esa-sceva/satcom-chunk-collection huggingface dataset:
# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 1 Introduction
When a person focuses on an item, a picture of that object is formed on the retina, and objects that are closer to or farther away from the accommodation distance are perceived as fuzzy pictures. Objects that lay within a limited zone around the accommodation point, however, might be regarded as having a high resolution (i.e., not blurred), with the extent of this region known as the depth of field (DOF) [16]. Figure 1: Typical model of no-reference video quality measure. The geometry of stereopsis depicted in Figure 2 corresponds to the experimental setting of the two-needle test [17] with a fixation point \(B\). The theoretical depth discrimination \(\Delta f\), according to it, can be determined from the definition of the convergence angle \(\alpha\) by applying \(d\alpha/df\), which provides  
\[\Delta f=-\frac{f^{2}}{b}\left(1+\frac{b^{2}}{4f^{2}}\right)\cdot\Delta\alpha \tag{1}\]  
with \(b\) the interpupillary distance and \(f\) the mean object distance. With \(4f^{2}\gg b^{2}\), which is even valid for a near point distance \(f_{near}=250\) mm, Equation (1) simplifies to the common form \(\Delta f\approx-f^{2}\frac{\Delta\alpha}{b}\). In binocular vision, \(\Delta\alpha\) is the stereoscopic acuity (the smallest detectable depth difference) which is required to get a proper binocular or 3D depth perception. Scientifically, it is observed that \(\Delta\alpha=10\)\(arcsec\), (a tolerable value under photopic lighting conditions) which is added to move the view from fixation point A (\(\alpha-\Delta\alpha\)) to (\(\alpha\)) and for more depth perception B (\(\alpha\)) to C (\(\alpha+\Delta\alpha\)). The minimum detectable depth difference being on the order of 0.3 mm for a fixation distance of 650 mm. Among other factors, stereoscopic acuity is affected mainly by parameters such as an object's luminance and spatial frequency, and angular distances from fixation and object motion. Therefore, a multidimensional 3D visual experience model needs to be defined to incorporate the aforementioned acuity factors based on their perceptual importance. Effective 3D video quality evaluation schemes should be designed based on the observations which will allow us to (i) synchronize between the two views of stereoscopic video, (ii) adjust parameters to maximize overall quality and comfort, (iii) control visual quality during transmission, and (iv) define the levels of quality for specific video services. The main goal of this research work is designing an NR quality metric for evaluating 3D videos considering different aspects of human binocular perception and real-time transmission. In Section 2, we include a summarized discussion of existing relevant research on 3D quality assessment and Section 3 introduces the proposed NR-based approach with a detailed algorithm presented in the subsections. The experiments and final results are discussed in Section 4. Finally, Section 5 concludes this research with the directions on possible future works.
## 5 Smart Grid Reliability Assessment  
### Model Structure  
A simple distribution system structure was created to analyze the reliability assessment of an electric power network coupled with a communications network [80; 81], see Figure 14 in Appendix E. Integrated communication allows for monitoring all nodes in the network, and thus faster detection of the location of failures in the power network and taking corrective actions. The component aging is disregarded and only the constant failure rate related to their useful life is analyzed. Since failures in the power system usually occur randomly, the sequential Monte Carlo method was employed to simulate and assess a smart grid's reliability over time. The method produces a distribution of possible outcomes rather than a single expected value.  
The artificial operating/failure histories of the relevant smart grid elements are generated. The period during which the element is operating is called time to failure (TTF). The period during which the element fails is called time to repair (TTR).  
The parameters TTF, TTR constitute random variables and may have different probability distributions. Exponential distribution is used here to assess the reliability of both the electric power distribution system and the communications network. The exponential distribution's probability distribution function is described as follows [80; 82]:  
\[f(t)=\begin{cases}\lambda e^{-\lambda t},&0<t<\infty\\ 0,&\text{otherwise}\end{cases} \tag{8}\]  
The method for generating an artificial failure history of a component is presented in Figure 4. Each time interval is computed with different random numbers. This simulates contingencies occurring in a real system realistically.  
