esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### Experimental Details
#### 4.1.2 Metrics  
For the UIEB dataset, we employed the Natural Image Quality Evaluator (NIQE) [37], the Underwater Color Image Quality Evaluator (UCIQE) [38], and the Non-Reference Underwater Image Quality Measurement (UIQM) [39] assessment metrics. For real underwater images (the UIEB dataset), the selected no-reference quality evaluation metrics are UCIQE and UIQM, both of which exhibit a positive correlation with image quality. In contrast, the NIQE metric shows a negative correlation. Together, these three metrics reflect the degree of consistency between enhancement results and human visual perception. For synthetic underwater images (the SUID dataset), a full-reference evaluation system is employed. In this system, PSNR [40] quantifies the pixel-level similarity between the enhanced image and the reference image, while SSIM [41] assesses the degree of match between the two images in terms of structural features.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 2 Semianalytical Approach for the Determination of Modulus and Damping
### The Method: Modulus and Damping Nonlinearities Evaluation (MoDaNE)
#### 2.1.1 One-Dimensional Solution  
The equation of motion for a 1D linear elastic medium, during the propagation of an elastic wave, can be easily written as:  
\[\rho\frac{\partial^{2}u}{\partial t^{2}}+\lambda\frac{\partial u}{\partial t}= S\frac{\partial^{2}u}{\partial x^{2}} \tag{1}\]  
where \(u(x,t)\) denotes the longitudinal displacement, \(\rho\) is the density, \(\lambda\) is the damping parameter and \(S\) is the elastic modulus. In the case of an infinite medium (or semi-infinite with a monochromatic forcing \(F(t)\) in \(x=0\)), the solution is a monochromatic wave:  
\[u(x,t)=U_{0}e^{-ax}e^{j(\omega(t-t_{0})-kx)} \tag{2}\]  
with phase \(\phi_{0}=-\omega t_{0}\) and amplitude \(U_{0}\) at \(x=0\) and \(t=0\). The dispersion relation provides the phase velocity \(c=\omega/k\) and the Q-factor is derived from \(Q=\omega/\alpha c\).  
There is a strong relation between the wave number \(k\), the attenuation coefficient \(\alpha\), the elastic modulus \(S\) and the damping parameter \(\lambda\). Indeed, substituting the solution (Equation (2)) into the wave equation (Equation (1)), one can derive the following expressions:  
\[S=\frac{\rho\omega^{2}}{(k^{2}-\alpha^{2})} \tag{3}\] \[\lambda=-\frac{2\alpha kS}{\omega} \tag{4}\]  
The solution remains valid also in the presence of different attenuation mechanisms described by different terms in Equation (1), but a different relation between the damping parameter \(\lambda\) and the attenuation coefficient \(\alpha\) is expected in this case. Furthermore, as discussed below, the equation and solutions remain approximately valid also in the case of weakly nonlinear media, in which only small distortions of the signal are detected. In this case, the effective modulus and damping play the same role as \(S\) and \(\lambda\), averaging over a period and over the propagation path.  
Taking into account a finite medium of length \(L\) with free-free boundary conditions, the analytical solution in \(x=L\) can be derived considering the solution as the superposition of multiple reflections:  
\[u^{+}(x,t)=\sum_{n=1}^{\infty}U_{0}e^{j(\omega t-k(2(n-1)L+x))+ \phi_{0}}e^{-\alpha(2(n-1)L+x)}\] \[u^{-}(x,t)=\sum_{n=1}^{\infty}U_{0}e^{j(\omega t-k(2 nL-x))+\phi_{ 0}}e^{-\alpha(2 nL-x)} \tag{5}\] \[u(x,t)=u^{+}(x,t)+u^{-}(x,t)\]  
The solution in (\(x=L\)) is still a sinusoidal monochromatic wave, but with different amplitude \(A\) and phase \(\phi\):  
\[u(L,t)= Ae^{j(\omega(t-t_{0})+\phi)}\] \[= Ae^{j(\omega t+\phi)} \tag{6}\]  
where their analytical expressions are  
\[A=\frac{U_{0}}{\sqrt{\cosh^{2}(\alpha L)-\cos^{2}(kL)}} \tag{7}\] \[\phi=-\text{ }\arctan\frac{\tan(kL)}{\tanh(\alpha L)} \tag{8}\]  
It is useful to recall that \(U_{0}\) and \(\phi_{0}\) are the amplitude and phase of the solution at \(x=0\) (see Equation (2)).
