esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Results of the Validation Techniques
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Matrix**} & \multicolumn{6}{c}{**Testing Data Set**} & \multicolumn{6}{c}{**Rank**} & \multirow{2}{*}{**Rank Total**} & \multirow{2}{*}{**CF**} & \multirow{2}{*}{**Priority Rank**} \\ \cline{2-2} \cline{5-13}  & **Precision** & **Efficiency** & **AUC** & **MAE** & **RMSE** & **Precision** & **Efficiency** & **AUC** & **MAE** & **RMSE** & **RMSE** \\ \hline ANN & 0.817 & 0.838 & 86.45 & 0.027 & 0.164 & 7 & 6 & 13 & 1 & 7 & 34 & 6.8 & 5 \\ SVM & 0.785 & 0.792 & 85.63 & 0.067 & 0.125 & 9 & 10 & 14 & 2 & 2 & 37 & 7.4 & 7 \\ RF & 0.667 & 0.708 & 86.95 & 0.085 & 0.146 & 14 & 14 & 4 & 47 & 9.4 & 10 \\ LR & 0.825 & 0.829 & 84.7 & 0.131 & 0.18 & 5 & 8 & 15 & 9 & 10 & 47 & 9.4 & 11 \\ ANN-SVM & 0.715 & 0.723 & 87.45 & 0.302 & 0.387 & 12 & 12 & 9 & 15 & 15 & 63 & 12.6 & 15 \\ ANN-LR & 0.705 & 0.709 & 86.69 & 0.139 & 0.186 & 13 & 13 & 12 & 10 & 11 & 59 & 11.8 & 14 \\ LR-SVM & 0.667 & 0.708 & 87.62 & 0.228 & 0.0287 & 15 & 15 & 8 & 14 & 1 & 53 & 10.6 & 13 \\ LR-RF & 0.748 & 0.77 & 86.98 & 0.102 & 0.159 & 11 & 11 & 10 & 6 & 6 & 44 & 8.8 & 9 \\ SVM-RF & 0.859 & 0.881 & 88.69 & 0.189 & 0.217 & 3 & 5 & 7 & 13 & 12 & 40 & 8 & 8 \\ ANN-RF & 0.821 & 0.885 & 89.91 & 0.109 & 0.164 & 6 & 4 & 5 & 7 & 8 & 30 & 6 & 4 \\ ANN-LR-SVM & 0.785 & 0.812 & 89.77 & 0.163 & 0.275 & 10 & 9 & 6 & 11 & 14 & 50 & 10 & 12 \\ ANN-RF-SVM & 0.857 & 0.889 & 92.03 & 0.172 & 0.241 & 4 & 3 & 4 & 12 & 13 & 36 & 7.2 & 6 \\ RF-SVM-LR & 0.815 & 0.835 & 92.29 & 0.089 & 0.149 & 8 & 7 & 3 & 5 & 5 & 28 & 5.6 & 3 \\ ANN-LR-RF & 0.878 & 0.893 & 93.98 & 0.117 & 0.138 & 1 & 1 & 1 & 8 & 3 & 14 & 2.8 & 1 \\ ANN-RF-SVM-LR & 0.873 & 0.889 & 92.63 & 0.0761 & 0.172 & 2 & 2 & 2 & 3 & 9 & 18 & 3.6 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of validation techniques and accuracy prioritization based on testing datasets.
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 1 Introduction  
With the growth of the social economy, the tourism industry is flourishing, and the enthusiasm of tourists worldwide continues to rise, leading to increasingly diverse tourism demands. In large scenic areas, the dispersed layout of attractions and the long travel distances between them often pose troubles for visitors [1]. To address these problems and improve the tourism experience, transportation services such as sightseeing and shuttle vehicles have been introduced [2]. A tourist shuttle bus is a vehicle that operates along a fixed route to transport passengers between two or more attractions, addressing the issue of limited accessibility within tourist areas [3]. However, due to the pronounced peak patterns in tourism, factors such as holidays or weather can lead to significant fluctuations in tourist numbers, placing significant pressure on these attractions' human and material resources. As a result, a critical issue that needs addressing is how to transport more passengers efficiently at lower costs.  
