esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Image Forensic Techniques Based on Deep Learning
## 4 Robust Image Watermarking Based on Deep Learning
### Robust Image Watermark against Non-Differentiable Attack
#### 4.2.2 Robust Image Watermark against Screen-Shooting Attack
Table 6 describes the methods from four aspects: watermark size (container size), category, robustness with BER metrics, and dataset. Table 7 describes the methods from four aspects: watermark size (container size), category, robustness with other metrics, and dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Watermark Size** \\ **Container Size** \\ \end{tabular} } & \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Robustness (BER (\%))**} & \multirow{2}{*}{**Dataset**} \\ \cline{3-3} \cline{5-6}  & & & **Distance (cm)** & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [132] \\ \end{tabular} } & 128 & \multirow{2}{*}{\begin{tabular}{c} Templated-based \\ (512 \(\times\) 512) \\ \end{tabular} } & 1.95 (20), 2.73 (40), & 4.3 (Up40), 1.17 (Up20), 7.03 (Down20), 7.03 \\  & & 11.72 (60) & & (Right20), 3.52 (Right40) \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [133] \\ (256 \(\times\) 256) \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Decoding based on \\ attention \\ mechanism \\ \end{tabular} } & 9.4 (Up45), 8.1 (Up30), 8.9 (Down30), 9.45 \\  & & 5.1 (15), 9.9 (35) & (Down45), 9.7 (Left5.8, 9.6 (Left50)), 9.8 \\  & & & (Right50), 9.3 (Right45) \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [134] \\ (512 \(\times\) 512) \\ \end{tabular} } & 32 & \multirow{2}{*}{\begin{tabular}{c} Distortion \\ compensation \\ \end{tabular} } & 2.54 (30), 3.71 (50), & 14.12 (Down30), 7.55 (Left5.1, 14.6 (Left60)), 6.7 (Bottom50) \\  & & 5.27 (Right5.1, 11.52 (Right30)) \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [138] \\ (64 \(\times\) 64) \\ \end{tabular} } & 64 & \multirow{2}{*}{\begin{tabular}{c} Keypoint \\ enhancement \\ \end{tabular} } & 0.43 (45), 0.35 (65), & 2.0 (Left60), 0.6 (Left50), 0.67 (Right30), 2.68 \\  & & 0.67 (75) & & (Right60) \\ \multirow{2}{*}{\begin{tabular}{c} [PERSON] et al. [142] \\ (400 \(\times\) 400) \\ \end{tabular} } & 10 & \multirow{2}{*}{\begin{tabular}{c} Distortion \\ simulation \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} - \\ (Right5) \\ \end{tabular} } & 1.0 (Left65), 0.7 (Left50), 0.7 (Right30), 5.3 \\  & & & (Right65) \\ \multirow{2}{*}{\
\begin{tabular}{c} [PERSON] et al. [143] \\ (128 \(\times\) 128) \\ \end{tabular} } & 30 & \multirow{2}{*}{
\begin{tabular}{c} Tamper detection \\ 0.62 (20) \\ \end{tabular} } & 2.08 (40), 1.25 (60), & 2.92/1.25 (Left70), 1.25/0.93 (Left70), 0.62/0.83 \\  & & 0.62 (20) & 1.05/1.04 (Right70002), 0.62/0.83 \\  & & & (Right7 Down40) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of deep learning-based image watermarking against screen-shooting attack with BER metrics.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 3 Packet Recovering Codes  
In this section, we analyze two solutions, based on low-delay binary and nonbinary convolutional codes, respectively. Two low-complexity sliding-window (SW)-decoding procedures are studied. The first one is based on ML-decoding applied to the ZT convolutional code inside the window and the second implies conventional BP decoding and BP decoding with using redundant parity-check (RPC) matrix of the same code [26; 27]. As explained below, RPC matrix contains linear dependent rows which destroy stopping sets of the code and improve erasure correcting capability compared to BP decoding while preserving low decoding complexity.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Quality Measures
#### 3.2.3 Reliability  
After applying the developed algorithm for quality control, the reliability analysis was divided into external and internal reliability analyses, as shown in Figures 11 and 12 which show the respective results of internal and external reliability. Despite elimination of the outlier, it is evident that its influence remained.  