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
###### Abstract  
Internet Protocol version six (IPv6) is more secure than its forerunner, Internet Protocol version four (IPv4). IPv6 introduces several new protocols, such as the Internet Control Message Protocol version six (ICMPv6), an essential protocol to the IPv6 networks. However, it exposes IPv6 networks to some security threats since ICMPv6 messages are not verified or authenticated, and they are mandatory messages that cannot be blocked or disabled. One of the threats currently facing IPv6 networks is the exploitation of ICMPv6 messages by malicious actors to execute distributed denial of service (DDoS) attacks. Therefore, this paper proposes a deep-learning-based approach to detect ICMPv6 flooding DDoS attacks on IPv6 networks by introducing an ensemble feature selection technique that utilizes chi-square and information gain ratio methods to select significant features for attack detection with high accuracy. In addition, a long short-term memory (LSTM) is employed to train the detection model on the selected features. The proposed approach was evaluated using a synthetic dataset for false-positive rate (FPR), detection accuracy, F-measure, recall, and precision, achieving 0.55%, 98.41%, 98.39%, 97.3%, and 99.4%, respectively. Additionally, the results reveal that the proposed approach outperforms the existing approaches.  
ICMPv6 flooding DDoS attacks; deep learning; machine learning; intrusion detection system 2022126150 28 April 2022
## 4 Parameterization and Analysis of the Proposed TDL Model
### Doppler Spectrum of Per Tap  
As TDL channel model is a \"block fading\" model, indicating that individual channel taps experience a \"quasi-stationary\" time variation. In other words, the large-scale parameters of channel are constant, whereas only the virtual movement causes the fast-fading. Therefore, to further model the amplitude variation of each tap in each flight phase, we evaluate the delay-Doppler spectrum of each selected channel taps for all considered scenarios. Figure 6 shows example of the delay-Doppler spectrum for individual scenarios. Considering the noise floor in the Doppler spectrum, we focus on the delay-Doppler spectrum for each tap within the frequency range \(|f|\leq 250\) Hz for a detailed visualization.  
Figure 7: The channel model is modeled in typical flight phases of UAV. Subfigures (**a–d**) are the measured PDF of normalized empirical APDP for FS1 to FS4 models, respectively.  
In scenario FS1, where the UAV lands vertically, the LoS path is the dominant component with much stronger power than MPCs. As a result, the Doppler spectrum caused by MPC at individual taps are rather weak compared to the first tap that contains the LoS path. Therefore, in this scenario, the Doppler frequency caused by scattering component is very small.  
For the FS2 and FS3 scenarios, the broadened Doppler spectrum can be clearly observed especially for channel taps with large delays, which may be caused by the complex terrestrial conditions. Correspondingly, when the UAV flies at a low altitude, e.g., lower than 300 m, a relatively wide spread in the Doppler spectrum can be considered, due to the effect of ground scattering MPC than causes a wide range of Doppler frequencies at different delays. The differences in the Doppler spectra when the UAV is flying at different altitudes (scenarios FS4 to FS6) are relatively small, though that spread in the Doppler spectrum caused by ground MPCs is also noticed.  
In addition, we also evaluate the Doppler spectrum for individual channel taps in the considered UAV flight phases. Figure 8 shows the Doppler spectrum of the first channel tap for the scenarios FS1 and FS2, compared with different types of typical spectra. It can be seen that the strongest component is approximately 50 dB higher than the rest Doppler frequency components for scenario FS1 and FS2. The \"Bell\"-type spectrum shows the best performance among considered spectrum types with minimum error. As such, we propose to use the \"Bell\"-type spectrum for the first two channel taps, and the \"Jakes\"-type spectrum for the remaining taps. It is also worth mentioning that Doppler spectrum for channel taps with delay larger than 400 ns is not considered in the model due to very weak power, though that the \"U-shape\" Doppler spectrum for those taps can be observed in Figures 6 and 7b,c. In addition, the fitted parameter \(A\) of Bell-type spectra are summarized in Table 4.