In most current public transportation systems, vehicles with fixed capacities are commonly used [4, 5, 6]. However, this approach is less effective in scenarios with substantialdemand fluctuations, where mismatches between vehicle capacity and travel demand can result in extended passenger wait times or low vehicle utilization rates, ultimately raising operating costs. Modular vehicles, an emerging transportation technology, offer a solution that can flexibly adjust capacity to better align with varying demands [7]. These vehicles can operate independently or in combination with multiple units, making this transportation mode highly valuable for enhancing efficiency, reducing operating costs, and offering more sustainable vehicle scheduling solutions [8]. Therefore, the use of modular vehicles can effectively contribute to the reduction in fleet operating costs.  
This paper investigates the modular vehicle scheduling problem for passenger transport in tourist attractions to minimize operating costs, focusing on the design of a modular vehicle scheduling strategy through a data-driven model to achieve a smaller operating cost by exploiting the advantages of modular vehicles in the case of large fluctuations in passenger demand and comparing it with the theoretical optimal results to validate the effectiveness of the model.  
The rest of this paper is organized as follows: Section 2 provides a review of the relevant literature. Section 3 describes the parameters and algorithms of the traditional instance response model, the data-driven model, and the theoretical optimal model. Section 4 provides a sample calculation comparing the optimization effect of the data-driven model and a sensitivity and validity analysis of the results. Section 5 presents the conclusions and limitations of this study, as well as an outlook for future research.
## 2 System Model and Problem Formulation
### SINR Assignment for One D2D Multicast Group
: _The optimal \(\lambda\) satisfying \(\sum\limits_{k\in S_{m}}\log_{2}(1+\gamma_{n,m,k,\max}^{D})=\log_{2}(1+ \Gamma_{m}^{D})\) is within \(\left[\lambda_{\min},\lambda_{\max}\right]\) where \(\lambda_{\max}\triangleq\max_{S_{m}}\left\{D_{k}\cdot\frac{1+\gamma_{n,m,k, \max}^{D}}{\left(H_{k,B}^{CB}H_{m,n}^{DD}-\widetilde{H}_{m,B}^{DB}\widetilde{H}_ {k,m,n}^{CD}\Gamma_{k}^{\zeta}\gamma_{n,m,k,\max}^{D}\right)^{2}}\right\}\) and \(\lambda_{\min}\triangleq\min_{S_{m}}\left\{\frac{D_{k}}{\left(H_{k,B}^{CB}H_{m,n }^{DD}\right)^{2}}\right\}\)._  
**Proof. ** The proof is presented in Appendix B.
## 2 Related Studies  
### Epidemic Routing and Spray and Wait  
_Epidemic Routing_[4] is an infectious routing in which a transmission terminal floods bundles to all the relay nodes to which it is connected. This increases the probability with which a bundle reaches its destination terminal, resulting in a high delivery rate. A downside is that flooding of bundles can lead to network congestion. In addition, every time a terminal is connected to a relay node and transmits data, it consumes its battery charge, which is precious in a disaster situation. To sum up, _Epidemic Routing_ tends to cause network congestion due to flooding of a large number of bundles, and consumes considerable battery charge due to frequent transmission of data.  
_Spray and Wait_[5] sets an upper bound on the number of copies that can be generated. It can avoid flooding-induced network congestion, and enables terminals to save more battery charge than _Epidemic Routing_. A downside is that the delivery rate is lower because suppression of flooding reduces the probability at which the relay nodes that hold copies reach the destination terminal. Besides, in the aftermath of a disaster, when the extent of the damaged area or the number of victims is likely to be unknown, it is difficult to determine the appropriate upper bound on the number of copies instantaneously.  
The characteristics of _Epidemic Routing_ and _Spray and Wait_ are summarized in Table 1.
## 3 Results
### IOD of Arcs from GEO Objects  
#### 3.2.1 Changchun GEO EA  
In this paper, the test is carried out using observation data from 6-10 February 2021. The arc length distribution of the observation data is shown in Figure 3. The arcs are matched with the TLE to identify the cataloged objects. The TLE data were downloaded from www.space-track.org, (accessed on 7 February 2023). The matching results are shown in Table 4, with approximately 74.3% of the arc segments being known objects. Based on the data and the results of the TLE matching, the number of observed arcs for each object can be analyzed, and the statistical results are shown in Figure 3, where it can be found that the number of observed arcs for most objects is no more than 100.  
