esa-sceva/satcom-chunk-collection huggingface dataset:
# A Subjective Study on User Perception Aspects in Virtual Reality
## 2 Overview and Related Work
### Key Factors and Main QoE Aspects  
In Figure 1, we summarize the key factors and main QoE aspects of 360 video. In this study, we cluster these factors into three groups. The first group includes the factors relating to user characteristics such as gender, age, and usage history. In the second group, the included factors relate to system characteristics such as encoding parameters, rendering device, rendering mode, and delay. The third group consists of the factors related to content characteristics of 360 video such as content motion and self-avatar. Here, the term \"content motion\" refers to all motions in a content including camera motion, object motion, and background motion.  
According to Recommendation ITU-T P.10/G.100 [19], QoE is \"the degree of delight or annoyance of the user of an application or service\". In fact, QoE is conceived as a complex and multidimensional concept that consists of multiple aspects. As mentioned in Section 1, four main QoE aspects of 360 video are perceptual quality, presence, cybersickness, and acceptability.
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 4 Calculation of TDMP Normalization Factor Based on Density Evolution
### The Initialization  
Under the BIAWGN channel and Binary Phase Shift Keying (BPSK) modulation, code words \(c_{i}\) are mapped to \(x_{i}=(-1)^{c_{i}}i=1,2,\cdots n\). The received code words are \(y_{i}=x_{i}+n_{i}\), \(n_{i}\) is independent and identically distributed Gaussian random variable and bilateral power spectral density is \(\frac{n_{0}}{2}\). Hence, \(y_{i}\) is a Gauss variable with a mean value of 1 and variance of \(\sigma^{2}\)[18].  
Assuming that the average energy of the signal bit transmitted by the channel is _Eb_, \(N_{0}\) is the power spectral density of noise and the code rate is \(r\). The relationship between the signal-noise ratio (SNR) and noise variance is \(\sigma^{2}=\frac{1}{2r(Eb/N0)}\).  
Suppose that the information is an equal probability distribution [21], where \(Pr(x_{i}=+1)=Pr(x_{i}=-1)=\frac{1}{2}\). The initial information is:  
\[\begin{array}{l}Pr(c_{i}=1|y_{i})=Pr(x_{i}=-1|y_{i})=\frac{1}{1+e^{2y_{i}/ \sigma^{2}}}\\ Pr(c_{i}=0|y_{i})=Pr(x_{i}=+1|y_{i})=\frac{1}{1+e^{-2y_{i}/\sigma^{2}}}\end{array} \tag{19}\]  
The log-likelihood ratio message is:  
\[L(x|y)=\ln(\frac{Pr(x_{i}=+1|y_{i})}{Pr(x_{i}=-1|y_{i})})=\frac{2y}{\sigma^{2}}, \tag{20}\]At this time, the initial information of the LLR is Gaussian random variable [21], whose mean value is \(\frac{2}{\sigma^{2}}\) and variance is \(\frac{4}{\sigma^{2}}\), that is, it satisfies \(LLR\sim N(\mu,2\mu)\), where \(\mu=\frac{2}{\sigma^{2}}\).  
According to the Gaussian distribution of LLR and relevant knowledge of probability theory, its probability density is \(f(x)=\frac{1}{\sqrt{4\pi\mu}}\cdot\exp(-\frac{(x-\mu)^{2}}{\mu})\).  
Introduction of the function \(Q(x)=\int_{x}^{\infty}q(t)dt\), where \(q(t)=\frac{1}{\sqrt{2\pi}}e^{-t^{2}/2}\). Since \(q(t)\) is the probability density function of the standard Gaussian distribution, the relationship between \(q(t)\) and \(f(x)\) is: \(\frac{1}{\sqrt{2\mu}}\cdot q(t)=f(x)\), where \(t=\frac{(x-\mu)}{\sqrt{2\mu}}\).
Existing studies typically use data from multiple arm positions for training, using datasets or feature sets large enough to achieve the desired performance. However, this approach usually fails to achieve the desired performance and can place a significant burden on model updates. To address the above issues, our research objective is to explore an algorithmic framework that can accurately recognize upper arm movements at different arm positions. In order to achieve this objective, a domain adversarial neural network named the Deep Adversarial Inception Domain Adaptation (DAIDA) is proposed. The main contributions of this paper are highlighted as follows:  
* Leveraging the power of the Inception module, we incorporated parallel convolutional kernels of different sizes to enhance the feature-capturing capability of the model. This ensures the model's ability to discern nuanced details crucial for upper arm motion recognition. Meanwhile, our algorithmic framework facilitates the seamless execution of two training phases without modifying any structures. These two training phases can eliminate heterogeneous features to ensure good motion recognition performance at the new arm position. * To validate the effectiveness of our proposed model, we conducted experiments involving both able-bodied subjects and transhumeral amputees. This result showcases the model's motion recognition accuracy and stability at different arm elevation positions, providing prospects for the advancement of myoelectric interfaces.