TTF and TTR are calculated for a given failure rate and repair rate from Table 13 with the formulas [83]:  
\[TTF_{i}=-\frac{1}{\lambda}\ln(u_{i}) \tag{9}\]  
\[TTR_{j}=-\frac{1}{\mu}\ln(u_{j}) \tag{10}\]  
where \(u_{i}\), \(u_{j}\) are random numbers uniformly distributed in the range of 0-1 and \(\lambda\) and \(\mu\) are the failure rate and the repair rate, respectively.  
Figure 4: Method of failure history generation [80; 81].  
The communications infrastructure in smart grid supplies additional information on power system states, thus improving overall network performance. It enables faster detection of and response to failures or even prevents their occurrence. Accessing information faster facilitates earlier dispatching of service teams or faster responses by different system operators to potential imbalances in power systems, for instance.  
By extension, communications shorten interruption times. This method, thus, entails shortening interruption times by shortening the time to repair for combined system. The shorter time to repair is denoted as \(\text{TTR}_{\text{SG}}\) (see Figure 5) and simulated. Since communications are assumed to improve distribution system reliability, their absence or failure do not diminish an electric power system's (EPS) performance, as shown on the right in Figure 5.
## 2 Materials and Methods
### Systems for Hand Tool Tracking
#### 2.1.3 System Based on Azure Kinect Body Tracking  
The system based on the Azure Kinect Body Tracking exploits the 3D body configuration estimation performed by the AKBD SDK of Microsoft. The system is based on the tracking of the upper link main segments to estimate a specific tool's point if the relative position of such a point, with respect to the hand reference system, is known.  
The Azure Kinect from Microsoft is a cutting-edge spatial computing developer kit with sophisticated computer vision and speech models, advanced AI sensors and a range of powerful SDKs. It is equipped with several sensors in order to sense the surrounding environment; the device integrates a 12-megapixel RGB camera supplemented by 1-megapixel depth camera, a 360-degree seven-microphone array and an orientation sensor. The main modules of the SDKs are the Sensor SDK and Body Tracking SDK. The first one is designed  
Figure 3: YOLO object detection output for office scene (from [PERSON], CC BY-SA 4.0, via Wikimedia Commons).  
for the interface with sensors and for managing data provided by them, automatically handling the problem of RGB and depth camera data alignment; the Body Tracking SDK is based on a complex deep learning model that supports body segmentation, human skeleton reconstruction, human body instance recognition and body tracking in real-time. The model recognizes 31 joints of the human body, each joint with its own reference system organized in a hierarchical structure, as shown in Figure 4. The deep learning model is able to reconstruct the entire human body model, where the higher the accuracy, the better the visibility of the body; the model has a good tolerance to the occlusions only for the highest joints of the hierarchical structure.  
Body Tracking SDK is able to detect and track all joints of a human body in the scene, returning position and orientation (in quaternion form) for each of them [25; 26].  
Intuitively, the kinect hand joints, i.e., n.8--left hand and n.15--right hand, should be the most useful to estimate the power drill chunk pose. Unfortunately, several tests led the authors to exclude the above-mentioned joints because experimental trials proved the poor quality of the orientation prediction. In fact, most of the time, the model predicted the power drill in the scene as part of the hand of the operator. Thus, the joints of the wrists (n.7--left and n.14 right) have been considered. The downside of this choice is that the tool pose changes relative to the wrist flexion/extension, and wrist radial-ulnar deviation movements are not considered.
## 3 Results and Discussion
### TGA Analysis  
In this work, TGA analysis was used to test the thermal stability of TiO\({}_{2}\) nanoparticles, POA, PPA, POA@TiO\({}_{2}\), and PPA@TiO\({}_{2}\). The TGA profiles as a function of temperature in all synthesized materials are shown in Figure 3b.  
The TGA curve of TiO\({}_{2}\) presents minor weight loss below 450 \({}^{\circ}\)C, which can be associated with the elimination of H\({}_{2}\)O, ethanol, and partial dihydroxylation of TiO\({}_{2}\) nanoparticles [12].  