Figure 9: QDOP and redundancy.  
Figure 11: Analysis of internal reliability.  
Figure 10: Posterior variance with QC and without QC. (**a**) Posterior variance without QC; (**b**) Posterior variance with QC.  
* _Internal reliability_
* _External reliability_
## 5 Simulation Results  
Numerical results have been obtained with a simulator developed in Python [32]. The GP algorithm has been implemented in DEAP [33], a Python module for evolutionary algorithms. Since the number of simulations required by the GP-based approach is high, we have developed a pure and lightweight simulator that integrates well with the DEAP module. This is the main reason for using the simulation available in [32], rather than using other network simulators for VANETs such as NS-2 and NS-3, which requires considerably more computation resources. Regarding the GP parameters, the population size is 100 individuals, and up to three depths of the tree have been evaluated, such as 4, 5, and 6. The \(\mathbf{p_{c}}\) used is within the interval [0.6, 0.8] with a step of 0.1. The \(p_{m}\) tested is within the interval [0.05, 0.2] with a step of 0.5. Therefore, 12 different parameter settings of the GP have been evaluated. The limit of operations among the \(a\) terms is 100.Both the depth of the trees and the number of operations reduces the algorithmic complexity of the proposed approach. Higher values of both terms will lead to solutions very difficult to implement in real-life scenarios. The number of generations is 100 and each setting is evaluated with 10 different seeds of random numbers. Regarding the VANET scenarios, they are based on real maps using C4R tool [34]. The density of vehicles varies within the interval [100, 200] in steps of 10. The VANET scenario is based on a real map of the city of Seville in Spain, which is shown in Figure 3. The size of the scenario is 2 km \(\times\) 2 km. The vehicle's communication range \(\mathbf{R}\) is 250 m and the unit disk model is used. To measure the correlation, we consider that the vehicles are static during the simulation. Notice that this assumption will not affect the correlation results between the Euclidean distance and the obtained dissimilarity metric in real mobile scenarios.  
Figure 3: The VANET scenario considered for the simulations.
## 2 The Proposed Method
### Feature Space Conversion  
The test data collected from the above experiment were adopted for further analysis. After data pre-processing, time and frequency domain analysis and WPT were utilized to extract the thirty-eight features in original feature space, here a 9810 \(\times\) 38 array composed of original feature was obtained. The eight representative original features that have been widely used for further research are displayed in Figure 5, in which all the waveforms of the eight features have an obvious mutation at time point 7000, however, only four of them (Mean frequency (MF), WPT1, WPT5, and WPT6) show slight abnormality at time point 5300, while the other four features (Skewness, Kurtosis, Crest, and Standard deviation frequency (SDF)) seem unable to detect the early slight abnormality.  
Next, the instinct dimension quantity of the thirty-eight original features was computed with the help of the MLE algorithm, the answer was got as six. Then the local non-linear space conversion technique LE was applied to project the features from high dimension space to lower dimensional space according to the steps in Section 2.2, hence, the 9810 \(\times\) 38 original feature dataset was transformed  
Figure 4: Test rig.  
Figure 5: Eight of the features in original feature space.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Number** & \begin{tabular}{c} **Ball** \\ **Diameter (mm)** \\ \end{tabular} & \begin{tabular}{c} **Contact** \\ **Angle (deg)** \\ \end{tabular} & \begin{tabular}{c} **Rotation** \\ **Speed (RPM)** \\ \end{tabular} & \
\begin{tabular}{c} **Load** \\ **(kN-m)** \\ \end{tabular} &
\begin{tabular}{c} **Sampling** \\ **Rate (kHz)** \\ \end{tabular} \\ \hline \hline ZA-2115 & 4 & 10 & 0 & 1500 & 26.50 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bearing parameters and experimental conditions.  
into a 9810 \(\times\) 6 one that composed of mapping features. From Figure 6, it can be discovered that four of the projected features (features 1, 2, 3, and 5) started becoming abnormal around time point 5300, while there occurred an obvious mutation around time point 7000 for all these six features.  