## 3 CFD Simulation Results
### Soot Formation  
Soot is the primary component of PM (particulate matter) emissions [54; 55; 56; 57]. Under high-temperature and fuel-rich conditions (high fuel-to-air equivalence ratios), which are usually found in the combustion process of DI diesel engines, the diffusion combustion of hydrocarbon (C\({}_{x}\)H\({}_{y}\)) fuels produces a lot of carbonaceous particles (soot). However, under the normal working conditions of ICEs, most soot emissions formed during the diffusion combustion stage (early stage of combustion) are completely burnt, caused by its oxidation with residual O\({}_{2}\) in oxygen-rich regions inside the engine combustion chamber during the later stages of the fuel combustion. This means that, in fuel direct injection ICEs, their PM emission characteristics or the final engine-out amount of soot are actually determined by the completeness of the soot oxidation during the combustion process of the engine [45].  
Figure 12 presents the soot emissions formed inside the engine cylinder in the three cases of using various piston bowls. The simulation results pointed to a similar soot formation process, as presented in the literature [45]. Specifically, soot was mainly formed very early in the diffusion combustion stage, owing to the fuel dissociations under high-temperature and high fuel/air equivalence ratio conditions inside the engine cylinder. Soot was then completely burnt due to the oxidation with residual O\({}_{2}\) in the engine cylinder in the later stages of the combustion. As a result, at the end of the expansion processes of the engine, the final generated soot amount was almost zero. This means that, although there were some differences in soot formation during engine combustion, the final amount of soot produced in all three piston bowl cases was roughly the same. That is, changing the type of piston bowl does not affect the engine-out soot emission characteristics of the engine.  
Figure 13 illustrates the soot and thermal NO zones in the injection region of DI diesel engines. As can be seen, soot is mainly formed in fuel-rich and high-temperature zones.  
Figure 12: Soot emissions diagrams for the three various piston types.  
Meanwhile, NO is strongly formed in very high-temperature zones. These results confirm the simulation results in this study.
## 2 Materials and Methods
### Elastic Constants  
Determining the Young's modulus \(E\) using the stress-strain curve is erroneous given the maximum length of the tensile specimen in the dilatometer is only 10 mm. Impulse Excitation Technique (IET) is a precise and reliable measuring method for the determination of elastic properties. The sound waves emitted from a free vibrating specimen are recorded by a sensitive microphone, allowing to evaluate the fundamental resonant frequencies [29]. In case of metallic alloys the resonance frequencies depend mainly on the geometry of the specimen, the elastic properties, and the density of the material. By analysis of simple geometries, such as cylinders or rectangular bars with respect to the fundamental flexural eigenfrequency, it is possible to calculate the Young's modulus in case of homogeneous and isotropic elastic properties by analytical approaches provided by the ASTM-standard [30]. The simplicity of the experimental set-up allows to perform the measurements at room temperature as well as at elevated temperatures.  
The specimens were rectangular prisms with dimensions of 60 \(\times\) 15 \(\times\) 4 mm\({}^{3}\). A standard specimen with the ferritic-pearlitic microstructure and a hardened specimen with martensitic microstructure were measured using a single specimen. To achieve this, the measurements were done while heating a hardened specimen from room temperature to 900 \({}^{\circ}\)C and also measured while slowly cooling it down after 5 min of soaking to let it austenitize completely. For hardening the specimen, it was austenitized in a heat treatment oven at 860 \({}^{\circ}\)C for 30 min and then quenched in water. Then [PERSON]'s modulus was measured while  
Figure 3: The geometry of the flat specimen used for the tensile tests using a deformation dilatometer (dimensions in mm).  
heating from room temperature at a rate of 16 K min\({}^{-1}\) and while slowly cooling down at 5 K min\({}^{-1}\).  