The TGA of all samples underwent four weight loss steps. An initial weight loss (5%) at temperatures over 120 \({}^{\circ}\)C was attributed to the evaporation of entrapped H\({}_{2}\)O, solvent, and monomers in the samples [39; 40; 41]. The second step of decomposition takes place at 160 \({}^{\circ}\)C to 450 \({}^{\circ}\)C and corresponds to the elimination of the oxidant. The third step of weight loss was observed in the range of 300 \({}^{\circ}\)C-500 \({}^{\circ}\)C and attributed to the removal of the acid dopant. Finally, the complete decomposition of polymer chains began near 500 \({}^{\circ}\)C and continued up to 630 \({}^{\circ}\)C for PPA and its nanocomposite PPA@TiO\({}_{2}\)[41]. By contrast, it was 700 \({}^{\circ}\)C for POA, and even after 700 \({}^{\circ}\)C, total decomposition did not occur [42]. POA@TiO\({}_{2}\) showed lower weight loss between 410 \({}^{\circ}\)C and 800 \({}^{\circ}\)C and the residue remaining in this zone provided an approximate estimate of filler content.  
Moreover, at temperatures up to 600 \({}^{\circ}\)C, 62.5% of the PPA and 47% of the POA had decomposed. The POA@TiO\({}_{2}\) nanocomposite presented greater thermal stability with a total mass loss of 25%. By contrast, PPA@TiO\({}_{2}\) presented a higher mass loss of 32%. These results indicate that the interaction between the TiO\({}_{2}\) particles and polymer chains may have limited the thermal motion of TiO\({}_{2}\) particles and provided thermal stability to nanoparticles [43].  
The weight mass loss of the three prepared polymeric samples displayed finite differences in their thermogravimetric analysis, indicating a minor decrease in the polymers' weight mass loss compared to nanocomposites. Therefore, the nanocomposites' thermal degradation presents higher stability than polymers. Additionally, the results show that the TGA curve of POA@TiO\({}_{2}\) lost less weight than the PPA@TiO\({}_{2}\) nanocomposite.
# Magnetohydrodynamic Nanoliquid Thin Film Sprayed on a Stretching Cylinder with Heat Transfer
## 5 Discussion
### Pressure Distribution  
This section describes the characteristics of pressure \(\frac{P-P_{\infty}}{c\mu_{nf}}(\zeta)\) in terms of various parameters. Pressure has important contribution in fluid motion and blood flow. Blood circulates in the veins due to pressure. In atmosphere, pressure is inevitable to exist. There are so many machines working due to pressure. Figure 13 indicates that pressure enhances for the greater values of thin film parameter \(\beta_{1}\). Pressure distribution is less in magnitude in the wider part of the channel of the motion. The pressure becomes strong with the greater size of film and more power is required to overcome the stress generated due to the thickness of film. The pressure and the film thickness posses a huge force in joint collaboration. This force is very important and is used to help in moving the vehicles/objects over water, in windmills when the fluid flow and objects motion are in the same direction otherwise this force opposes the motion (in case of opposite directions) of the bodies/boats/ships. Figure 14 demonstrates that with the rise of volume fraction parameter \(\phi\) the pressure distribution \(\frac{P-P_{\infty}}{c\mu_{nf}}(\zeta)\) rises because with the addition of nanoparticles the concentration increases, consequently the pressure enhances. When the concentration increases then the fluid becomes thick as a result collision of atoms/molecules increase and exert a great pressure with one another and on the walls of vessel. This characteristic of fluid is very useful for blood flow and for the concentration in medicine. Thick blood runs fast in moving down through pipe due to pressure when injected to the patient. Apart from this, almost all the chemical reactions are made at high pressure. At high pressure, cooking is done easily compared to low pressure. The pressure distribution \(\frac{P-P_{\infty}}{c\mu_{nf}}(\zeta)\) reduces with greater quantities of Reynolds number \(Re\) as it is depicted by Figure 15. Pressure automatically goes to minimum value in the wider way of motion due to inertial effects. Due to inertial forces the particles of the fluids are packed tightly and firmly and high pressure is required to make motion from the rest. Pressure has no effect on these attractive forces. High pressure is required to overcome these intermolecular forces. The impact of magnetic parameter \(M\) lies in Figure 16. It states that pressure distribution \(\frac{P-P_{\infty}}{c\mu_{nf}}(\zeta)\) is low due to the Lorentz force in the wider channel of the flow. The magnetic is applied perpendicular to the flow of fluid. Lorentz force capture the fluid in the boundary layer. To compete the Lorentz force due to strong magnetic field, pressure must be high in order to make the motion of the fluid.  