It can also be easily discovered that the mapping features in projected feature space performed much better than the features in original feature space for abnormality detection. The abnormal phenomena expressed by the features described above may indicate that the bearing applied in the test might have slight faults around time point 5300, while serious degradation might occur around 7000. Additionally, since all the curves of both original and projected features before time point 5300 performed smoothly and stably, it can be inferred that the machine was running under normal condition during the period before this time point.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing
### Characteristic Wavelength Extraction of Spectral Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic Wavelength** & **Algorithm Principle** & **Advantages and** & **Disadvantages** & **Related Literature** \\ \hline Principal component & The n-dimension feature is mapped to the k-dimension (k \(<\) n), which is a new orthogonal feature. & Reduces the algorithm & Does not have good nonlinear & [PERSON] (2013), \\  & & & calculations, removes noise, has & dependence, & [PERSON] (2018), \\  & & & no parameter limitation. & Does not effectively estimate the number & [PERSON] (2015), \\  & & & & of potential hidden variables. & [PERSON] (2019) \\ \hline Successive projections & A forward variable selection & Eliminates redundant & The selection of the initial band is & [PERSON] (2020), \\  & algorithm minimizes the collinearity & information in the original & random, which makes the subsequent & [PERSON] (2014), \\  & & vector space. & spectral matrix. & band have more redundant information. & [PERSON] (2020). \\ \hline Genetic algorithm (GA) & A search algorithm based on natural selection and a population & Has low complexity, few parameters, high efficiency, and & Limited exploration ability, it is too easy & [PERSON] (2014), \\  & genetic mechanism. & is easy to realize. & to converge on the local optimal solution. & [PERSON] (2016). \\ \hline Competitive adaptive & Through the search method and the criteria of evaluating the importance & The calculation speed is fast & Excessive emphasis emphasis on the cross-validation results of the calibration & [PERSON] (2019), \\  & & degree of variables, the optimal & while the number of & set is prone cause inconsistencies between \\  & subset of variables can be obtained. & wavelengths is small. & the calibration set and the validation set. & [PERSON] (2016). \\ \hline \multirow{4}{*}{Particle swarm optimization (PSO)} & A random particle is initialized with a random solution, and then the optimal solution is found through an & There is no crossover or mutation operation, few parameters need to be adjusted, & It is easy to fall into the local optimum, \\  & iterative process. In each iteration, & parameters need to be adjusted, & resulting in low convergence accuracy. \\  & the particle tracks two extremum to & and the structure is simple. & Decre Decre and combinatorial optimization \\  & update itself. & & problems are difficult to solve. \\ \hline Random frog (RF) algorithm & Blonic optimization algorithm based on swarm intelligence. & This method is easy to understand and is robust. & The local exploration ability is poor, The \\  & & & understand is robust. & optimization result is not ideal, and the \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithms for characteristic wavelength extraction of spectral data.
## 3 Materials and Methods
### Learning-Style Classification-Performance Evaluation  
In this study, standard performance metrics, such as accuracy, precision, recall, F1-score, and area under the curve (AUC), were employed to assess the performance of the classifiers in differentiating EEG recordings from reading, lecture, discussion, and discussion. To obtain consistent recognition performance, fivefold cross-validation was employed in this study. For fivefold cross-validation, the features were randomly divided into five relatively equal subsets (folds). Four out of the five subsets were then used for model training, while the fifth subset was used for evaluation. This fivefold process was performed five times so that each fold was used as a test set, resulting in five classifier accuracy scores for each feature set classification method. To evaluate the overall classification performance, the average and the standard deviation (SD) of the final metrics were computed across the five folds. The cross-validation was conducted in a manner that preserved the independence of student trials across folds, ensuring that data from the same student did not appear in both the training and testing folds simultaneously. This strategy helped in obtaining reliable estimates of the model's performance and in selecting the optimal hyperparameters without compromising the model's ability to generalize to unseen data.
