esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Effectiveness in General Applications (TID2013)  
In the second experiment, all tested metrics were compared against the TID2013 database [43], which includes 24 miscellaneous types of distortions, such as several types of noise, blurring, compression, change in brightness and contrast, as well as compression and transmission errors. It is important to note that the TID2013 database does not contain images with reprojection-related types of distortion (described in Section 3).  
The correlation (calculated as SROCC) between subjective and objective quality was estimated independently for each type of distortion. Figure 5 presents SROCC and PLCC values averaged over all 24 types of distortions.  
As presented in Figure 5, the proposed IV-SSIM metric outperforms most tested metrics also for non-immersive video applications. For SROCC, IV-SSIM is the second-best metric, and its performance is nearly the same as that of VMAF. VMAF is a meta-metric containing several sub-metrics combined with weights trained for natural images; therefore, its high performance for natural images from the TID2013 database is not surprising. Considering PLCC, it is only worse than VMAF, PSNR-HA, and PSNR-HMA (however, the difference is negligible). Such a discrepancy shows that the correlation between IV-SSIM and MOS is less linear than for these three metrics. However, correlation linearity is less crucial than the ability to assess which video or image is better (what is measured by the rank-order SROCC metric).  
The discrepancy between the two used correlation coefficients exists (and it is consistent for all SSIM metrics: IV-SSIM, SSIM\({}_{\text{VUV}}\), and SSIM\({}_{\text{Y}}\)), but its impact can be considered negligible, as both coefficients indicate that the proposed IV-SSIM metric performs well even though it was designed for handling a different type of data. Therefore, it could be stated that IV-SSIM properly mimics the subjective perception of quality not only for immersive video but also for any video-related application.  
Figure 4: SROCC and PLCC values for all considered metrics; immersive video coding scenario.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 4 Heterogeneous Graph Structure Learning Model
### Sequence Modeling
#### 4.4.1 Construction of Sequence Graphs  
For any given sequence \(s\), we transform the sequence into a graph \(G_{s}\), which captures the correlation between nodes from the graph perspective. Specifically, the graph \(G_{s}\) uses the visited POIs as nodes and the order of user visits as edges. In other words, if a user visits POI \(i\) and then POI \(j\) in the same sequence, there is a directed edge from node \(i\) to \(j\). Additionally, to capture the spatio-temporal relation between two check-ins, we also consider the time interval and spatial interval between two check-ins.  
Unlike the sequential model, which can only learn about the information related to visited points, using a graph data structure can better capture the connections between different POIs, especially those between non-adjacent POIs. However, considering only the current users' trajectory may not fully reflect their behavior, as the same trajectory segments can lead to users visiting different POIs. Utilizing all the check-in history may also include much noise. Therefore, to better model sequence information, this paper extracts sequences from both the users' historical sequences and that of other users, which can help predict where the user is inclined to visit. Specifically, for the currently known trajectory \(s_{1}\), if there is a historical sequence \(s_{2}\), which makes \(Jaccard(s_{1},\ s_{2})\) large, we treat \(s_{2}\) as a similar sequence to \(s_{1}\). The Jaccard index is computed as follows (Reviewer 1, Comments 3):  
\[\text{Jaccard}(s_{1},s_{2})=\frac{|s_{1}\cap s_{2}|}{|s_{1}\cup s_{2}|} \tag{14}\]  
Note that this paper distinguishes between whether a trajectory belongs to the same user when considering similar trajectories. We select \(k_{1}\) sequences of the same user, and \(k_{2}\) for different users. In the end, together with the current sequence, the total (k\({}_{1}\) + k\({}_{2}\) + 1) sequences are used to construct the sequence graph.  
The order of users' visits is a critical factor when we are tackling the next POI recommendation task. The _Jaccard_ index is used to choose the similar sequences, while the order is considered in the construction of sequence graphs and the following sequence modeling. We separately considering choosing the similar sequences and the order of visits, allowing us to apply more complex order-based sequence preference learning.
## 3 Simulation Results
### The Maximal PV Real Power Injection for Different Power Factor Control Abilities  
Neglecting the period with very low level of irradiation, for load between 8:00 a.m. and 6:00 p.m., the maximal PV real power injection at each hour for different power factor control abilities is calculated based on the power flow program of OpenDSS and the real and reactive power control scheme of the smart PV inverter, and depicted in Figure 6.  
