esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Method
### Feature Extraction
#### 3.2.1 Discrete Wavelet Transform  
The Bi-orthogonal Discrete Wavelet Transform (BDWT) was applied to individual images for receiving salient features in terms of wavelet coefficients. The two-dimensional coefficients, including approximation, horizontal, vertical, and diagonal details, were transformed into a feature-vector, as shown in Figure 4. This feature-vector was marked as F1. In the proposed work, Bi-orthogonal-1.3 wavelet (Bior-1.3) was used up to second-level decomposition of character image. The kernel size of the Bior-1.3 wavelet is summarized in Table 2.  
Figure 4: The frame format of features received from DWT.  
Figure 3: Design of the proposed work.  
Referring to Table 2, the level 1 decomposition produced the feature vector size of 1296 (i.e., 4\(\times\)(18 \(\times\) 18)), including all the four types of coefficients (approximation, horizontal, vertical, and diagonal), while the feature-vector size of 484 (i.e., 4\(\times\)(11 \(\times\) 11)) for level-2 decomposition offered a promising reduction in feature dimensionality. This made the level 2 decomposition a logical choice for the proposed work.
## 4 Selected Use Cases and Opportunities
### Healthcare  
UAV-assisted THz-VLC systems can be utilized in a healthcare setting to transmit patient medical information to a nearby laboratory, as depicted in Figure 3. Patient medical information is transmitted from the sender using a VLC uplink to a DF relay within the hospital. The DF relay decodes the received information and retransmits it to a UAV using THz communication. At the UAV node, an AF relay architecture is used to enhance the SNR of the transmitted signal by amplifying and forwarding it to another DF relay positioned at the laboratory at a different location. The DF relay at the laboratory decodes the information and retransmits it to the lab technician using a downlink VLC. Utilizing VLC ensures that interference with sensitive equipment in the hospital and laboratory is avoided, while THz communication enables high-speed information transmission.
## 4 Results
### Ella Head Simulation Results  
Figure 6 depicts the reconstruction of the Ella head model simulation. Conductivity images are shown from different head positions under noise-free conditions. All the results display the basic  
Figure 4: Reconstructed conductivity images under different noisy transceive phase data using the conventional method (CM), double regularization method (DRM), and the proposed method (PM).  
Figure 5: The root mean square error (RMSE) of reconstructed conductivity using the conventional method (CM), double regularization method (DRM), and the proposed method (PM).  
structure of the head, which includes WM and GM. Similar to the phantom simulation, the CM conductivity images yield artifacts in the boundary, especially around the CSF tissue. For the DRM, the reconstructed conductivity shows precise results compared with target images. However, the dispersal in the CSF boundary emerges evidently in positions 2 and 3. Compared with DRM, the proposed approach shows a more accurate structure, and the edge is sharper and precise.  
Figure 7 describes the head model results under various noise conditions, which are SNR = 200 and SNR = 100. The results of all methods get worse as the noise increase because the structure of the head is more complicated than the phantom. In addition, the larger Laplacian kernel and the first-order spatial derivative operator dismiss several small structures. The proposed method preserves more details than the results of DRM, such as the WM region in position 2. Figure 8 depicts the RMSE of the reconstructed conductivity images under noise conditions. The RMSE increases as the noise increases and the proposed method shows the lowest values, which display the advantage of the proposed method.  
Figure 6: The reconstructed conductivity images of the Ella head model under noise-free conditions using the conventional method (CM), double regularization method (DRM), and proposed method (PM).