Some of the known observation arcs were selected for IOD, and the relationship between the IOD success rate and arc length was initially analyzed. The arc length of GEO object data was longer than 30 s, and the average arc length was about 77.4 s, with a total number of 1602 successful IODs and a success rate of 87.84%. For the observations in which the TLE was matched successfully, the TLE was used to evaluate the initial orbit parameter errors, using the SMA and inclination as examples, and the results are shown in Tables 5 and 6. According to Table 5, it can be found that 40% of the total SMA errors are less than 30 km, and about 71% of them are less than 100 km. According to Table 6, it can be found that 82% of the inclination errors are less than 0.1 (the total number of IODs is 539).  
When the time interval is less than 36 h, the correlation rate of arcs of the same object is about 89%. During the data processing, some problems were found. Further preprocessing was needed, such as the duplication of data between observation files or failure to correlate consecutively observed arc segments of the same object, splitting into two or more files. The statistics revealed that many of the observation arcs for a given object had small intervals between them or even overlapped.
Entropy_**2014**, _16_, 6133-6151; doi:10.3390/e16116133  
_Artcle_  
**Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses**  
**in Aquaculture**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 1,3,4**  
1 Research Center for Experimental Marine Biology and Biotechnology--Plentziako Itsas Estazioa (PIE), University of the Basque Country UPV/EHU, Plentzia 48620, Spain; E-Mail: [EMAIL_ADDRESS]  
2 Department of Systems Engineering and Automatics, University of the Basque Country UPV/EHU, Donostia 20018 Spain; E-Mail: [EMAIL_ADDRESS]  
3 IKERBASQUE Basque Foundation for Science, Bilbao 48013, Spain  
4 Norwegian College of Fishery Science, Faculty of Biosciences, Fisheries and Economics, University of Tromso, Tromso N-9037, Norway  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-946-018-448.  
External Editor: [PERSON] and [PERSON]  
_Received: 6 October 2014; in revised form: 13 November 2014 / Accepted: 17 November 2014 / Published: 19 November 2014_  
**Abstract:** The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (_Dicentrarchus labrax_), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.  
keywords:entropy; fractal dimension; nonlinear analysis; image analysis; clustering; optical flow; pattern recognition; seafood safety; fish welfare; intelligent methods; environmental monitoring; aquaculture
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**No Raw Salt Consumption**} & \multicolumn{2}{c}{**Max. 1-2 Serre per Week**} \\ \hline  & & **No at Risk** & **n (C)** & **AOR (95\% CD \({}^{*}\)** & **n (C)** & **AOR 69\% CD \({}^{*}\)** \\ \hline \multirow{4}{*}{Sex} & Total & 307 & 66 (21.5) & & 46 (15.0) & \\  & Male (Ref.) & 153 & 35 (22.9) & & 31 (20.3) & \\  & Female & 154 & 31 (20.1) & 0.81 (0.43, 1.54) & 15 (9.7) & 0.51 (0.24, 1.09) \\ \hline \multirow{4}{*}{Age (years)} & 30-40 (Ref.) & 46 & 11 (23.9) & & 12 (26.1) & \\  & 40-59 & 160 & 31 (19.4) & 1.05 (0.46, 2.44) & 23 (14.4) & 0.73 (0.30, 1.81) \\  & \(\geq\)0-years & 101 & 24 (23.8) & 1.48 (0.59, 3.72) & 11 (0.99) & 0.42 (0.15, 1.20) \\ \hline \multirow{4}{*}{Level of education} & No acdination (Ref.) & 99 & 14 (14.1) & & 9 (9.1) & \\  & Primary to high school & 148 & 31 (20.9) & 1.86 (0.90, 3.84) & 17 (11.5) & 1.19 (0.49, 2.91) \\  & SSC or above & 59 & 21 (35.6) & 4.27 (1.69, 10.8) & 20 (33.9) & 4.41 (1.55, 12.5) \\ \hline \multirow{4}{*}{SSS \({}^{*}\)} & Poor (Ref.) & 92 & 18 (99.6) & & 13 (41.1) & \\  & Middle class & 214 & 48 (22.4) & 0.94 (0.49, 1.77) & 33 (15.4) & 0.71 (0.33, 1.54) \\ \hline \multirow{4}{*}{Occupation \({}^{*}\)} & Farmer (Ref.) & 66 & 12 (18.2) & & 11 (16.7) & \\  & Houswife & 146 & 30 (20.5) & 1.71 (0.15, 19.1) & 15 (10.3) & 0.89 (0.26, 2.54) \\ \cline{1-1}  & Employed & 53 & 14 (26.4) & 1.21 (0.44, 3.30) & 9 (17.0) & 1.01 (0.30, 3.68) \\ \cline{1-1}  & Business & 24 & 5 (20.8) & 0.96 (0.28, 3.34) & 6 (25.0) & 1.06 (0.31, 3.72) \\ \hline \multirow{4}{*}{Diabetes status \({}^{*}\)} & No diabetes (Ref.) & 217 & 47 (21.7) & & 34 (15.7) & \\ \cline{1-1}  & Diabetes & 41 & 11 (26.6) & 1.07 (0.47, 2.43) & 4 (0.8) & 0.41 (0.12, 1.33) \\ \hline \end{tabular}  
* \({}^{*}\) Adjusted Odds ratio (AOR) (095% confidence interval (CII))- adjusted for the covariates in the model; \({}^{*}\) Indicates some missing cases; for example, information for one participant SISs was missing.  