From Figure 6, we can see that (1) the stronger the smart inverter's power factor control ability, the larger the allowable real power injection amount; that is, the PV installation capacity can also be increased; (2) the maximum real power injection for the power factor control abilities 0.9 and 0.85 are the same after 8 o'clock; that is, the smart PV inverter with more power factor control ability does not further increase the PV real power injection; thus, the smart PV inverter with power factor control ability _pf_ = 0.9 will be sufficient to play the role to increase the PV power injection to the feeder.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 2 Optical Camera Communication  
While VLC receivers based on photodiodes convert the incident light into a photocurrent, a camera returns a color image of the light source and its surrounding environment. This fundamental difference in the output signal of an image sensor therefore requires more complex operations at the receiver to process the signal. Despite the increased difficulty, a camera offers several advantages over a photodiode, such as a larger field of view, a spatial separation of light providing a clear distinction between different light sources, or a wavelength separation of the incident light into RGB component through the Bayer filter. With all the benefits brought by OCC, a wide range of applications have been identified in a wide variety of areas, such as indoor localization [15], Vehicle-to-Vehicle (V2V) communication [16], or IoT applications [17]. All the different steps required in the processing of the OCC images based on rolling shutter, from the image capture to the original information recovering, are illustrated in Figure 1.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 2 Proposed Chaos-Based Cryptosystem
### Circular Substitution Process Based on the S-Box and Their Inverse Processes
#### 2.2.3 Inverse Substitution Process  
The reverse substitution operation is carried out by first, the construction of the inverse S-box, \(IS\), which is derived from the S-box \(S(t)\) by a simple operation satisfying the following equation:  
\[IS[S(t)]=t \tag{17}\]  
used during the inverse substitution process, then, performing the inverse substitution itself using the following formula to recover each plain pixel \(p_{l}(k)\):  
\[\left\{\begin{array}{l}p_{l}(k)=(IS[s_{l}(k)]+256-h)\ mod\ 256\\ \\ h=s_{l}(k)\oplus m_{h}\end{array}\right. \tag{18}\]
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 5 Results and Analysis
### Comparative Performance Analysis  
The localization accuracy of the proposed RNN-based localization approach is compared with traditional localization approaches presented in related research studies, and Table 3 summarizes the localization performance of each of the systems. A minimum mean localization error of 0.39 m was achieved for a small-scale urban area in our previously published work [45]. [PERSON] et al. [49] used Multilateration algorithm and achieved a minimum localization error of 6.2 m while [PERSON] et al. [50] obtained a minimum localization error of 7.57 m. [PERSON] et al. [51] used deep learning and obtained a minimum localization error of 18.8 m, and [PERSON] et al. [52] used a linear regression model and achieved a localization error of 45.75 m. [PERSON] et al. [53] used deep neural networks and obtained a localization error of 191.52 m; [PERSON] et al. [54] also used kNN and obtained a localization error of 340 m. [PERSON] et al. [46] used kNN method and achieved a localization error of 398.4 m; [PERSON] et al. [8] also used ANN and obtained an error of 358 m, while [PERSON] [55] used ANN approach and achieved a localization error of 500 m. From Table 3, it is clear that the proposed RNN-based localization model outperforms the other RSSI fingerprint LoRaWAN-based localization systems from related research with a minimum localization error of 0.29 m. The proposed RNN method has achieved 25%, 95%, 96%, 98.5%, 99.85%, 99.37%, 99.92%, 99.93%, and 99.94% improvements in mean localization error compared with our previous work [45], [PERSON] et al. [49], [PERSON] et al. [50], [PERSON] et al. [51], [PERSON] et al. [52], [PERSON] et al. [53], [PERSON] et al. [54], [PERSON] et al. [46], [PERSON] et al. [8], and [PERSON] [55], respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Samples** & **Mean Localization Error (m)** \\ \hline
1000 & 0.356 \\
3000 & 0.308 \\
5000 & 0.312 \\
10,000 & 0.314 \\
15,000 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average mean localization error (m) for used samples.  
Figure 8: Minimum mean localization error values for the used samples.