Following the ASTM-standard, the [PERSON]'s modulus is calculated by  
\[E=0.9465\left(\frac{m\ f_{\mathrm{f}}^{2}}{b}\left(\frac{L^{3}}{l^{3}}\right) \right)T_{1}, \tag{11}\]  
where \(l\) is the length, \(b\) the width, and \(t\) the thickness of the specimen. The specimen mass is denoted by \(m\) and the measured fundamental flexural resonance frequency by \(f_{\mathrm{f}}\). The formula is based on Euler-Bernoulli beam theory, whereby the correction factor \(T_{1}\) accounts for the finite thickness of the specimen.  
A sensitivity analysis of Equation (11) shows that the calculated [PERSON]'s modulus is highly sensitive to the specimen thickness. It is challenging to evaluate the thickness exactly due to the distortion caused during the quenching. Hence, the specimen was subsequently ground with sand paper by hand in order to remove the slight curvature. Finally, the dimensions of the specimen were measured by use of a high precision micrometer screw at six different positions for determining its thickness and width and at three positions for the determination of its length.
## 5 Simulation Results and Discussions
Therefore, as the distance to the communication target becomes closer, the average SINR that is received would increase and thus, the achievable data rate would also increase. The system energy efficiency for different traffic scenarios is compared as a function of the V2V communication distance, as displayed in Figure 10. In Figure 10, it is observed that the system energy efficiency is higher for the high scenario. This is because in the high scenario, the proportion of clustered VEs increases, such that the number of VEs communicating through the RRH decreases. Thus, the system energy efficiency also increases. In addition, it is observed that the system energy efficiency of the proposed algorithm is higher compared to _Compare1_ for the same density although _Compare1_ is also based on the vehicle clustering method. The number of V2I mode VEs in the proposed algorithm is smaller than that of _Compare1_. As a result, system energy consumption is relatively small and interference is reduced, resulting in a higher achievable data rate. Figure 8: Comparison of average SINRs with various maximum cluster sizes: (**a**) With low scenario; (**b**) with high scenario. Figure 3:0 _Cont._  
Figure 3:1 Comparison of achievable data rates with various maximum cluster sizes: (**a**) With low scenario; (**b**) with high scenario. The proposed algorithm exhibited the best performance in terms of system energy efficiency, achievable data rate, and average SINR. The proposed algorithm maximized energy efficiency while ensuring QoS. As mentioned earlier, the system energy efficiency and achievable data rate have a trade-off relationship. The proposed algorithm used vehicle clustering method and DQN based resource allocation method to achieve the highest performance compared to other algorithms.
## 3 Results and Discussion
### Acid Values of CSO  
The predicted acid value of the CSO of the center point (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size > 1.4 mm) STDs 9, 10, and 11 was 1.54 mg KOH/g of oil, whereas the actual acid values of the oil samples were 1.79, 1.33, and 1.69 mg KOH/g of oil, respectively. Another predicted acid value of the center point (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size < 1.4 mm) STDs 20, 21, and 22 was 1.34 mg KOH/g of oil, but the actual acid values were 1.46, 1.07, and 1.41 mg KOH/g of oil, respectively (Table 2). The lowest acid value was observed in STD 21 (restriction die: 0.6 cm, temperature: 60 \({}^{\circ}\)C, and sieve size < 1.4 mm), with an actual value of 1.07 mg KOH/g of oil compared with the predicted value of 1.34 mg KOH/g of oil (Table 2). An analysis of the variance of the reduced quadratic model for the acid value was carried out. The reduced quadratic model for acid value was significant (\(p\) = 0.0008) with adjusted R\({}^{2}\) (0.6601), predicted R\({}^{2}\) (0.5050), and non-significant lack of fit (\(p\) = 0.9395) values. The results indicated that the reduced quadratic model equation was used for predicting the acid values of the CSO samples. The CCD-generated reduced quadratic equation for acid values (mg KOH/g of oil) was as follows (Equations (7) and (8)):  
\[\text{Acid value }(\text{mg KOH per g of oil})=\frac{1}{\frac{1.12}{ \sqrt[1.12]{-1.2176+0.5115\text{A}+0.0499\text{B }-0.0004\text{B}^{2}}}}\text{ }\text{ }\text{(For sieve size : $>1.4$ mm)} \tag{7}\]  
\[\text{Acid value }(\text{mg KOH per g of oil})=\frac{1}{\frac{1.12}{ \sqrt[1.12]{-0.5546+0.5115\text{A}+0.0406\text{B }-0.0004\text{B}^{2}}}}\text{ }\text{ (For sieve size : $<1.4$ mm)} \tag{8}\]  
where A = restriction die (0.4-0.8 mm) and B = temperature (40-80 \({}^{\circ}\)C).  
