esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Implementation and Setup Details
The SSIM value ranges from \(-1\) to \(1\), where a value closer to 1 indicates better image quality. For two input images, \(X\) and \(Y\), the mathematical expressions for SSIM are as follows:  
\[\text{SSIM}(X,Y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\delta_{xy}+C_{2 }\right)}{\left(\mu_{X}^{2}\mu_{Y}^{2}+C_{1}\right)\left(\delta_{x}^{2}\delta_{ y}^{2}+C_{2}\right)} \tag{16}\]  
In the SSIM formula, \(\mu_{x}\) and \(\mu_{y}\) represent the pixel means of images \(X\) and \(Y\), respectively, while \(\delta_{x}\) and \(\delta_{y}\) are the variances of \(X\) and \(Y\). The covariance of \(X\) and \(Y\) is denoted by \(\delta_{xy}\). Additionally, there are two constants, \(C_{1}\) and \(C_{2}\), which play a role in preventing division by zero. Relative Payload (RP) is utilized as an evaluation metric to compare the steganographic capacity of each steganographic image. RP is calculated as the ratio of the amount of secret information that a steganography method can embed to the cover image capacity. This metric gauges the efficiency of information hiding in the image steganography. A higher relative payload signifies that the method can embed more secret information in the image. The mathematical expression for RP is as follows:  
\[\text{RP}=\frac{bits(\textit{secret}\;\textit{information})}{bits(\textit{ cover}\;\textit{capacity})}\times 100\% \tag{17}\]  
Furthermore, we assess the security of our CHASE and benchmarks in Section 4.4 using two prominent types of steganalysis tools: statistical steganalysis and deep learning-based steganalysis.
## 2 Methodology
### Experimental Process  
The overall flow chart is shown in Figure 3. Firstly, all images are proportionally randomly divided into the training set, calibration set and testing set. Image augmentation and random 50% confetti noise methods are applied to the training set to train the improved VGG-16 model. Secondly, binary processing is carried out on the image labels of the calibration set by using the conversion function, i.e., the non-crack images are assigned 0, and the crack images are assigned 1. The trained network is used to predict the average thermal scores and binary labels of all images in the calibration set. Then, a receiver operating characteristic (ROC) curve is created, where the \(x\)-axis and the \(y\)-axis are the false positive rate and the true positive rate, respectively. The ROC curve is a stepped line, and it increases sequentially from left to right. To express the performance of the classifier more intuitively, the area under the ROC curve (AUC) is proposed, and as the value of AUC becomes larger, the network performance becomes better. Another measure indicator of the ROC curve, the maximum Youden index, is used to determine the best boundary of the classifier, namely the thermal threshold. The thermal threshold is the score corresponding to the \(x\)-axis of the maximum difference between the true positive rate and the false positive rate. Finally, the image labels of the original testing set are binarized. Then, the trained classification network model is used to predict the preprocessed testing set images, and average thermal scores are obtained. The classification results are obtained with a comparison on the thermal threshold and the average thermal scores, i.e., if the average thermal score of a sample is greater than the thermal threshold, it is considered a crack image; otherwise, it is considered a non-crack image.  
In the experiments, all the images in the testing set are classified and labeled with corresponding class labels, and then a comparison is made between these labels and the real ones to determine the confusion matrix of the model. Subsequently, the evaluation indicators given in Section 2.3 can be calculated in light of the confusion matrix. To obtain reliable results, a simple method is used to handle classification with the imbalanced datasets, including the training set, calibration set and test set. At first, the image sequences of the dataset are shuffled and randomly divided into the training set, calibration set and testing set according to proportion. Secondly, steps 1-2 in Figure 3 are recycled 10 times to obtain 10 neural network models with different evaluation indicators. Lastly, the 10 models are compared, and the best one is selected as the classifier for the corresponding dataset.  
that are predicted as non-cracks; and TN is the number of non-crack samples that are predicted correctly.