## AOG Solutions  
Research by [11; 12] provides actionable insights for operators in reducing AOG incidents. They showed that predictive maintenance could boost maintenance efficiency by 20% to 30%, a critical advantage for the research base, which relies on high fleet utilization for its ACMI and charter services. By applying predictive models to monitor key components like engines and landing gear, SmartLynx can address potential failures proactively, keeping aircraft operational and minimizing disruptions. Moreover, they highlighted the financial benefits of predictive maintenance, particularly in optimizing spare parts inventory and reducing costly downtime. For SmartLynx, forecasting parts demand in advance can streamline its supply chain, eliminate delays in sourcing critical components, and reduce the financial impact of AOG events.  
These findings align with Airline's ongoing efforts to use data analytics and machine learning. Predictive algorithms analyzing real-time data from the airline's A320 and A321 fleets can provide early warnings of potential issues, ensuring higher fleet availability and smoother operations.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 5 Performance Analysis
### Searching Probability of Next-Hop Nodes
When the distance between \(C_{i}^{(j)}\) (with data rate \(x\)) and \(C_{i}^{(j+2)}\) (with data rate \(y\)) is \(l\in[\overline{d},2\overline{d})\) km, the searching probability for \(C_{i}^{(j+1)}\) in OT-OR-OR mode can be calculated as  
\[P_{2 hop\_1}(d_{x},d_{y},l)=\left\{ \begin{array}{ll}(1-\beta)\frac{V_{0}(d_{x }d_{y}l)}{V_{omni}(d_{x})},&Condition\,\,I\\ (1-\beta)\frac{V_{0}(d_{x}d_{y}l)}{V_{omni}(d_{x})}+\beta\frac{V_{0}(d^{\prime }_{x}d_{y}l)}{V_{omni}(d^{\prime}_{x})},&Condition\,\,II\end{array} \right. \tag{9}\]  
where \(d^{\prime}_{x}\) and \(d^{\prime}_{y}\) denote the transmission ranges of a pair with the priority (i.e., SP or PP) higher than that of \(C_{i}^{(j)}\) and \(C_{i}^{(j+2)}\), _Condition I_ denotes that pair \((C_{i}^{(j)},C_{i}^{(j+2)})\) has the highest priority, or that contestant pairs with higher priority do not exist. _Condition II_ means that the contestant pairs are of higher priority than pair \((C_{i}^{(j)},C_{i}^{(j+2)})\). Figure 7: Searching probability region in Scenario 3. In addition, the searching probability for \(C_{i}^{(j+1)}\) in DT-DR-OR mode is affected by the beam direction due to the adoption of directional antenna. Let the influence of beam direction on searching probability be \(\xi\), the searching probability for \(C_{i}^{(j+1)}\) can be deduced as  
\[P_{2 hop\_2}(d_{x},d_{y},l)=\left\{ \begin{array}{ll}(1-\xi)(1-\beta)\frac{V_{0 }(d_{x},d_{y},l)}{V_{dir}(d_{x})},&\text{Condition }I\\ (1-\xi)\bigg{[}(1-\beta)\frac{V_{0}(d_{x},d_{y},l)}{V_{dir}(d_{x},\beta_{\max} )}+\beta\frac{V_{0}(d_{x^{\prime}},d_{y^{\prime}},l)}{V_{dir}(d_{x^{\prime}}, \beta_{\max})}\bigg{]},&\text{Condition }II\end{array} \right. \tag{10}\]  
where \(l\in[2\overline{d},4\overline{d})\). In Scenarios 1-3, among \(n\) neighbor nodes, there exists at least one such node, with the probability as follows:  
\[P^{\prime}(d_{x},d_{y},l)=1-\sum_{(x^{\prime},y^{\prime})>(x,y)}P^{\prime}(d_ {x^{\prime}},d_{y^{\prime}},l)-\left(1-\sum_{(x^{\prime},y^{\prime})\geq(x,y) }P(d_{x^{\prime}},d_{y^{\prime}},l)\right)^{n} \tag{11}\]  
The searching probability of next-hop nodes in single-hop and 2-hop transmissions has been deduced as described above; thus, the probability \(P^{\prime}(d_{x},d_{y},l)\), which is closely related to the data rate (\(x,y\in\{1,2,5.5,11\}\)) and other influences (\(\xi\) and \(\beta\)), will play a critical role in the analysis of saturated throughput.
# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 1 Introduction  
Face alignment, also called 'feature point location', is a process in which a supervised learned model is applied to an input face image to automatically estimate the locations of a set of face landmarks distributed in the eyes, eyebrows, mouth, nose and so on. Face alignment is an important stage in the pipeline of many face analysis applications, such as human computer interactions, expression recognition and identity authentication. Today, it is still a challenging task to automatically and accurately align faces because intrinsic and extrinsic variations exist. The former implies the diversities of the inherent attributes among individuals, such as the shape and texture of eyes, mouth and face. And the latter implies the variations of the image acquisition conditions, such as hair style, glasses, pose, expression and illumination.  
Face alignment has a history of 30 years, starting with the early conventional Point Distribution Models, moving to the later Shape Regression methods and to deep learning methods in recent years. Appearance representation is the key to face alignment. The ideal features should reflect only the intrinsic variation, but not be sensitive to the extrinsic variations. Different features can be extractedfrom a face region or a whole face, which respectively reflect low and high frequency information of a face. Accordingly, the existing face alignment methods can be classified into the two categories: global and local methods.
## 3 Experimental Section
### Analysis on Ti-Si Thin-Film Specimens with the Acousto-Optical Method
#### 3.2.2 Observation of Resonance-Like Behavior
This is understandable if we consider the nature of the signal detected by the digital camera with the help of Equation (5). The Bessel function \(J_{0}\) appearing in the second term of Equation (5) is steeper as the Fourier spectrum peak gets lower (Figure 3 (3)). This means that the greater the oscillation amplitude caused by the function generator, the more sensitive the spectrum peak becomes to the fluctuation of the voltage input to the transducer. The output voltage from the function generator used in the present study fluctuated approximately \(\pm 0.5\%\) over the period of data taking for each driving frequency. To reduce those errors, we repeated the same measurement a number of times. The plots shown in Figure 7 are averages of over five measurements. The fluctuations in the peak ratio among these measurements were approximately \(\pm 5\%\) near a valley frequency and \(\pm 2\%\) in the frequency range away from valleys, respectively. From the slopes near valleys in Figure 7, the \(\pm 5\%\) error in the peak ratio is translated into \(<\pm 1\%\) error in the valley frequency. Thus, the differences in the valley frequencies listed in Table 3 are significant. In the case of the Ti-Au film, both the untreated and treated specimens show a significant reduction in the peak ratio. It is likely that this is because of poor adhesion between the Au and Ti films, rather than the poor adhesion between Ti and silicon substrate. Poor adhesion between Au and Ti layers was observed elsewhere in a film system similar to the present case [23]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & \multicolumn{2}{c}{**Valley 1**} & \multicolumn{2}{c}{**Valley 2**} \\ \cline{2-5}  & **Treated** & **Untreated** & **Treated** & **Untreated** \\ \hline Ti-only & 13.0 & 14.5 & 22.5 & 27.0 \\ Ti-Pt & 5.2 & 5.8 & 9.0 & 10.8 \\ Ti-Au & 5.4 & 6.0 & 9.4 & 11.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Valley frequencies observed in Ti-only specimens and expected valley frequencies in Ti-Pt and Ti-Au specimens based on the mass ratio defined in Table 2 (unit: kHz).
## 5 Conclusions  
In this paper, the effects of using RPWM to control the switching activity of power electronics converters in power systems involving PLC for advanced meter reading are investigated. To this end, both the power and the communication part of the system were modeled in SIMULINK. The comparison versus traditional PWM is carried out in terms of bit error rate achievable by application of the two modulation schemes. Additionally, the effects of connecting the PLC modems to the power line in different configurations is presented. Based on the obtained results, it is possible to conclude that applying RPWM on the power converter in the PLC system, besides having a significant effect on reducing the CEs, results in less bit error rate compared to the conventional PWM when the modulation frequency of the communication signal and the frequency of the significant peaks of the noise exiting the DC-DC converter are overlapping. Conversely, higher bit error rate will be obtained if the two frequency spectra do not overlap. Results also showed that, to improve the bit error rate, it is preferable to connect the PLC coupling/decoupling networks between two phases of the power line, rather than between a phase and ground, since this assures higher symmetry of the PLC channel. The obtained results will be confirmed by laboratory experiments in the future. Another improvement regards the modeling of the power line by a distributed-parameter circuit model (based on transmission line theory), which will allow accounting for the effects due to propagation on the communication signal.  