## 2 Materials and Methods
### Characterisation of the Fabricated Scaffolds--Porosity, Pore, and Strut Sizes  
Optical microscopy was performed on the scaffold to image the scaffold structure and cross-section using a Nikon SMZ800 Zoom Stereomicroscope (Nikon Instruments, Tokyo, Japan). Image J v1.51q (National Institute of Mental Health, Bethesda, MD, USA) was used to measure pore and strut size. A Scanning Electron Microscope (SEM) JEOL 6500 FEG SEM (Advanced Microbeam Inc., Vienna Center, OH, USA) was used to image scaffolds for closer inspection of pore and strut formation and to identify any printing errors. Scaffolds were coated with 10-20 nm of gold using an Auto Sputter Coater (Agar Scientific, Stansted, UK). To examine internal side geometry, scaffolds were also sectioned along the Z plane using a scalpel to expose the cross-sectional area. The initial porosity of the scaffold was measured using the liquid displacement method [25], where each scaffold (n = 4) was submerged in a known volume of ethanol (V\({}_{1}\)). The final volume of the liquid and the liquid-impregnated scaffold was measured as V\({}_{2}\), and the volume of the remaining liquid when the liquid-impregnated scaffold was removed as V\({}_{3}\). The porosity of the scaffold was then calculated using Equation (1).  
\[\text{Porosity}=\frac{\text{V}_{1}-\text{V}_{3}}{\text{V}_{2}-\text{V}_{3}} \tag{1}\]
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 3. Robust Optimization for Household Load Scheduling
### Solution to the Robust Counterpart  
The household load scheduling with uncertain parameters is finally transformed into a mixed integer linear programming (MILP) problem. In this MILP problem, the objective function is still the one before transformation, but the constraints consist of the new constraints as presented as Equations (34)-(35), (42)-(47), and the original constraints as presented by Equations (11)-(23). Many existing methods, tools, and commercial software are available to obtain the ultimate solution. Among the abundant methods, the heuristic-based evolutionary algorithms, like particle swarm optimization (PSO), the model predictive method, and the commercial software CPLEX are some representative ones.  
However, because of numerous appliances, the MILP proposed in this paper contains too many decision variables and constraints which may cause the curse of dimensionality. In this situation, the heuristic algorithms are not suitable for solving. Given the powerful calculating capacity and various solvers, this paper adopts the commercial software CPLEX (Version 12.6.3.0, IBM, New York, NY, USA, 2016) to gain the optimal schedules. CPLEX is a commercial mathematical programming solver produced by IBM Corporation in the United States. It can solve various mathematical programming problems, including linear programming (LP), quadratic programming (QP), and mixed integer linear programming (MILP) stably and efficiently.
## 5 Experimental Results
### Evaluation of Dataset  
To provide a more intuitive demonstration of the superior performance of our model compared to other models, we conducted visual comparisons of the dataset segmentation results. Figure 6 shows a visual comparison of the Synapse dataset, and Figure 7 shows a visual comparison of the ACDC dataset.  
Figure 6: Visualizes the results of different methods on the Synapse dataset.  
Figure 6 shows the qualitative comparison results of different methods on the Synapse dataset. We divided the comparison methods into those that are CNN-based and those that are Transformer-based. We can see in the picture that some models produce the wrong segmentations for organs, such as some models in the second row that interpret part of the background information as foreground information, and Unet and Swin-Unet in the fourth row have a big difference in the segmentation and label value for the pancreas. In addition, some models are too vague in the boundary positioning of different organs. For example, the stomach and liver parts in the network segmentation diagram of AttUnet, Swin-Unet, etc. in the third line are mixed together, so the boundary cannot be clearly defined. These errors in segmentation are due to the limitations of these network models, which only simply connect the output features of the encoder to the input features of the decoder. The semantic gap between the shallow and deep features is too large, and this simple dependency relationship cannot fully fuse multi-scale feature information, leading to segmentation errors. However, our channel-cross fusion Transformer module can improve this problem. The differences in segmentation performance accuracies are due to the limitations of CNN-based or Transformer-based network models. Our DAT module can simultaneously focus on the correlation between individual samples and the correlation between different samples. This multidimensional information can help our model achieve more accurate segmentation results.  