# A Traffic Equilibrium Model for Multi-Modal Networks with Uncertain Demands
## 1 Introduction
On the other hand, in some studies, uncertain demands are assumed to belong to a specific set ([PERSON], [PERSON], and [PERSON] (2011) [16]; [PERSON], [PERSON], and [PERSON] (2011) [17]; [PERSON], [PERSON], and [PERSON] (2021) [18]). [PERSON], [PERSON], and [PERSON] (2011) [17] introduce the strong Wardrop equilibrium in a new model which allowed flexible demand and supply uncertainties. Later, [PERSON], [PERSON], and [PERSON] (2011) [16] proposed a robust Wardrop's user equilibrium assignment by following the expected residual approach, which is a deterministic formulation of the stochastic complementarity problem. Recently, [PERSON], [PERSON], and [PERSON] (2021) [18] proposed a vector network equilibrium model where the demands belong to a closed interval. However, few works focus on the multi-modal network equilibrium problem with uncertain demands. In this paper, an effective travel cost (ETC)-based traffic equilibrium model is presented for multi-modal networks under uncertain demand. By applying supernetwork theory, multi-modal networks are transformed into super networks, in which the probable transfer rules are automatically captured and combined-mode trips and pure-mode trips are presented completely. Travel demands are expressed as a closed interval whose upper and lower bounds are determined using historical data. Passenger flows and travel costs are also formulated as closed intervals to capture the effects of demand uncertainty. Then, the ETC concept is analyzed, which explicitly considers both reliability and unreliability aspects of interval travel cost for different travel modes and the travel route choice decision process. ETC is proposed as a mode and route choice criterion in a traffic equilibrium framework and an equilibrium model is presented to better understand travel mode and route choices under interval travel costs. The model is formulated as a variational inequality problem and solved using an interval-based MSA algorithm. Illustrative examples are also presented to demonstrate the model's characteristics and its differences compared to traditional equilibrium models, in which the expected travel cost is regarded as a choice criterion.
## 3 Results
### Repellency
The differences in the numbers of WFT between treated and control flowers were significant only at 3 and 6 h for stemonine, 6 and 48 h for nicotine, and 6 to 48 h for rotenone. The repellency of nicotine, rotenone, pyrethrins and stemonine diluted to 0.005, 0.01, 0.025, 0.05 and 0.1% were further evaluated in two-choice repellency (concentrations) bioassays. Flowers treated with pyrethrins and rotenone solutions diluted to 0.01% to 0.1% consistently harbored significantly fewer WFT than Tween 20-treated (control) flowers at all observation times, but the differences in the numbers of WFT on treated and control flowers at 0.005% active ingredient were not significant for rotenone and inconsistent for pyrethrins (Table 3). Stemonine consistently failed to repel WFT and resulted in similar abundances of WFT on treated and control flowers at almost all concentrations and observation times. Nicotine diluted to 0.005 and 0.01% was not effective in repelling WFT, whereas rates of 0.05 and 0.1% were less repellent than 0.025%. \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{1}{c}{**Active**} & \multicolumn{4}{c}{**Time after Introduction (hour) 1**} \\ \cline{2-6}
**Ingredient** & **0.5** & **3** & **6** & **24** & **48** \\ \hline Nicotine & 0.5 \(\pm\) 0.2 ab & 0.9 \(\pm\) 0.4 ab & 1.5 \(\pm\) 0.4 b & 1.8 \(\pm\) 0.7 ab & 1.9 \(\pm\) 0.6 b \\ Pyrethrins & 0.1 \(\pm\) 0.1 b & 0.4 \(\pm\) 0.2 b & 0.3 \(\pm\) 0.1 c & 0.1 \(\pm\) 0.1 c & 0.5 \(\pm\) 0.2 c \\ Rotenone & 0.5 \(\pm\) 0.3 ab & 0.6 \(\pm\) 0.2 ab & 0.6 \(\pm\) 0.2 bc & 0.5 \(\pm\) 0.2 bc & 0.4 \(\pm\) 0.2 c \\ Stemonine & 0.3 \(\pm\) 0.1 ab & 0.5 \(\pm\) 0.2 b & 0.3 \(\pm\) 0.2 c & 1.6 \(\pm\) 0.4 ab & 2.5 \(\pm\) 0.5 ab \\ Tween 20 & 1.1 \(\pm\) 0.5 a & 1.8 \(\pm\) 0.5 a & 2.6 \(\pm\) 0.6 a & 2.1 \(\pm\) 0.6 a & 3.7 \(\pm\) 0.6 a \\ \(\chi^{2}\) & 16.3784 & 24.5938 & 55.6203 & 39.7143 & 64.5075 \\ d.f. & 4 & 4 & 4 & 4 & 4 \\ \(p\) & 0.0026 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ \hline \end{tabular}  
* The hypothesis of equal numbers of thrips among the treated and control flowers was tested with chi-square goodness-of-fit tests at \(\alpha=0.05\). Means in a column (specific hour after introduction) followed by the same letters are not significantly different among active ingredients by distribution-free Bonferroni Multiple Comparison \(\alpha=0.05\).  
\end{table} 
Table 2: Mean numbers (\(\pm\)SEM) of adult western flower thrips observed on periwinkle flowers treated with selected plant extracts and Tween 20 (control) at varying times after introduction in multiple-choice repellency bioassays.