Conceptualization, A.H.B., F.G., and P.S.C.; methodology, A.H.B., L.W., F.G., P.S.C., and W.E.S.; software, A.H.B., W.E.S., F.G., and P.S.C.; validation, A.H.B., L.W., F.G., P.S.C., X.L., X.W., and W.E.S.; formal analysis, A.H.B., L.W., F.G., P.S.C., X.L., X.W., and W.E.S.; investigation, A.H.B., L.W., F.G., P.S.C., X.L., X.W., W.E.S., G.S., and S.A.P.; resources, F.G. and S.A.P.; data curation, A.H.B. and L.W.; writing--original draft preparation, A.H.B. and F.G.; writing--review and editing, A.H.B., L.W., F.G., P.S.C., X.L., X.W., W.E.S., G.S., and S.A.P.; visualization, F.G., W.E.S., and P.S.C.; supervision, F.G., P.S.C., G.S., and S.A.P.; project administration, F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 812753.  
The authors declare no conflict of interest.
# The Effect of Task Complexity on Time Estimation in the Virtual Reality Environment: An EEG Study
## 1 Introduction
### NASA-Task Load Index  
The NASA-Task Load Index (NASA-TLX), a widely applied workload assessment tool [47], has proven its usefulness in measuring task complexity from a subjective perspective [48; 49], but it has some disadvantages. NASA-TLX is normally applied after an individual finishes a task, meaning that the result depends on the individual's memory and willingness to answer the questions without any bias [50]. Given the limitations of NASA-TLX, researchers have considered estimating perceived workload using responses such as time estimation [50; 51] and EEG signals [37; 45]. To further verify in the VRE that time estimation and EEG signals can work as workload assessment tools, our third research goal was to use correlation analysis to discover the relationship between time estimation error, EEG band power, and the NASA-TLX score.  
**RQ3:**_Are there relationships among time estimation, brain signals, and perceived workload in the VRE?_  
To answer the three research questions, we designed controlled experiments with independent and dependent variables and analyzed collected experimental data, which are described in Section 2. Section 3 presents the descriptive statistics of dependent variables and the results from the analysis of variance (ANOVA) that show the effects of the independent variables on the dependent variables (RQs 1 and 2). The results from the correlation analysis are also provided to show the relationship between various dependent variables (RQ3).
## 4 Case Study
### Results
#### 4.2.3 Formulation of Value Structure  
Based on the information conveyed by nodes and edges, several formulae can be drawn up. It is assumed that node strength is proportional to the number of repetitions. First, the formation of the overall UX was assumed to consist of three core value dimensions according to the research framework in this study. Second, the formation of each core value dimension was filtered by node strength. When applying the min-max normalization to node strengths, dimensions of more than 0.3 were selected.  
As a result, several nodes were considered to affect core value dimensions such as convenience, fun, and fatigue. Nodes such as practiced, learnability, doubtful, tension, desire to use, and gesture shape were regarded to affect convenience; nodes such as tension, easy, gesture shape, and new were assumed to affect fun; nodes such as limited, range of motion, stressful, desire to use, learnability, exactness, subtle, type of control, seamless, error, and independent were considered to affect fatigue.  
In order to refine the value structure, a post hoc analysis with an experiment was carried out. Considering the case study had a limitation to probe into gestural interactions with only qualitative descriptions, a post hoc analysis aimed to assess quantitative measurements which would be used to verify the result of the case study.  
Experimental tasks were repeated with the same experimental device without an in-depth interview, but each participant was required to evaluate gesture interactions psychophysically. A total of 21 influential dimensions (Table 5) were rated with the 0-10 scale, utilizing the psychophysical measurements used in previous studies [34; 35; 36]. Note that design variables such as type of control were also included the questionnaire items for building models.  
The simple linear regression method was applied to build value structure models, based on a total of twenty participants' evaluation data. The variables consisted of the core linkage concept in the network analysis output. When applying the method, a dimension whose variation inflation factor (VIF) was higher than 10 was removed from the value structure models to minimize multi-collinearity [37]. As a result, totally four models were built, whose R2 and the maximum VIF were 0.70 and 4.53 on the average (Table 6).  
Figure 6: Partial semantic network of fatigue (red and blue nodes indicate core values and design variables, respectively).