Figure 12: Non-dimensional temperature \(\theta(\zeta)\) sketch for \(h=-1.10\), \(\beta_{1}=1.10\), \(M=0.10\), \(\phi=0.04\), \(Re=0.70\) and various values of \(Pr\) (CuO-H\({}_{2}\)O).
## 4 Response Characteristics of Stabilizing Piles under Seismic Load
### Bending Moment
After each shaking event, the distribution of the residual bending moment along the instrumented pile gradually increases from the slope surface to the bedrock surface, then decreases from the bedrock surface to the pile bottom, showing an outward convex shape (Figure 16a-c). The position of the maximum residual bending moment is near the bedrock surface, which is related to the earth pressure acting on the stabilizing piles. After seismic loading at PGA = 0.125 g, the residual bending moment along the instrumented pile begins to increase, indicating that the overlying deposit begins to slide under the influence of the ground motion, which is consistent with the phenomenon whereby the aforementioned slope crest settlement starts to occur in shaking event EQ2 (Figure 11b). In the first three shaking events (EQ1, PGA = 0.055 g; EQ2, PGA = 0.125 g; EQ3, PGA = 0.236 g), the peak value of dynamic bending moment appears at measurement point S3 (H = 5.5 m), that is to say, it reaches its peak value near the bedrock-deposit interface: however, in EQ4 (PGA = 0.421 g), the positive maximum dynamic bending moment is still located at S3 at t = 159.19 s, and the bending moment measured at S2 rapidly approaches that at S3 at t = 159.97 s. At t = 161.84 s, the positive bending moment at S2 has exceeded that at S3, that is, the positive maximum dynamic bending moment is located at S2 (H = 3.5 m). The residual bending moment after earthquake EQ4 and the maximum positive dynamic bending in EQ5 are finally located at S2 (H = 3.5 m) (Figure 16d,e). This shows that, under strong earthquake excitations, the part of the bedrock surface behind the stabilizing piles begins to yield, and the bearing layer of the bedrock socketed section moves down, as depicted in Figure 17, indicating that the strong seismic loading has significantly changed the stress state in the embedded section of the pile. It can be foreseen that, when the seismic load is further increased and the overturning moment generated by the landslide body exceeds the stabilizing moment provided by the bearing capacity of the bedrock, the stabilizing piles will undergo tilting-overturning failure. It can be seen from Figure 16d,e that, if the slope model has experienced strong ground motion (e.g., EQ4, PGA = 0.421 g) beforehand, when the slope experiences a smaller earthquake motion (e.g., EQ5, PGA = 0.236 g) thereafter, the residual bending moment on the stabilizing piles is almost unchanged. When continuous five-level excitation is applied, the maximum residual bending moment approaches 3250 kN\(\cdot\)m. That is, the seismic load causes the overlying deposit to slide, and the permanent thrust generated on the stabilizing piles increases the maximum bending moment by 3250 kN\(\cdot\)m.
# Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser
## 6 Image Quality Improvement Experiment Based on Inertial Reference Laser
### Experimental Method and Process  
The method and process of the image restoration experiment based on an inertial reference laser are illustrated in Figure 11 and are summarized as follows:  
1. A degraded image was taken using the camera, and the inertial reference laser system was used to measure the image motion information of the optical system.