## 2 Related Work
### Anomaly Explanation Method  
After anomaly detection, the focus of researchers' concern has been how to interpret the detected anomaly. The multi-scale convolutional recurrent encoder-decoder (MSCED) [29] uses different channel widths to capture short-, medium-, and long-termanomalies. HitRate@P% is used in OmniAnomaly [25] to measure the diagnostic performance of the model. TranAD [28] adds the normalized discount cumulative NDCG@P% on this basis. Bayesian networks are proposed in [30] to perform causal relationship detection for multiple networks and the physical features of a system. Finally, an unsupervised approach is used in [31] to reduce the anomaly feature space to isolate anomalies continuously.  
The anomaly detection methods, as mentioned earlier, have good detection performances. However, their performances across different time series datasets are inconsistent (limited ability to set or identify \"slight\" anomalies using only static thresholds), and their performances in anomaly explanation are inadequate. In addition, anomalies are explained in methods that do not intuitively give the most likely dimensions of an anomaly for researchers to use. To address these issues, the CAT-IADEF model combines CNN adversarial training and the POT dynamic threshold selection, enabling the model to identify \"slight\" anomalies while improving its stability and detection performance. Further anomalies are also carried out in the Anomaly Explanation section.
## 5 Experimental Study and Result Analysis
### Security Analysis  
Note that the final image we obtain after performing RDH through the proposed scheme will be encrypted using a secure image encryption scheme. The RDH scheme does not directly provide any data security for the image or hidden clinical report. However, the use of secure image encryption will protect the confidentiality of the medical image and confidential information from attackers.  
During the simulation of the proposed scheme, we used the AES image encryption technique, which is very popular for the secure transmission of data in all domains. Entropy and histogram analysis are the two efficiency parameters helpful in analyzing the randomness of the final encrypted image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Name** & **PSNR (in dB)** & **SSIM** \\ \hline \multicolumn{3}{c}{Natural images} \\ \hline Airplane & \(\infty\) & 1 \\ \hline Peppers & \(\infty\) & 1 \\ \hline Boat & \(\infty\) & 1 \\ \hline Baboon & \(\infty\) & 1 \\ \hline \multicolumn{3}{c}{Medical images} \\ \hline Medical image-1 & \(\infty\) & 1 \\ \hline Medical image-2 & \(\infty\) & 1 \\ \hline Medical image-3 & \(\infty\) & 1 \\ \hline Medical image-4 & \(\infty\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PSNR and SSIM observed during image recovery.  
Entropy is a measure that analyzes the data to identify randomness [45]. Entropy from an 8-bit grayscale image can be computed in the following way:  
\[E=-\sum_{X=0}^{N-1}P_{X}\text{Log}_{2}P_{X} \tag{13}\]  
where \(E\) indicates the entropy measure of the image given image, and \(X\) is the different grayscale values that can be present in the given image. In an 8-bit grayscale image, \(X\) will be from the set {0, 1,...,255}. \(P_{X}\) indicates the probability of the grayscale value \(X\) in the given image.  
The entropy observed from the selected images before RDH (\(E1\)), the encryption process, and the entropy observed in the encrypted image (\(E2\)) is demonstrated in the bar graph given in Figure 10.  
From Figure 10, it can be seen that the data hiding process does not affect the entropy from the encrypted image. The final entropy measure (\(E2\)) is very close to 8, and this indicates that the pixels in the final encrypted image are uniformly distributed, which led to the entropy measure of 8.  
In addition to this, we also analyzed the histogram of the original image and the final encrypted image to show that the proposed RDH scheme does not affect the encryption efficiency. The histograms obtained from the original images and the encrypted images are given in Figure 11. From the histogram analysis given in Figure 11, it can be seen that the histogram obtained from the final encrypted images looks flat since the pixel values are uniformly distributed. The RDH process does not affect encryption efficiency.