## 2 Materials and Methods
### Color Measurements  
The Hunter Miniscan XE Plus colorimeter (HunterLab, Reston, VA, USA) was used to measure the color of randomly chosen fillets. It was set to collect spectral data with illuminant A/observer D65/10\({}^{\circ}\) on the CIELAB scale (L*, a*, b*). Before starting the measurements, the device was calibrated using ceramic reference plates (white and black). Color measurements were performed on fresh meat (approx. 9 h after slaughter, n = 30) and refrigerated meat (7, 9, 12, and 14 days, air temperature approx. 2 \({}^{\circ}\)C, n = 6), in 3 repetitions for each sample. Additionally, when assessing the color of the meat, two elements were taken into account: the tenderloin (m.pectoralis minor--MPn) and the fillet (m.pectoralis major--MPJ), including the external surface (ES) and the internal surface (IS). To increase the objectivity of the assessment, places with visible defects (e.g., hemorrhages) were omitted during the measurements. Figure 2 shows a visualization of the different colors of the fillets in terms of brightness (L* parameter).  
Figure 1: Fillet with small (**a,c**) and large (**b**) hemorrhages.
## 2 Forecasting Models
### State Space Models
To distinguish the models with additive and multiplicative errors, an extra letter E was added: the triplet of letters \((\mathrm{E},\mathrm{T},\mathrm{S})\) refers to the three components: \"Error\", \"Trend\" and \"Seasonality\". The notation \(\mathrm{ETS}(,)\) helps in remembering the order in which the components are specified. For illustration, the equations of the model \(\mathrm{ETS}(\mathrm{A},\mathrm{A},\mathrm{A})\) (additive Holt-Winters' method with additive errors) are [15]:  
\[y_{t} = l_{t-1}+b_{t-1}+s_{t-m}+\varepsilon_{t}, \tag{5}\] \[l_{t} = l_{t-1}+b_{t-1}+\alpha\varepsilon_{t},\] (6) \[b_{t} = b_{t-1}+\beta\varepsilon_{t},\] (7) \[s_{t} = s_{t-m}+\gamma\varepsilon_{t}, \tag{8}\]  
and the equations of the model \(\mathrm{ETS}(\mathrm{M},\mathrm{A},\mathrm{A})\) (additive Holt-Winters' method with multiplicative errors) are [15]:  
\[y_{t} = \left(l_{t-1}+b_{t-1}+s_{t-m}\right)\left(1+\varepsilon_{t} \right), \tag{9}\] \[l_{t} = l_{t-1}+b_{t-1}+\alpha\left(l_{t-1}+b_{t-1}+s_{t-m}\right) \varepsilon_{t},\] (10) \[l_{t} = \alpha\left(y_{t}-s_{t-m}\right)+\left(1-\alpha\right)\left(l_{t- 1}+b_{t-1}\right),\]0 (11) \[l_{t} = \alpha\left(y_{t}-s_{t-m}\right)+\left(1-\alpha\right)\left(l_{t- 1}+b_{t-1}\right),\]1  
where  
\[l_{t} = \alpha\left(y_{t}-s_{t-m}\right)+\left(1-\alpha\right)\left(l_{t- 1}+b_{t-1}\right),\]2  
and \(\varepsilon_{t}\) is a zero mean Gaussian white noise process with variance \(\sigma^{2}\). Equations (5) and (9) are the measurement equations and Equations (6)-(8) and (10)-(12) are the state equations. The measurement equation shows the relationship between the observations and the unobserved states. The transition equation shows the evolution of the state through time. It should be emphasized that these models generate optimal forecasts for all exponential smoothing methods and provide an easy way to obtain maximum likelihood estimates of the model parameters (for more details about how to estimate the smoothing parameters and the initial states by maximizing the likelihood function, see pp. 68-69 [9]). After identifying an appropriate model, we have to check whether the residuals are statistically insignificant which can be done through a Portmanteau test. According to [16], the most accurate Portmanteau test is the Ljung-Box test. The Ljung-Box test tests whether the first \(k\) autocorrelations of the residuals are significantly different from what would be expected from a white noise process. The null-hypothesis is that those first \(k\) autocorrelations are null, so large \(p\)-values are indicative that the residuals are not distinguishable from a white noise series. Using the usual significance level of 5%, a model passes a Ljung-Box test if the \(p\)-value is greater than 0.05 [17]. If the model fails the Ljung-Box test, another one should be tried; otherwise, forecasts can be calculated.