\end{table} 
Table 4: Associations of sociodemographic, occupation, and diabetes status with no raw salt consumption and fruit intake maximum 1–2 serves in a week using logistic regression from a cluster RCT in a rural area in Bangladesh. The association of salt consumption with blood pressure was not significant. The mean (95% confidence interval) of SBP was 149.0 (144.8, 153.2) and DBP 89.3 (87.3, 91.4) for people who did not consume raw salt, compared to 148.8 (146.4, 151.2) and 90.0 (88.7, 91.3) for people who consumed raw salt. Fruit consumption was also not associated with SBP or DBP (Table 5).
# Effects of Kerr Nonlinearity in Physical Unlonable Functions
## 2 Methods
This equation treats each spectral component independently, while propagation takes place on a discretized space, with the initial condition \(\Psi(\mathbf{\rho},z_{2};\lambda_{i})=\{\Psi(\mathbf{\rho}_{m},z_{2};\lambda_{i}):1\leq m \leq\mathscr{M}\}\), where \(\Psi(\mathbf{\rho}_{m},z_{2};\lambda_{i})\) is given by Equation (4b). Note also that different spectral components experience different nonlinearities, and thus different dynamics, due to the intensity \(J(\
u_{i})\). Throughout this work, we are interested in short propagation lengths \(z_{3}=L\lesssim 1\) mm, so that the token is sufficiently thin for cryptographic applications (e.g., engineering of all-optical smart cards) [1; 9]. As a result, in Equation (5), we neglect effects of dispersion, self-steepening and space-time coupling, assuming pulses with duration such that \(2\pi cT\gg\lambda_{0}\)[24]. For observation times that are long compared to the coherence time of the scattered light (\(\sim\)\(1/\delta\
u_{\mathrm{c}}\)), the intensity of the scattered light at the \(m\)th output mode after the nonlinear medium (plane at \(z=z_{3}\)) can be approximated by [17]  
\[E(\
u)=\sqrt{2\pi l_{0}}\tau\exp\left[-2\pi^{2}\tau^{2}(\
u-\
u_{0})^{2} \right]. \tag{2}\]0  
Without loss of generality, we consider odd values for \(\mathscr{N}\), with the index \(i\in[-(\mathscr{N}-1)/2,(\mathscr{N}+1)/2]\), and \(i=0\) refers to the central wavelength \(\lambda_{0}=c/\
u_{0}\). We also neglect effects of free-space propagation, as they are expected to contribute equally in the speckles of the actual token and a clone. Using the above sequential model for the nonlinear multiple-scattering token, we have performed a number of simulations in order to gain insight into the role of the nonlinearity on the speckle that is produced by such a medium as well as on the sensitivity of the speckle on the cloning of the token. Equation (5) was solved by means of the split-step Fourier method [25]. The reference token is described by a multi-spectral transmission matrix \(\mathbf{T}_{\text{ref}}:=\{T_{m,j}^{(\lambda_{i})}:1\leq m,j\leq\mathscr{M};-( \mathscr{N}-1)/2\leq i\leq(\mathscr{N}+1)/2\}\). The matrix involves \(|\mathbf{T}_{\text{ref}}|=\mathscr{M}\times\mathscr{M}\times\mathscr{N}\) independent complex Gaussian random variables, with zero mean and variance \(l/L\mathscr{M}\) (e.g., see [17; 19; 20; 21; 22] and references therein). The wavelength for the \(i\)th spectral degree of freedom is given by \(\lambda_{i}=\lambda_{0}+i\delta\lambda_{\mathrm{c}}\). For a fixed multi-spectral transmission matrix, we generate random clones along the lines described in Refs. [6]. More precisely, the multi-spectral transmission matrix of a \(Q\)-close clone is obtained from \(\mathbf{T}_{\text{ref}}\), by choosing new random values for \(\lceil Q|\mathbf{T}_{\text{ref}}|\rceil\) randomly chosen elements. The parameter \(Q\) is essentially associated with the quality of the cloning, with \(Q=0\) and \(1\), corresponding to a perfect and fully randomized clone, respectively.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
###### Abstract  
This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 \(\times\) 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.  