## 3 Results
### Prediction of Physical Activity Through Machine Learning Models
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Hyperparameter** & **Values/Options** \\ \hline DT & maximum depth & \([3,20]\) \\  & split criterion & Gini, entropy \\  & minimum samples per split & \([2,8]\) \\ \hline KNN & number of neighbours & \([1,20]\) \\ \hline LR & penalty & L2 \\  & C & \(C=\{0.01,0.001,0.1,1,10\}\) \\ \hline MLP & hidden layer sizes & \(\{(\text{num\_features},4),(\text{num\_features},3),(\text{num\_features}, )\}\) \\  & activation function & Tanh, RELU \\  & optimizer & SGD, Adam \\  & alpha & \(\{0.0001,0.05\}\) \\  & learning rate & constant, adaptive \\ \hline RF & \# estimators & \(\{10,20,30,40\}\) \\  & maximum depth & \([1,16]\) \\ \hline SVM & kernel & RBF \\  & \(\gamma\) & \(\{0.01,0.001,0.0001,0.00001\}\) \\  & C & \(\{0.01,0.001,0.0001,0.00001\}\) \\ \hline TabPFN & number of ensemble & \(\{16,32\}\) \\  & batch size & \(\{20,30\}\) \\ \hline XGB & \# estimators & \(\{10,20,30,40\}\) \\  & maximum depth & \([1,16]\) \\  & learning rate & \(\{0.1,0.01,0.001\}\) \\  & subsample & \(\{0.5,0.7,1.0\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters explored for the supervised ML-based models DT, KNN, LR, MLP, RF, SVM, TabPFN, and XGB. With the goal of providing interpretability to ML-based models, SHAP was used alongside the models built with the selected features obtained by the FS methods. The SHAP analysis is commonly used for gaining interpretability of the importance value assigned to each feature. This leads to identifying how they contribute to the prediction model. Figure 7 shows the SHAP bar plots associated with the ML models with best predictive results for each device (i.e., SVM for FSLCstrip, and LR for both Eversense and FSL). These plots show the order of relevance of the features for the models' predictions in decreasing order (vertical axis). It is remarkable to see that the total number of adverse events (both hypoglycemic and hyperglycemic) during the day (F_Adverse) has the most important influence on all models' decisions, receiving the maximum importance value in all cases. The rest of the features appear to have very different impacts on every model. Since the Eversense device provided the best predictive results, we used glucose measures from this device and conducted an analysis at different times of the day. The ML-based models were trained using the most relevant features associated with each time interval during the day (\(M_{int}\), \(A_{int}\), \(E_{int}\), \(N_{int}\)) and \(F_{int}\). Following a voting FS strategy, we selected the most relevant features for each time interval:  
* \(M_{int}\): M_Sum, M_Var, M_Adverse, M_Hypo, and M_Median;
* \(A_{int}\): A_Var, A_Entropy, A_Hyper, A_Hypo and A_Median;
* \(E_{int}\): E_Entropy, E_Median, E_Hyper, E_Var and E_Hypo;
* \(N_{int}\): N_Hypo, N_Hyper, N_Var, N_Median and N_Entropy;
* \(F_{int}\): F_Adverse, N_Median, N_SumValues, N_Var, E_Median, A_Var, E_Hyper, N_Hypo, M_Entropy, M_Hypo, E_Hypo.
# Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel
## 2 Preliminaries and System Description
### Problem Formulation  
Due to external interference such as water flow, the designed dockyard will drift randomly and frequently. This paper models USV as an extended version of Dubins, that is, the speed of the USV is not constant. Therefore, the USV can change its speed to shorten the travel time. The main principle of automatic docking is to achieve target tracking in a moving scene by adjusting the USV's heading angle iteratively, which is shown in Figure 6.  
The optimization objective proposed in this paper is to find a time-optimal trajectory to approach the mobile dockyard. By means of mathematical method, the optimization problem is formulated to minimize the travel time \(T(\Omega)\) necessary to execute the trajectory sequence set \(\Omega\) for each specific segment \(\zeta_{i}\in\Omega\), \(i\leq m\).  
\[\begin{array}{l}\min T(\Omega)=\min\sum_{1}^{m}t_{i}\\ \textbf{s.t.:}\\ \zeta:[0,path_{end}]\in SE(2)\\ \zeta(0)=q(1),\zeta(path_{end})=q(end)\\ t_{i}=\frac{|\zeta_{i}|}{V_{i}}\\ R_{\min}\leq kV_{i}\leq R_{\max}\\ V_{\min}\leq V_{i}\leq V_{\max}\\ [x,y]\in[W,L]\end{array} \tag{10}\]  
Figure 5: Six cases of the Dubins curve.