To further verify the generalization performance of DATUnet, we conducted qualitative comparisons on the ACDC Heart Segmentation dataset in Figure 7. It can be seen that the segmentation results of Swin-Unet have rough organ edges, and TransUnet has poor segmentation results for small targets, such as in the second row and fourth column, where a part of the Myocardium organ is wrongly segmented as the Right Ventricle organ. For example, in the third column of the second row, some Myo organs are incorrectly divided into RV organs, and, in the third column of the third row, the Myo undersegmentation is serious. The UNet network on the first line incorrectly identifies the background information as the RV organ. However, our model performs well on both large and small targets, which further demonstrates that our model can improve the segmentation results in a comprehensive manner.  
Figure 7: Visualizes the results of different methods on the ACDC dataset.
###### Abstract  
With the rapid development of 5G and 6G communications in recent years, there has been significant interest in space-air-ground integrated networks (SAGINs), which aim to achieve seamless all-area, all-time coverage. As a key component of SAGINs, flying ad hoc networks (FANETs) have been widely used in the agriculture and transportation sectors in recent years. Reliable communication in SAGINs requires efficient routing algorithms to support them. In this study, we analyze the unique communication architecture of FANETs in SAGINs. At the same time, existing routing protocols are presented and clustered. In addition, we review the latest research advances in routing algorithms over the last five years. Finally, we clarify the future research trends of FANET routing algorithms in SAGINs by discussing the algorithms and comparing the routing experiments with the characteristics of unmanned aerial vehicles.  
unmanned aerial vehicles; flying ad hoc network; routing algorithms; space-air-ground integrated +
Footnote †: journal: Advances in Neural Networks  
1 Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China; s210700508s.upc.edu.cn (Y.L.); [EMAIL_ADDRESS] (H.Z.); [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (P.Z.)
## 3 Results and Discussion
### Free Volatile Aroma Compounds  
The changes in volatile free aroma compounds in the juice of Shine Muscat grapes from 16 to 22 WAFB during ripening are presented in Table 2. The total alcohol content tended to decrease by 20 WAFB and then increased thereafter. Six-carbon alcohols (C6-alcohols) such as (_E_)-2-hexen-1-ol, (_Z_)-3-hexen-1-ol, and 1-hexanol comprised the majority (>98%) of the total alcohol content. In particular, (_E_)-2-hexen-1-ol, and 1-hexanol significantly affected total alcohol content. These alcohol species are responsible for the herbaceous flavor of unripe grapes [25]. The total aldehyde content tended to increase with ripening. The major species were hexanal and (_E_)-2-hexenal, which accounted for >99% of the total aldehyde content. The result was in agreement with that of a previous study [26]. Aldehydes partly contribute to the herbaceous characteristics of grapes. Monoterpenes are the major volatile compounds responsible for the intensity of muscat flavor in Shine Muscat grapes. Among them, linalool, geraniol, nerol, citronello, and \(\alpha\)-terpineol are the major constituents of the floral and fruity flavors of Muscat grapes [19]. However, the concentrations of nerol, citronello, and \(\alpha\)-terpineol were relatively lower than those of geraniol and linalool. The concentration of geraniol tended to decrease with ripening, which is in agreement with a previous study by [PERSON] et al. [27]. They reported that the geraniol concentration in Muscat grapes was higher than that of other monoterene compounds before veraison and then decreased during ripening. The concentration of linalool, the most abundant monoterepene compound in ripe berries, tended to constantly increase during ripening. Thus, the total monoterepene content was dependent on linalool content. Oxidized forms of linalool, including funenoid and pyranoid forms, were also present, and their concentrations tended to increase during ripening.  
The volatile aroma compounds were grouped according to their aroma types, and their proportions are shown in Figure 2. Green aroma compounds occupied the highest proportion (69-82%), regardless of the ripening degree, which seemed to be largely affected by (_E_)-2-hexenal and hexanal. It was presumed that green and herbaceous flavors would govern the overall flavor. The proportion of fruity aroma compounds tended to decrease owing to the decreasing trend of (_E_)-2-hexen-1-ol with increasing ripening. Meanwhile, the proportion of floral aroma compounds tended to increase because of the increasing linalool content. The proportion of herbal aroma compounds was significantly affected by the concentration of 1-hexanol.