The reduced quadratic model equation was statistically significant for acid value prediction. Sieve size significantly affected the acid value. The correlation between extraction temperature and the acid value was positive, which indicates that the quality of the CSO depends on the extraction temperature. The acid values of CSO were also influenced by restriction die size (Figure 2).  
Figure 1: Response surface plot showing the effect of temperature and restriction die size on yield of CSO. (**a**) Sieve size: >1.4 mm, (**b**) Sieve size: <1.4 mm.
## 4 An Engineering Application
### Analysis of the Pipeline Settlement Displacement  
#### 4.2.1 Analysis of the Measured Displacement  
The settlement curves of different construction periods are selected for data analysis, and the results of GX04 to GX06 are shown in Figure 15. In this example, the construction period is divided into five stages and lasts for 93 days. The construction activities mainly contain I period, construction of piling (0-14 th day); II period, construction of top beam and excavation (15-30 th day); III period, construction of anchor bar and excavation (31-56 th day); IV period, construction of anchor bar and excavation (57-72 nd day); V period, construction of anchor bar and excavation (73-93 rd day).  
From Figure 15, it can be seen that in the I period, the settlement of the pipeline is basically linearly related to the construction time. However, due to the influence of excavation depth and anchor bar support in different periods, there have been significant fluctuations in pipeline settlement. All excavation of the foundation pit is completed at 93 days, and, thereafter, the increase in pipeline settlement displacement obviously decreases and stabilizes. In the V period, the displacement increase rates of GX04 to GX06 are 0.18 mm/d, 0.11 mm/d, and 0.14 mm/d, respectively, which are less than the warning value of 3 mm/d.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Layer** & **Thickness/m** & **Cohesion/kPa** & **Frictional Angle/(\({}^{\circ}\))** & **Density/(kN\(\cdot\)m\({}^{-3}\))** \\ \hline Miscellaneous fill & 0.5 & 0 & 8 & 16.5 \\ Silty clay & 0.5–5.6 & 16.8 & 22 & 19.3 \\ Sandy clay & 0.9–7.3 & 18.5 & 20 & 19.2 \\ Silty clay & 4.7–10.6 & 30.6 & 12 & 20.6 \\ Fine sandy & 0.8–6.5 & 6 & 15 & 17.4 \\ Silty clay & 3.5–21.5 & 17.6 & 20 & 19.1 \\ Weathered Granite & 19.2–27.5 & 33 & 22 & 21.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of soil.  
Figure 15: Relationship of pipeline settlement with construction time (GX04–GX06).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Distance/mm** & **Number** & **Distance/mm** \\ \hline GX01 & 23,617 & GX02 & 14,549 \\ GX03 & 11,209 & GX04 & 10,744 \\ GX05 & 11,497 & GX06 & 17,048 \\ GX07 & 13,250 & GX08 & 7778 \\ GX09 & 13,174 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length between measuring points and foundation pit side.
## 3 Proposed Hardware Architecture
### Routing Networks  
To facilitate the efficient transfer of data within the architectural framework, a network of six routing channels is implemented. These channels are named Data_RN_1, Data_RN_2, ALU_RN_1, ALU_RN_2, ALU_RN_3, and ALU_Output. The selection of data for processing within these channels is governed by a set of control signals. These control signals are named DATA_CNRL_1, DATA_CNRL_2, ALU_CNTRL_1, ALU_CNTRL_2, ALU_CNTRL_3, and ALU_CNTRL_4. The dimensions of Data_RN_1 and Data_RN_2 channels are \(6\times 1\). The outputs of Data_RN_1 and Data_RN_2 channels are OP1 and OP2. In contrast, ALU_RN_1, ALU_RN_2, and ALU_RN_3 are configured as \(2\times 1\), tailored to efficiently channel data within the Arithmetic Logic Unit (ALU). The size of ALU_Output is \(3\times 1\). These routing networks ensure the seamless flow of information throughout the architecture, thereby enhancing its overall data-handling capabilities.