## 4 Real-Time Orbit and Clock Offset Performance
### Real-Time Clock Offset Performance  
The post-processing products from the CODE analysis center were chosen as a reference to evaluate the real-time satellite clock offset accuracy, and the assessment method was referred to in [34]. The real-time clock offset accuracy for each satellite is displayed in Figure 8. As can be seen from the figure, the clock offset accuracy of most GPS and Galileo satellites is better than 0.15 and 0.25 ns, respectively. For BDS, impacted by satellite orbit geometry, the clock offset accuracy of BDS-2 IGSO satellites is worse than the MEO satellites. Compared to BDS-2, the clock offset accuracy of the BDS-3 satellite is significantly improved. On the one hand, there are more MEO satellites for BDS-3, and more satellites can be tracked for stations located outside the Asia-Pacific region, which is beneficial for clock offset estimation; on the other hand, the improved rubidium atomic clocks and passive hydrogen atomic clocks were equipped on BDS-3 satellites, and the frequency stability of the atomic clocks was considerably improved [35]. For the GLONASS satellites, the clock offset performance is better than 0.6 ns, except for R09.  
The average clock offset accuracy for all satellites is shown in Table 4; the clock offset accuracy is 0.158, 0.163, 0.553, 0.280 and 0.317 ns for GPS, Galileo, BDS-2, BDS-3 and GLONASS satellites, respectively.  
Figure 7: Real-time orbit accuracy for GNSS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **GPS** & **Galileo** & **BDS-2** & **BDS-3** & **GLONASS** \\ \hline clock offset STD & 0.158 & 0.163 & 0.553 & 0.280 & 0.317 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average accuracy of the real-time clock offset (unit: ns).  
Figure 8: Real-time clock offset off accuracy for GNSS.
[PERSON]  
1 Laboratorio de Analisis del Aroma y Enologia (LAAE), Department of Analytical Chemistry,  
Universidad de Zaragoza, Instituto Agradimentario de Aragon (IA2) (UNIZAR-CITA), Associate Unit to Instituto de las Ciencias de la Vid y del Vino (ICVV) (UR-CSIC-GR), \(\epsilon\)/[PERSON] 12, 50009 Zaragoza, Spain; amarrufo@unizares (A. (70-95%) of these compounds are present in wine in the form of odourless hydroxyalkylsulfonates [8]. On one hand, the acetaldehyde-SO\({}_{2}\) adduct can be considered irreversible [9], on the other hand, the SO\({}_{2}\)-adducts formed with Strecker aldehydes have been shown to be reversible [10]. The free forms are released from the non-volatile adducts as the SO\({}_{2}\) in the wine disappears, either by oxidation or by reaction with other species. In this way, Strecker aldehydes release due to equilibria shifts and thus could be sensory perceived. It has already been reported that the accumulation of aldehydes could be detrimental to wine quality [11], but to date there is no sensory study that determines the concentration of Strecker aldehydes able to decrease the aromatic quality of wines. Taking into account the relevance of aldehydes and their reactivity, their removal might look interesting in wine as it is necessary in many areas such as pharmacy [12], industry [13], food [14] or biotechnology [15]. Such removal can be carried out in following different approaches such as distillation, gas stripping, pervaporation, solvent extraction, solid phase extraction [16], derivatisation [17] or precipitation and filtration from an organic solvent [15]. Nevertheless, for wine and with the intention of scaling the process of elimination of aldehydes to the winemaking process, it seems that the most appropriate methodology would be to use solid phase extraction with resins. Furthermore, if it were a reversible binding process it would allow the regeneration of the resins, as occurs with the Amberlite resin in the bisulphite form (IRA-SO\({}_{3}\) H) for the removal of 3-hydroxypropionaldehyde during the biotechnological conversion of glycerol [15]. Up to now, studies for the wine industry have been focused on the effect on organoleptic properties (i.e., colour improvement or haze prevention) of the use of ion exchange resins [18; 19; 20; 21]. Currently the studies in the winery are aimed at the search for treatments that allow for the elimination of substances recognised as wine defects such as acetic acid or ethylphenols. From these ideas, the hypothesis arose of developing a methodology that could eliminate oxidation aldehydes present in wine through reactive polymers endowed with special functional groups [22] or classic-form and modified form ion-exchange resins [23]. Therefore, the objectives of this work are to study about the sensory impact derived from the release of Strecker aldehydes from hydroxyalkylsulfonate adducts during wine storage and to evaluate the potential capacity of different commercial resins to remove acetaldehyde and Strecker aldehydes from wine.