esa-sceva/satcom-chunk-collection huggingface dataset:
# A Subjective Study on User Perception Aspects in Virtual Reality
###### Abstract  
Three hundred and sixty degree video is becoming more and more popular on the Internet. By using a Head-Mounted Display, 360-degree video can render a Virtual Reality (VR) environment. However, it is still a big challenge to understand Quality of Experience (QoE) of 360-degree video since user experience during watching 360-degree video is a very complex phenomenon. In this paper, we aim to investigate four QoE aspects of 360-degree video, namely, perceptual quality, presence, cybersickness, and acceptability. In addition, four key QoE-affecting factors of encoding parameters, content motion, rendering device, and rendering mode are considered in our study. To the best of our knowledge, this is the first work that covers a large number of factors and QoE aspects of 360-degree video. In this study, a subjective experiment is conducted using 60 video versions generated from three original 360-degree videos. Based on statistical analysis of the obtained results, various findings on the impacts of the factors on the QoE aspects are provided. In particular, regarding the impacts of encoding parameters, it is found that the difference of QoE is negligible between video versions encoded at 4 K and 2.5 K resolutions. Also, it is suggested that 360-degree video should not be encoded at HD resolution or lower when watching in VR mode using Head Mounted Display. In addition, the bitrate for good QoE varies widely across different video contents. With respect to the content motion factor, its impact is statistically significant on the perceptual quality, presence, and cybersickness. In a comparison of two rendering device sets used in this study, there is no statistically significant difference found for the acceptability and cybersickness. However, the differences of the perceptual quality and presence are indicated to be statistically significant. Regarding the rendering mode, a comparison between VR and non-VR modes is also conducted. Although the non-VR mode always achieves higher perceptual quality scores and higher acceptability rates, more than a half of the viewers prefer the VR mode to the non-VR mode when watching versions encoded at the resolutions of fHD or higher. By contrast, the non-VR mode is preferred at the HD resolution.  
subjective experiment; quality of experience; 360-degree video; virtual reality +
Footnote †: journal: Applied Soft
## 5 System Performance Analysis
### System-Level Simulation # 2  
The performance of a digital communication system is typically estimated with the bit error rate (BER), signal-to-noise ratio (SNR) and EVM [51]. EVM is defined as the magnitude of the difference between a complex transmitted data symbol and its ideal counterpart. It is a figure of merit of transmission accuracy and is primarily a quantity of concern in link budget analyses of transceivers [52; 53]. Relating EVM to individual component performance is a challenging task [54]. In addition to the worst-case EVM for \(\pi/4\) DQPSK- and 64-QAM-modulated signals, a technique is presented in this section to characterize the EVM of Path-1 in Figure 16a and demonstrate how it is related to the component parameters. Before calculating the EVM for 5G NR radio transmission and reception in practice, the measured waveform is corrected by sampling the timing offset, RF frequency offset, removal of carrier leakage and equalization in the receiver. The EVM level of each NR carrier for 5G is specified by 3 GPP: QPSK at 17.5% and 64 QAM at 8%, respectively [17; 55].  
The ideal and distorted constellations of the 28 GHz RF carrier modulated by \(\pi/4\) DQPSK are shown in Figure 18. EVM includes the effects of the non-linearity of the diplexer, coupler and PA in this case. The symbol rate is set equal to the bandwidth of the band n257 filter (3 GHz). Its total EVM is 2%, out of which 0.1% is contributed by the amplifier. The remainder is the EVM signature of the integrated passive component block. It is to be noted that the source power is set to 0 dBm so that the amplifier operates in linear region. Similarly, the ideal and distorted constellations of the 28 GHz RF carrier modulated with a 64-QAM signal and a 3 GHz symbol rate are depicted in Figure 19. For this case, the total EVM is 5.6%, which meets the 3 GPP specification.  
Figure 17: **(a)** Group delay distortion observed in path–1, and **(b)** power changes at detector port of coupler due to variations in RF source power (path–2).