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 3 Results
### Photodegradation Reaction Kinetics  
In this paper, we examined seven factors that may have impacts on the degradation rate and they are hydrogen peroxide concentration, ionic liquid concentration, temperature, pH, side chain length, and chloride ion concentration. Combined with the degradation rate constant and the half-life of the ionic liquid, we found that the ionic liquid concentration (\(c_{0}\)), temperature (\(T\)), and hydrogen peroxide concentration (\(G\)) have a clear mathematical relationship with the rate constant within a certain range, and the rest of the conditions are not. Therefore, the power exponent equation (Equation (12)) is used to represent the change of ILs content over time during degradation. The specific calculation methods are shown in Figure S3.  
\[k=f(G,\,c_{0},T)=\varepsilon G^{a}c_{0}^{b}T^{c}, \tag{12}\]  
We perform a regression analysis of the rate constant \(k\) and the hydrogen peroxide concentration (50-150 mM) to obtain the relationship between \(k\) and hydrogen peroxide concentration(\(G\)).  
\[k=k_{1}G^{a}, \tag{13}\]  
\[lnk=lnk_{1}+alnG, \tag{14}\]  
In Equation (13), \(k_{1}\) and \(a\) are constants. When the concentration of hydrogen peroxide is in the range of 50-100 mM, \(\ln k\) is plotted on the abscissa and \(\ln G\) is plotted on the ordinate. We can calculate the values of \(k_{1}\) and \(a\).  
\[k=0.02260G^{0.1900}, \tag{15}\]  
Figure 6: Effect of pH on degradation; [IL] = 0.1 mM, t = 45 °C, [H\({}_{2}\)O\({}_{2}\)] = 100 mM.  
Similar to the above method of calculation, when the IL concentration is in the range of 0.05 to 0.20 mM, the values of \(k\) and \(b\) can be calculated as follows:  
\[k=0.0071c_{0}{}^{-0.8063}, \tag{16}\]  
When the temperature is between 25 and 45 \({}^{\circ}\)C, the values of \(k\) and \(T\) can be calculated as follows:  
\[k=0.003697^{0.7037}, \tag{17}\]  
In this paper, the total reaction kinetic constant could be expressed by a power exponential equation (Equation (18)). When pH = 5.06, \(T\) = 45 \({}^{\circ}\)C, \(G\) = 100 mM, and \(c_{0}\) = 0.1 M; the value of \(\varepsilon\) can be found to be \(2.44\times 10^{-4}\). Therefore, the general equation can be written as  
\[k=2.44\times 10^{-4}G^{0.1900}c_{0}{}^{-0.8063}T^{0.7037}, \tag{18}\]  
The influence of three factors on the degradation of benzothiazole ILs from strong to weak was: initial concentration of IL (\(c_{0}\)) > temperature (\(T\)) > concentration of hydrogen peroxide (\(G\)). Moreover, hydrogen peroxide concentration, temperature, and degradation rate constant are positively correlated, while ionic liquid concentration is negatively correlated.
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
## 2 Concept of the Mesh Based Enterprise Wireless Sensor Network  
It is a core network design with numerous redundant connections between network nodes shown in Figure 1. Thus, if any nodes fail in a wireless mesh architecture, there are many alternative ways to communicate with each node. A mesh network combines other topologies such as Star, Ring and Bus in a hybrid topology. Moreover, specific WAN architectures, like the Internet, use mesh routing, which works even in disaster [1].  
Full mesh and partial mesh are the two mesh topologies. When every node in a network has a connection linking it to every other node, this is referred to as full mesh  
Figure 1: Mesh based enterprise wireless sensor network.  
topology. The maximum concentration of redundancy is achieved by using a full mesh. As a result, if a node fails, network traffic may be diverted to any other node. Specific nodes are fully meshed with partial mesh, while others are linked to one or two others. In peripheral networks connected to a full mesh backbone, the partial mesh is prevalent. Partial mesh is less costly but less redundant than complete mesh [18].
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation
### Impact of \(K\)  
The parameter \(K\) determines the number of similar neighbors. To study the impact of \(K\) on the hybrid model HL-RW, we set the \(K\) increasing from 1 to 10. The other parameter \(\lambda\) is set to the default value, and the results are shown in the following Figure 7.  
Figure 6: Sensitivity to \(\lambda\). (**a**) Training set density = 5%; (**b**) Training set density = 10%; (**c**) Training set density = 5%; (**d**) Training set density = 10%.  