2. The image motion model was reconstructed based on the image motion information, and the image motion degradation model in the integral time was obtained.
3. Based on the comprehensive degradation model, the degraded image was restored using image restoration software to obtain a clear image.
4. The MTF values of the degraded and restored images at a Nyquist frequency were calculated for a quantitative evaluation.
## 4 Conclusions  
Wireless sensor nodes are known for their limited resources such as local memory, processor and other internal components. The deployment strategy of the WSN changes the need for unique energy-saving plans and lifetime saving plans. On this basis, this article has conducted a deep study on WMAC policies, routing protocols and energy optimization solutions. This article found the importance of energy-efficient processes under MAC policies, channel allotment policies and network routing protocols to attain novel growth towards the green computing era. As the applications of WSNs and IoT environments are widely growing around the world, energy consumption and feasible data communication practices are surely expected. Accordingly, this article technically and practically compared the recent contributions of various articles to ensure optimal energy consumption plans in WSNs. This article reviewed various energy-optimized routing protocols, load balancing approaches, variants of the WMAC protocol and data scheduling algorithms under an experimental testbed. The experimental evaluation of the existing energy optimization solutions and load balancing solutions (E1, E2, E3, E4, E5, E6, E7, E8, E9, E10, E11 and E12) against network dynamics (number of nodes, mobility, session, etc.) clarified the contributions of each work. Notably, this experimental survey found a better performance of multi-layer energy optimization policies (MAC and routing protocol) and routing policies than single-layer optimization solutions. From this experimental survey, the research community can understand the practical limitations and benefits of the mentioned techniques. In addition, the future world is in need of tiny yet efficient network participants (nodes) with the hope of safe energy plans. In particular, this article gives a future direction for researchers to build energy-optimized sensor nodes.  
Conceptualization, D.D.; Supervision, R.S. and S.B.G.; Validation, A.S.R. and S.B.G.; Writing--original draft, P.S. and M.S.B.; Writing--review & editing, M.S.R., T.C.M., C.V. and G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. This paper was partially supported by UEFISCDI Romania and MCI through BEIA projects NGI-UAV-AGRO, SOLID-B5G, AISTOR, IPSUS, iPREMAS, ENTA, STACK, IMMINENCE and by European Union's Horizon 2020 research and innovation program under grant agreement No. 883522 (S4 ALLCTIIES) and No. 101008060 (RRREMAKER). The authors declare no conflict of interest.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### Link Received Power
For the method proposed in this study, \(PL\) is calculated considering the log-distance path loss model defined in Equation (10), where \(\lambda\) is the wavelength, and \(d\) is the distance between transmitter and receiver, in meters. The parameter \(\beta\) is the path loss exponent and \(d_{0}\) is the reference distance. \[PL\ (\mathrm{dB})=10\cdot\log_{10}\left[\left(\frac{4\pi d_{0}}{\lambda} \right)^{2}\right]+10\cdot\beta\cdot\log_{10}\left(\frac{d}{d_{0}}\right) \tag{10}\]  
The link power loss (\(LPL\)) is given by Equation (11). \[LPL\ (\mathrm{dB})=PL\ (\mathrm{dB})+LDL\ (\mathrm{dB}) \tag{11}\]  
Finally, the link received power (\(LRP\)) between a smart meter (\(SM\)) and a router (\(RT\)) and/or gateway (\(GW\)) is computed using (12):  
\[LRP\ (\mathrm{dBm})= P_{tx}^{SM}\ (\mathrm{dBm})+G_{tx}^{SM}\ (\mathrm{dBi})\] \[+G_{rx}^{RT/\mathrm{GW}}\ (\mathrm{dBi})-LPL\ (\mathrm{dB}) \tag{12}\]  
where \(P_{tx}^{SM}\) is the SM transmission power, \(G_{tx}^{SM}\) is the SM antenna gain, and \(G_{rx}^{RT/\mathrm{GW}}\) is the router/gateway antenna gain. Equation (12) is also used to calculate the LRP for the link between two smart meters.