Keywords:neural network; watermark; deep learning; property verification; copyright protection; video; HEVC; H.265; YUV420; YUV420p +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Results
### Number Concentration  
The aerosol number concentration \(\mathrm{N_{p}(r)}\) can be retrieved from aerosol backscatter signal using ancillary information provided by the optical particle counter (Fidas, 200) and reported in Section 3.2. Following the same structure of the previous subsection, the backscatter profiles \(\beta_{\mathrm{aer}}(r)\) yield an estimation of the range-dependant aerosol number concentration \(\mathrm{N_{p}(r)}\).  
For a non-range dependant aerosol size distribution, let us write Equation (6) as:  
\[\beta_{\mathrm{aer}}(r)=\mathrm{N_{p}(r)}\langle\mathrm{d}C^{\mathrm{bac}} \rangle\quad[m^{-1}sr^{-1}] \tag{23}\]  
where \(\langle\mathrm{d}C^{\mathrm{bac}}\rangle\) is the average differential backscattering cross-section calculated for the normalized fog-oil plume log-normal distribution. Thus, the number concentration can be estimated from he retrieved aerosol backscatter profiles as:  
\[\mathrm{N_{p}(r)}=\frac{\beta_{\mathrm{aer}}(r)}{\langle\mathrm{d}C^{\mathrm{ bac}}\rangle}\quad[\mathrm{part\;cm^{-3}}] \tag{24}\]  
Figure 6 shows the retrieved aerosol number concentration profiles of the fog-oil event in false color. From the measurements, a high number concentration of particles was detected in the first 30 s of the measurement, reaching values up to 5.0 10\({}^{5}\) part cm\({}^{-3}\), and then evidencing the rapid decrease of the number of aerosol concentration over time, reaching values lower than 10\({}^{5}\) part cm\({}^{-3}\) in less than 30 s. The time-averaged aerosol number concentration \(\langle N_{p}\rangle\) measured during the first 60 s of the event is represented in the right panel of Figure 6.  
Figure 5: Spatio-temporal (left panel) and 60 s time-averaged (right panel) aerosol backscatter profiles of fog-oil plumes measured by the Colibri micro-lidar with high range and temporal resolution. The time-averaged molecular and aerosol contributions are, respectively, in red and blue color.
## 7 Simulations and Results
### Scenario 3: HEMS with ESS and PV  
In this scenario, the electricity consumer had their own ESS and a PV generation system integrated with it. The consumers fulfilled their load demand by getting energy from the PV generation, ESS, and utility. In each time interval, when the output energy of the PV plus ESS discharge energy was less than to the consumer load demand, the consumer imported energy from the utility, and whenever the output energy of PV plus ESS discharge energy was greater than the consumer load demand, then  
Figure 16: AWT with ESS.  
Figure 15: PAR with an ESS.  
the consumer exported the extra energy to the utility at 50% of the purchasing electricity cost for both the RTP and CPP tariff schemes. In time slots 7, 8, and 11 to 18, the consumers had extra energy. Therefore, they exported their extra energy to the grid. Due to this, the total cost of the electricity consumption reduced, and the consumer got the benefits of net metering. The hourly load of this scenario is demonstrated in Figure 17.