From Figure 7, it can be seen that in both response time and throughput datasets, the values of prediction error (Normalized Mean Absolute Error) undergo stable change, and are in a quite limited range. The optimal value can be achieved at \(K\) = 4, 5 or 6. On the one hand, it indicates that the proposed method is not sensitive to the setting of \(K\), that is, the number of similar neighbors, to different QoS properties. This is important for improving the applicability of the method, since the method can be used in different QoS properties with one unified parameter setting. On the other hand, it can be inferred that a small number of similar neighbors are enough to guarantee the prediction accuracy, which is also beneficial to save the computation overhead.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 3 Routing Protocols
### Remarks  
In order to have a deeper understanding of the routing protocols of UAV swarm Ad hoc network, Table 3 compares the protocols summarized in this paper. The static routing protocols are not suitable for dynamic topology networks like UAV swarm Ad hoc network due to the fixed routing tables and less scalability. The proactive routing protocols have large overhead for maintaining tables up-to-date. Moreover, their slow reaction to topology changes results in delays. The main disadvantage of the reactive routing protocols is the high latency in routing finding. Source routing does not scale well, for large network overheads may increase because of large header size. Hybrid routing protocols combine the proactive and reactive protocols to overcome the limitations. However, in UAV networks dynamic nodes and link behaviors make it difficult to obtain and maintain information. Therefore, the topology-based routing protocols are not suitable for scenarios characterized by high dynamic movement and a large number of nodes. The geographic/position-based routing protocols add geographical location attribute to UAV nodes. This makes it outstanding in dealing with high mobility of nodes and frequent change of topology in UAV networks.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**Routing** & **Suitable for** & **Packet Delivery** & **Routing Finding** & **Large** & **Routing** & **Packet Loops** & **Link** & **High** & **Location** \\
**Protocols** & **Dynamic Topology** & **Solable** & **Delays** & **Delays** & **Overhead** & **Loops** & **Loss** & **Failure** & **Bandwidth** & **Services** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Routing Protocols.
## 1 Introduction
### Dimensioning of Anti-Bending Bars
For example, the maximum length of the anti-bending bars increased according to the natural frequency of the experimental model of the carbody with anti-bending bars from 900 mm, at \(f_{\rm{b}p}^{b}=12\) Hz, to 1006 mm for \(f_{\rm{b}p}^{b}=18\) Hz, respectively, 11.78%. The increase in the minimum diameter of the anti-bending bars was 89%, respectively, from approx. 4.19 mm to 7.92 mm. Figure 16: Minimum diameter of the anti-bending bars. Figure 15: Maximum length of the anti-bending bars. It was also noted that the natural frequency of the vertical bending of the experimental carbody model could be brought outside the range of sensitivity of the human organism to vertical vibrations for anti-bending bars with a maximum length of 907 mm and a diameter greater than 4.288 mm. Sizing of the anti-bending bars could be done starting from a certain imposed frequency of vertical bending of the experimental model of the carbody with anti-bending bars, convenient from the point of view of ride comfort, and conveniently choosing the length of the bars. Applying the algorithm presented above, the value of the diameter of the bar was obtained, the value of which was then rounded to a whole number for practical reasons. Finally, the natural frequency of the vertical bending of the experimental model of the carbody with anti-bending bars was calculated and it was verified that it was not significantly different from the one imposed initially. For example, if the length of anti-bending bars \(l_{b}=900\) mm was imposed and adopted (value which is less than the limit of 966.3 mm, according to Figure 15), then \(D_{b}=6.22\) mm results. Rounding to \(D_{b}=6\) mm, a deviation of approx. 2.34% in relation to the imposed frequency was obtained. To experimentally test the effectiveness of the method of reducing the vertical bending vibrations of the experimental carbody model with the help of anti-bending bars, several constructive variants of the anti-bending bars could be adopted. For instance, the solution of anti-bending bars of fixed length, \(l_{b}=900\) mm, but with different diameters could be adopted. Table 4 shows the values of the natural frequencies of the vertical bending of the experimental model of the carbody with anti-bending bars that could be obtained for bars with a diameter between 5 and 8 mm, mounted at \(l_{b}=40\) mm.
## 3 Proposed Method
### Hardware Configuration  
Figure 3 shows the hardware configuration of the ALCC-glasses, which is composed of a scene camera, a beam-splitter, a transmissive LCD panel, an OST-HMD, and a user-perspective camera. The scene camera is used for capturing the scene image, \(I_{S}\), which is required for computing the compensation image, \(I_{C}\). In order to achieve pixel-specific color modification, the image shown on the LCD, the one displayed on the OST-HMD, and the external environment's image must be aligned. We used a 50/50 beam-splitter to separate the environmental light equally into the scene camera and to the LCD and the OST-HMD. The environmental light will decay after passing through the LCD and the OST-HMD. Moreover, nonlinear distortion usually occurs when rendering an image on an OST-HMD. We realized the color calibration by taking into account the decay and distortion in generating the compensation image. For this purpose, a camera was installed at an eye level position in the prototype to capture the user-perspective image, \(I_{U}\); these images were used to collect the data required for the calibration. The user-perspective images were also required for evaluating the effectiveness of the proposed system, which will be introduced in Section 4.  