# A PEKS-Based NDN Strategy for Name Privacy
## 3 Fundamentals of PEKS-Based NDN  
In PEKS-based NDN, [PERSON] in Figure 4 is a data producer. She owns a pair of private and public keys. She uses her own private key to create a trapdoor for each component \(N_{k}\) of a name. The outputs are then concatenated by using separator \"/\" and a trapdoor name is produced as \(/T_{N_{1}}/T_{N_{2}}/.../T_{N_{k}}\). Hereafter, the notation \(T_{W}\) in Figure 4 is changed to \(T_{N_{k}}\) to represent a trapdoor of each name component \(N_{k}\). When a consumer, [PERSON], wants to send an interest packet to [PERSON], he uses [PERSON]'s public key to transform each name component \(N_{k}\) to PEKS ciphertext \(C_{k}\) and obtains an encrypted name \(/C_{1}/C_{2}/.../C_{N_{k}}\). Routers in the network take the role of the server of Figure 4 and use the test algorithm of the PEKS scheme to decide where to forward packets or to search in CS. In these operations, the PEKS scheme is applied to the name of NDN packets. Name components becomes keywords in PEKS-based NDN. Only name is encrypted in this work.
## 2 System Principle  
### The LED-ID Detection and Recognition  
The VLP system is mainly comprised of two processes: LED-ID recognition and the positioning algorithm. The recognition of LED-ID is realized by using the image sensor-based VLP, which utilizes the rolling shutter mechanism of the CMOS image sensor. The working principle of CMOS sensors and Charge Coupled Devices (CCD) sensor are shown in Figure 1. With a CCD sensor in Figure 1, all pixels on the sensor are simultaneously exposed, thus, at the end of each exposure, the data of all pixels are simultaneously read out. This mechanism is often referred to as the global shutter of a CCD sensor. However, CMOS has a completely different working mechanism than CCD sensor. For a CMOS sensor, the data of this row are immediately read out whenever the exposure of one row is completed, which means that the exposure and data reading are performed row by row. This working mechanism is called the rolling shutter mechanism of the CMOS sensor. The image captured by the CMOS sensor would produce bright and dark stripes while turning the LED on and off during a period of exposure due to the rolling shutter mechanism of CMOS sensor [6; 22].  
For the same LED project, the change in distance from the CMOS sensor does not affect the width of light and dark stripes, because the scanning frequency of the CMOS sensor is fixed. However, the area of the LED project on the CMOS sensor will decrease as the distance between the LED and the camera increases, and this will also result in a reduction in the number of light stripes and dark stripes. Therefore, the area of the LED project, the number of bright stripes and the duty-ratio of the bright stripe were selected as the features of each LED, and a classifier was used to identify different  
Figure 1: Schematic diagrams of the: (**a**) global shutter of the CCD sensor; and (**b**) rolling shutter of the CMOS sensor.  
LEDs. Our method is different from the traditional modulation and demodulation method, which introduces the classification algorithm from the field of machine learning to solve the problem of LED-ID detection and recognition. We modulate each LED using a varying frequency and duty-ratio Pulse-Width Modulation (PWM) method to enable the LED-ID to overproduce three identifiable features: the area of the LED project on the CMOS sensor, the number of the bright stripes, and the ratio of the width of the light stripe to the width of the light stripe and the dark stripe (the duty-ratio of the bright stripe). To identify LED-IDs using features that would be extracted by the image processing method, we use the Fisher classifier and the linear support vector machine through off-line training for the classifiers and online recognition of LED-ID. Experiments show that the proposed scheme could improve both the speed and accuracy of LED-ID identification and make the system more robust. The detail process of LED-ID detection and recognition has been deeply discussed in our prior work [22] and it can be seen in Figure 2. For the readers that are interested in the LED-ID modulation and recognition, please refer to our previous reports.
## 2 The Conventional Language Identification Approaches
### Classification Approaches for Language Identification  
In addition to feature extraction approaches, there have been significant efforts in terms of pattern classification. Various pattern classification approaches such as SVM and LDA have been employed for LID. The SVM is a representative supervised learning method that has been used for classification, regression, and outlier detection [21]. It aims to derive a kernel function from a set of training data for the purpose of setting a criterion for classification. The LDA regards the distribution of feature vectors as multivariate normal distribution [22; 23]. The main objective of LDA is to find an optimal linear decision boundary that furthers the distance of mean values of each data group while making the covariance of data belonging to each group smaller.  
In recent years, deep learning-based classification approaches have been investigated for LID. The Recurrent Neural Network (RNN)-based modeling approach, which widely uses the Long Short-Term Memory (LSTM) as an architecture, is representative as it is very pertinent to time-series data. A more sophisticated LSTM called the bidirectional LSTM was applied for CNN-based LID [24].
