-effective and environmentally friendly alternative to antibiotics [50; 52]. In the present study, an innovative composite of silver nanoparticles suspended in sodium alginate was used as a safe, non-toxic, antibacterial component in a diluent for rabbit semen storage. The purpose of this study was to evaluate the effect of silver nanoparticles, stabilized with sodium alginate, and obtained by chemical synthesis using two reducing agents, xylose and sodium borohydride, on the morphology and integrity of nuclear chromatin in rabbit spermatozoa.
# Quality of Service Based NOMA Group D2D Communications
## 4 Numerical Results and Discussion
### Comparison between Paired and Grouped D2D Communications  
The average outage probability achieved by Q-NOMA group D2D communications under different path loss exponents is shown in Figure 4. The performance of paired D2D communication based on OMA is also presented in the figure as a benchmark for comparison. It can be observed that Q-NOMA group D2D achieves overall lower outage probability than the paired D2D communication for different values of path loss. This is because, as opposed to paired D2D, Q-NOMA group D2D communication uses only single transmission, which results in better SINR at the DRs under an interference limited scenario.  
Figure 4: Outage comparison between paired and group D2D.  
Figure 3: Impact of \(d\) on outage probability.
## 3 SAgic-IoT Platform Description
### Layered IoT Architecture  
The SAgric-IoT design complies with the remote monitoring needs, contributing to reducing human resource investment. SAgric-IoT defines a five-layered model (as shown in Figure 2) with a transparent interconnection of all platform components and data dissemination in the agriculture precision area. The defined architecture covers all seven OSI model layers, as described in the following sentences. The gathering layer comprises the data link and physical layers. The communication layer is integrated with the data link and network layers. Finally, the processing and end-user layers are directly related to the four upper layers (transport, session, presentation, and application). The security layer is a transversal layer that interacts with the other layers of the architecture to obtain an end-to-end security level.
## 2 Materials and Methods
### Accuracy Evaluation Method  
The accuracy evaluation of satellite clock offset is effective to verify the reliability of satellite clock offset data processing results. The optimal clock offset accuracy evaluation uses the theoretical real clock offset to evaluate the calculated clock offset accuracy. However, it is very difficult to obtain the theoretically real clock offset. The clock offset accuracy can be evaluated through different methods such as the inner coincidence accuracy comparison method and the outer coincidence accuracy comparison method. As for the internal coincidence accuracy comparison method, the accuracy is judged based on internal errors that generally refer to the fitting residual or prediction error of clock offset. According to the external coincidence accuracy comparison method, the accuracy is judged based on external information or other link information, which generally refers to the closure error of clock offset, mainly including the inter-satellite station closure error and three-satellite closure error.
# Analytical Hierarchy Process for Construction Safety Management and Resource Allocation
## 2 Background
### The 5M Model
#### 2.1.2 Mission  
Assessing risks in construction work is challenging due to the dynamic nature of the construction environment, which differs significantly from other industries where the work environment is generally static. Construction sites undergo constant changes, including team rotations, high turnover rates, varying ground conditions, diverse technologies and execution methods, unique project-specific environments, and different teams with distinct missions operating in the same area. These characteristics increase the complexity of safety risk assessment [13]. [PERSON] et al. [14] developed an attribute-based model for risk assessment using accident reports from OSHA's Integrated Management Information System (IMIS) [15]. Their model aimed to identify measurable safety attributes and incorporate them into a risk analysis approach based on historical data. However, this model has limitations, notably the need to expand attribute-based safety risk analysis beyond specific risk types and accommodate variations in risk perception among different professions in construction. [PERSON] et al. [16] developed a quantitative analysis method for generating a loss index for various accidents on construction sites. This method, based on the Bayesian Network (BN) and AHP solution, calculates the contribution rate of each risk factor to a particular accident.  
Additionally, studies have highlighted that designers often lack familiarity with construction risks and may be reluctant to prioritize safety in their design decisions [17]. Collaboration among stakeholders, ongoing training, and integrating safety considerations into design and planning processes are essential for mitigating risks and promoting a safer work environment.  
Consequently, the mission aspect underscores the dynamic nature of construction work, necessitating innovative risk assessment models that account for the evolving work environment and project-specific challenges.
## 3 Methodology Description
### Experimental Approach
#### 3.1.3 Phase-Shift, \(\phi\)  
The phase-shift \(\phi\) is defined as the angle related to the time-shift between the vertical sloshing force peaks \(F_{slosh}\) and tank position peaks \(y\). The first 3 time-shifts \(\Delta t_{i}\) are shown in Figure 4 for a water test at 50% fill level and \(a_{0}=10\) g.  
For each time-shift, a phase-shift between the sloshing force is defined and the tank motion as \(\phi_{i}=\Delta t_{i}\omega_{0}\), and the averaged phase-shift for one experiment is the mean over the first 10 phase-shifts.  
Keeping the experimental rig constant, this means using the same linear guide, springs, measuring devices, and rest of mechanical auxiliary parts; the parameter space that this problem involves is still large. The dimensional analysis of this problem has already been performed in several papers; see [8; 27; 29]. The remaining degrees of freedom of the experiment can be listed as:  
* Sloshing liquid. Changing the liquid that partially fills the tank implies changing the density, viscosity and the surface tension of the dominant fluid.
* Initial amplitude \(y_{0}\). This change implies a different initial acceleration and inertial forces.
* Fill level. This change implies varying the liquid mass inside the tank and consequently the mass ratio \(r=m_{l}/m_{s}\) between the liquid and the solid moving masses.
* The global tank dimensions can also be scaled; see [27].  
Figure 4: Normalized vertical sloshing force and tank position for a water test at 50% fill level and \(a_{0}=10\) g. The first 3 time-shifts have been indicated as \(\Delta t_{1}\), \(\Delta t_{2}\) and \(\Delta t_{3}\).  
The variation of these degrees of freedom, which imply the variation of the dominant non-dimensional numbers of the problem in the context of our experimental rig, was thoroughly studied experimentally in [27] and computationally in [26], where it was found that the maximum damping is obtained for a 50% filling level, and the sloshing-induced damping ratio increases with the liquid density. Additionally, it has been concluded that the damping ratio increases with the initial amplitude up until a threshold amplitude of \(y_{0}/H\approx 0.4\), after which it starts to decrease. No relevant influence on the damping ratio has been reported due to surface tension variation. The damping ratio is unaffected by liquid dynamic viscosity changes if the flow is at high Reynolds numbers.