## 3 AoI Minimization in UAV-Assisted WSN/IoT Application Architectures
### UAV Trajectory
#### 4.1.3 Trajectory and Scheduling
In this regard, the authors design a semi-randomized peak age-optimal trajectory based on the Metropolis-Hastings algorithm and the proposed charging strategy. The agent moves randomly around ground terminals and the power state largely determines when it visits and leaves the charging station. A heuristic trajectory is further proposed to determine the visit of each ground terminal. To fill the gap in the study of AoI-based UAV-aided wireless networks with RF-EH, ref. [85] investigated the joint optimization of UAV trajectory and task assignment in AoI-aware UAV-aided wireless-powered IoT (Figure 11a). In this case, the UAV leaves the data center and flies to each sensor node one after the other to transfer energy to them. It then collects data from the sensor nodes and returns to the data center. The objective, in this case, is to minimize the systems' average AoI. The UAV functions as a mobile energy source that charges low-power ground IoT nodes and performs data collection. The authors consider a nonlinear EH model based on real data measurements. In the aforementioned architecture, they formulate an optimization problem to minimize the average AoI of data collected from the ground sensor nodes by optimizing UAV trajectory, the time required for EH, and the time needed for data collection at each sensor node. The authors show that as  
Figure 10: Illustrative architecture for UAV recharging in post-disaster IoT- (**a**) and UAV-assisted IoT backscatter communications (**b**) based on [5,106] respectively. UAV altitude increases, the AoI increases, and the gap between the nonlinear model and linear EH with respect to AoI is increased. However, the trajectory design of the UAV was created before the UAV's flight and not in real-time [105]. Furthermore, only data collection was studied while data transmission has recently been incorporated with data collection (see [98]). Similarly, the unreliable nature of channel conditions was not captured, unlike in [8]. Moreover, the proposed scheme does not yield optimal solutions but near-optimal results [106].
# When Social Networks Meet D2D Communications: A Survey
## 2 System Description  
As mentioned in the introduction, D2D communications were initially introduced to benefit from [2]:  
* the proximity of devices for assuring higher bitrates, lower delays and reduced power consumption;
* the reuse of resources by simultaneously allowing both D2D direct-link communications and \"infrastructured\" communications (e.g., cellular or WiFi); and
* the use of a single link rather than involving resources for both an up- and a downlink.  
The concepts of social networking can help in finding solutions and improvement for D2D communications in the communication domain where devices can help or be assisted before accessing a network or before starting a D2D communication and in the social domain where devices can create a social network governed by social relationships and events. Considering these two main domains, the following three key issues can be investigated:  
* Relay discovery and peer selection: The efficiency of a D2D communication is mainly related to the neighbor discovery process that is necessary prior to the effective direct communication. Peer selection can be time and energy consuming if network support is not available.
* Communication mode selection: A device can select either an established transmission channel respecting the procedures and constraints of the used network infrastructure, or a D2D communication. Involving all the devices to take this decision can improve data transmission capacity and resource utilization.  
* Spectrum resource allocation and management: D2D communications use the same spectrum resource and air interfaces as normal, structured communications. To use these resources more efficiently, D2D communications may be performed for example by reusing the same frequency channels, involving decision and scheduling methods for spectrum sharing.  
A schematic representation of a D2D communication environment, containing both the social and communication domains, is depicted in Figure 1. The previously identified key issues are inserted in the lower half of the schematic representation. It has to be said that, although identified, these three key issues cannot be clearly divided from the social point of view, considering the fact that some social concepts can interfere simultaneously with more than one issue.  
The next section presents each of these three keys topics in terms of the solutions and algorithms proposed to improve D2D communications performance based on the social components.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Abnormal Detection  
DGGAN adds pseudo-anomaly frame generation based on the reconstructed anomaly detection method. Specifically, the pseudo-abnormal frame \(x^{\prime}\) and the original training frame \(x\) are used to train the reconstruction generator, so that the generator can recognize the abnormal distribution in advance and improve the perception ability of abnormal. In the test phase, the test frame \(x^{\prime\prime}\) is input into the trained reconstruction generator to generate a reconstructed test frame. Comparing the reconstructed frame with the real frame, the calculation process of the reconstruction error \(S(x^{\prime\prime})\)[27] is shown in Formula (16):  
\[S(x^{\prime\prime})=\lambda L_{app}+(1-\lambda)L_{opt} \tag{16}\]  
where \(\lambda\) is a weight parameter.  
After obtaining the reconstruction error, we normalize it to obtain the reconstruction score. The calculation process is as in Formula (17):  
\[Score(x^{\prime})=\frac{S(x^{\prime\prime})-min(S(x^{\prime\prime}))}{max(S(x^{ \prime\prime}))-min(S(x^{\prime\prime}))} \tag{17}\]  
If the reconstruction score of the \(t\)-th frame is less than the threshold, it is determined that the frame is a normal frame; otherwise, it is determined that the video frame at this moment contains an abnormal event.