In the current implementation, we used two Blackfly USB color cameras (BFLY-U3-23S6C-C), a Sony LCX017 LCD panel (1024 \(\times\) 768) with polarizer and controller, an EPSON BT-35E (1280 \(\times\) 720) OST-HMD, and a 50/50 beam-splitter. We also used an EIZO CS2730 LED monitor to produce environmental light. When the LCD displays a brighter color (e.g., white as \((255,255,255)_{RGB}\)), it gets more transparent. When it displays a darker color (e.g., black as \((0,0,0)_{RGB}\)), it gets less transparent and blocks the light. We processed the image taken by the scene camera, and calculated the output for the LCD and OST-HMD on a PC with an Intel Xeon CPU E31245 using a single-threaded naive implementation.  
The scene camera and the user-perspective camera both had all automatic adjustments and gamma correction turned off (\(\gamma=1\)). For alignment, we adjusted the position of the environmental light (monitor) to a position roughly consistent with, the virtual position of the image rendered on the OST-HMD. Thus, both the environment and the image being shown on the OST-HMD can be seen clearly and pixel-specific color modification becomes possible. To avoid the confusion caused by ambient light, we conducted all experiments in a dark room.  
Figure 3: Hardware configuration of the proposed prototype system.  
Figure 2: Daltonization process with an OST-HMD.
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 3 Methodology
### Structure of Adaptive Multi-Channel Residual Shrinkage Network  
The proposed diagnostic model was designed with four AMC-RSB based on the resnet18 [21]. To obtain more classification accuracy, the Meta-ACON activation function is used before the fully connected layer. Meta-ACON activation function can calculate whether to activate the channel by the output of each channel, can effectively filter the interference channel, thus improving the diagnostic accuracy. The overall architecture and specific parameters of the proposed network are shown in Figure 2.  
Figure 2: Overall architecture and specific parameters.  
In this paper, an end-to-end fault diagnosis process is proposed. First, the time series signal is sampled overlappingly to obtain the data-enhanced samples, and the samples are divided into training set and test set. Then, the training set is fed into the model to train the diagnostic model. Finally, the trained model is tested with the test set to obtain the classification results and analyzed. The overall process of the proposed AMC-RSN is shown in Figure 3.
## 2 Formulation  
### Ionospheric Parameter Model  
Near the ground, the ionosphere is weakly ionized, and the electron density and collision frequency between electrons and neutral ions can be calculated by analytical formulas [20,21]. The physical model of electromagnetic-wave propagation in the ionosphere is shown in Figure 1. However, when the height increases, the degree of ionization increases, the electron density and collision frequency of the ionosphere no longer change regularly, and the ionosphere parameters cannot be expressed by formulas [22, 23, 24]. The description of ionospheric properties involves many parameters, such as electron density and electron collision frequency. In the low ionospheric exponential model established in this paper, the electron density and collision frequency, which have a great influence on the electromagnetic-wave propagation in the ionosphere, are mainly considered. The electron density and collision frequency of the exponential ionospheric parameter model widely used in the low ionosphere are shown in Equation (1) and Equation (2), respectively.  
\[N(h)=1.43\times 10^{7}e^{-0.15h^{\prime}}e^{(\beta-0.15)(h-h^{\prime})} \tag{1}\]  
\[v(h)=1.816\times 10^{11}\mathrm{e}^{-0.15h^{\prime}} \tag{2}\]  
where \(N\) is the electron density, \(v\) is the collision frequency, \(h\) is the altitude above the ground, \(h^{\prime}\) is the reference altitude, and \(\beta\) is the gradient coefficient. The geomagnetic field is also an important reason for the anisotropy of the ionosphere [5]. In the calculation area of the model, the geomagnetic field remains constant with a strength of about 0.5 gauss, here being \(\beta_{0}=50\) uT, if its direction is about 45 degrees relative to the ground and the direction of electromagnetic-wave propagation, and the sizes of each component are \(\beta_{r}=\beta_{0}/\sqrt{2}\) and \(\beta_{\theta}=\beta_{\phi}=\beta_{0}/2\), respectively.