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution
### Reset Criterion  
When the frequency is stable and the maximum absolute value of the rate of change of frequency in the last five power cycles is less than threshold, the calculation of the accumulated phase angle drift is terminated and the state of the power system returns to \"Normal Status\". To improve the reliability, the time window for \(df/dt\) calculation should not be too short. The reset criterion is expressed in Equation (13).  
\[Max\{\left|\frac{df}{dt}(n-4)\right|\cdots\left|\frac{df}{dt}(n)\right|\}<( \frac{df}{dt})\_set \tag{13}\]
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs  
To improve the QoS of optical sensing and optical communication against node/link failures, many survivability techniques have been developed. The traditional survivability techniques can be divided into two categories: protection schemes and restoration schemes. The protection scheme needs to conduct resource reservation before failures. Since it avoids the complex routing and signaling processes, the protection scheme has a short recovery time. Moreover, the protection scheme can also be classified as dedicated and shared protection. Dedicated protection only assigns backup resources to specified working traffic, but shared protection allows multiple backup paths sharing the spectrum resource on common links as long as their working paths do not fail simultaneously. Compared with dedicated protection, shared protection has higher spectrum efficiency. The restoration scheme attempts to establish a new recovery path for the interrupted service by employing the remaining available resources after a failure. It needs to conduct routing and signaling processes to establish a new light path. Therefore, the restoration scheme requires more time than the protection scheme. Moreover, the restoration scheme may fail when the remaining available resources are inadequate. In this section, we first elaborate the traditional survivability techniques designed for optical sensing, such as the fiber bus protection scheme [40; 41; 42; 43; 44; 45] and self-healing architecture [46; 47; 48; 49; 50; 51; 52; 53]. Then, we elaborate the traditional survivability techniques that can be applied in OSNs, such as \(1+1\) protection and extensions [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72], the photonic millimeter-wave bridge scheme [73; 74; 75; 76; 77; 78], the p-cycle scheme [79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97], the p-polyhedron scheme [98; 99; 100], the multipath protection scheme [111; 112; 113; 114; 115; 116; 117; 118; 119; 120], and the restoration scheme [121; 122; 123; 124; 125; 126]. Finally, we conduct performance analysis for the above-mentioned survivability technologies.
## 4 Application and Results
### Decision Result of Water Sensor Installation Pipeline
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Priority**} & \multirow{2}{*}{**Pipe Name**} & \multicolumn{2}{c}{**Abnormal**} & \multicolumn{2}{c}{**Normal**} \\  & & **Situation** & **Situation** & **Situation** \\  & & **Sensitivity** & **Sensitivity** & **(1,0)** \\ \hline \multirow{4}{*}{\(1\)} & \#D744 & 0.96 & 0 & 0.04 \\ \cline{2-5}  & \#D568 & 0.96 & 0 & 0.04 \\ \cline{2-5}  & \#D680 & 0.96 & 0 & 0.04 \\ \cline{2-5}  & SA-25 & 0.96 & 0 & 0.04 \\ \cline{2-5}  & LE-99-3 & 0.96 & 0 & 0.04 \\ \hline \multirow{4}{*}{\(2\)} & LB-67 & 0.88 & 0 & 0.12 \\ \cline{2-5}  & LG072(5) & 0.88 & 0 & 0.12 \\ \cline{2-5}  & \#D685 & 0.88 & 0 & 0.12 \\ \hline \multirow{4}{*}{\(4\)} & \#D686 & 0.8 & 0 & 0.2 \\ \cline{2-5}  & \#D666 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & SA-24 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & LA-03 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & LE-75 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & MP-24 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & MP-23 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & MP-22 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & LE-74-2 & 0.72 & 0 & 0.28 \\ \cline{2-5}  & LE-94 & 0.72 & 0 & 0.28 \\ \hline \multirow{4}{*}{\(5\)} & LG-106 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & LB-66 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & LB-62 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & LB-60 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & SA-43 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & \#D740 & 0.64 & 0 & 0.36 \\ \cline{2-5}  & \#D623 & 0.64 & 0 & 0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measuring instrument location decision priority result (Case 4).
###### Abstract  
Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.  
multi-operational energy management; IDE; RES; smart microgrid; optimization; cost effective; gas emission +
Footnote †: journal: Applied Sci.
# Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm
## 1 Introduction
It employs hypothetical orbital layers and photon absorption and emission to find the best possible solution [31]. It is a population-based algorithm, and its performance comes from the fact that it can find the best global solutions quickly and with fewer iterations. A memory-based improved gorilla troops optimizer (MIGT), demonstrating outperformance of other metaheuristic algorithms by combining the gorilla memory-saving technique and the explorative gorilla with an adaptive mutation mechanism [32]. However, the optimization process requires a high number of iterations in order to obtain the optimal solution, including increased computational load due to the memory-saving technique and potential performance fluctuations in high-dimensional PV models. While parameter extraction in PV systems has been widely studied using metaheuristic algorithms, the current literature still struggles with balancing accuracy, convergence speed, and scalability. The novelty and contributions of the proposed work for extracting parameters of photovoltaic cells and panels are the following:  
* The work is the first that uses the PID-based Search Algorithm (PSA) to extract parameters for both single- and double-diode models for six types of photovoltaic cells and panels (RTC France, amorphous silicon, and monocrystalline silicon photovoltaic cells, PVM 752 GaAs, and STM6-40 photovoltaic panels). Additionally, multijunction solar cells at three temperatures (41.5 \({}^{\circ}\)C, 51.3 \({}^{\circ}\)C, and 61.6 \({}^{\circ}\)C) were utilized. * The PSA was compared with twenty-six top-performing algorithms and consistently achieved RMSE and MBE levels that are equivalent to or better than the best-performing algorithms. It also proved computationally efficient, requiring a smaller population and faster extraction times, validating its effectiveness for both PV cells and panels. * The PSA's performance is considerably improved by adjusting the proportion, integral, differential, and beta (\(\beta\)) parameters, aiding the algorithm in both exploration and exploitation phases. * Improving the effectiveness of the PSA by calculating current and historical errors of the deviation signal outside the main iterative loop provides crucial beginning values for the incremental PSA and a solid foundation for further computations. * Implementing a Levy flight-based zero-output mechanism that allows the PSA to probabilistically adjust to avoid local optima entrapment. This mechanism enables broader search diversity early in the process, whereas \(\lambda\) is decreased over time in order to gradually shift the algorithm's focus on exploitation in later stages. The following is the plan for this paper: photovoltaic cell and panel models, statistical tests, problem complexity, and the proposed algorithm are described in Section 2. Results and discussions are further discussed in Section 3. Finally, the conclusions and suggestions for further study are presented in Section 4.
# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 2 Related Work
### GAN-Based Image-to-Image Translation  
Recent studies have explored various models with GANs for image-to-image translation. For example, Pix2 pix, the first GAN-based approach of [PERSON] et al. [3], used a conditional, generative adversarial network [19] for image translation, when a source and target image pair was given. Similar methods have been adopted for several tasks, such as synthesizing a photograph from a sketch [20] and changing the weather of an original image [21]. More recently, [PERSON] et al. [18] proposed a spatially-adaptive normalization layer to consider semantic information.  
When paired training data were not available for some tasks, other studies have suggested several approaches to transform from an unpaired image. CoGAN [22] used a weight-sharing constraint to learn a joint representation over multi-domains. CycleGAN [8] used cycle consistency [23] and least-squares loss [6]. DualGAN [24] suggested a similar method to CycleGAN but used a Wasserstein loss [25] instead of the least-square loss. UNIT [26] and CD-GAN [27] used a shared latent space and cycle consistency for better quality and accuracy.  
Unlike the approaches described above, our method is not limited to a specific task, nor do we rely on predefined relationships between the source and target domains. Our method can be applied to make a general-domain solution for many image-to-image translation tasks. We compare our trained model against previous approaches and experiment with our approach under various conditions in Section 4.
## 3 Results and Discussions  
### SPME Optimization  
To guarantee the higher extraction rate with shortest time, the extraction profile was determined and the extraction time during SPME optimized for a representative beer. As shown in Figure 1, five extraction times were evaluated and total volume of peaks were used as response parameter to ensure adequate transfer of the analytes from the sample matrix to the extracting phase.  
The extraction profile reveals that the equilibration time for the analytes is reached within 40 min of extraction, after that the amount of analyte extracted is about the same. Therefore, to ensure sample throughput, extraction time was fixed in 40 min.  
Figure 1: Extraction profile of volatile and semi-volatile organic compounds (VOC) from beer. The response is presented as the sum of the peak volumes from the GC\(\times\)GC chromatogram.