## 2 Background and Related Work
### DRL Framework for Traffic Signal Control
Finally comes the output layer, with 9 neurons, and each one will display the Q-Values for each action. The actions will be linked to traffic management in the next sections. The next action that the agent will choose is determined by the maximum Q-Value output. To improve this prediction of Q-Values, a Mean-Squared-Error function is used that is a mathematical function that quantifies the difference between the predicted Q-values and the target Q-values, as it is possible to see in Equation (3). \[MSE\ Loss=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}Q_{target}-Q_{pred}\Bigr{)}^{2} \tag{3}\]  
where \(N\) is the number of samples in the memory, \(Q_{pred}\) is the \(Q\)-value predicted by the main network, and \(Q_{target}\) is acquired using a network similar to the main one but that is not trained. At certain intervals of epochs, episodes that are used to train the main network, and the weights of this network are copied to the secondary network, bringing greater stability to the training. The \(Q_{target}\) values are calculated based on the following Equation (4). Where \(r_{t}\) is the reward obtained and \(\gamma\) is a discount factor applied to the \(maxQ_{target}\) value, lowering the importance of the future reward compared to the immediate reward. \[Q_{target}=r_{t}+y\cdot maxQ_{target}(s_{t+1,d}) \tag{4}\]  
The MSE loss function calculates the squared difference between predicted and target values, aiming to minimize this difference during training. The model adjusts the weights \(\mathfrak{dk}\) of the neurons to better approximate the Q-value and improve predictions, leading to better decision making by the agent. The network is evaluated based on the average number of cars in queues, showing that it can manage traffic. However, without communication between agents about lane occupancy, peaks in vehicle queues can occur. As traffic scenarios become more complex with multiple intersections, communication between agents becomes crucial. In simpler cases, where vehicles only pass through one junction, communication is less important. But with closely spaced junctions and heavy traffic, such as during rush hour, agents must coordinate and exchange information to manage queues effectively. This dynamic phase activation reduces congestion and ensures smoother traffic flow.
# Marketing Decision Support System Based on Data Mining Technology
## 2 Work Related to Decision Support System
The data show that the DSSs successfully put into use abroad only accounts for approximately 30% of the total R&D, and the same is true in China [22]. There are many reasons for the failure of DSS development, but there are two main reasons. First, traditional DSSs can only provide data-level support in the process of auxiliary decision-making. However, the data needed for practical decision-making are often distributed and heterogeneous, which limits the adaptability of the system and does not meet the needs of the decision-making process. Secondly, the traditional DSS requires the decision-makers not only to have professional domain knowledge, but also to have higher knowledge of the DSS construction model, which makes it difficult for the decision-makers to understand and accept [23]. With the emergence of data warehouses, online analytical processing, and data mining technology, a new generation of decision support system based on DW, OLAP, and DM has been proposed. Because of the inherent connection and complementarity among the three, combining them to design a new DSS architecture, that is, a solution based on data warehouses and using data mining and OLAP tools as means, can give full play to their respective advantages. Utilizing the massive data existing in the enterprise to mine valuable knowledge and rules can provide more effective support for decision-making. The new decision support system is data-driven, which is less difficult to develop. It makes up for the shortcomings of traditional DSSs, and has become a hot direction of the development of decision support system. Through the organic combination of DM technology and DSSs, this paper designs a set of intelligent DSSs, and applies it in marketing. Specifically, the decision support system extracts, transforms, and loads various business data related to marketing decisions according to the theme, and establishes a unified, standardized, and highly shared comprehensive theme data center. On this basis, OLAP and data mining technology are used to analyze data from multiple perspectives and extract potential knowledge from them, so as to establish an efficient marketing decision support system [24]. The system provides assistant decision support for management and decision-makers in new product development, product pricing, market maintenance and development, competitor analysis, customer relationship management (CRM), marketing goal formulation, product promotion, advertising promotion, performance evaluation, employee management, and many other aspects related to marketing decisions, and lays a solid foundation for enterprise development and market development.
## 2 Model and Methodology
### System Overview  
We summarize the hybrid day-ahead solar radiation forecasting model described in Figure 2. The procedure mainly involves two steps, that is, the solar radiation from WRFv3.9.1 model given the best configuration of radiation schemes and the implementing Kalman filter considering the clearness index. In order to validate whether the forecasts from the novel hybrid model can be improved, a controlled experiment is conducted as displayed in Figure 2:  
* KF_TS: Implementing Kalman filter on the time-sequence of forecasts.
* KF_Kt: Implementing the clearness index based Kalman filter on three rearranged forecasts data series stated in Section 2.3.  
The total length of the sequence of forecasts is 12 months and the proportions of the three rearranged data series are mentioned in Section 4.2. Two weeks are chosen as a training period for implementing the Kalman filter properly for both cases.
## 2 Theory
### Establishment of the Calibration Equation
\right]\left[T_{12}^{\text{Lk}}\right]\left[T_{23}\right]\left[ \begin{array}{cc}a_{\text{m21}}^{\text{Lk}}&a_{\text{m22}}^{\text{Lk}}\\ b_{\text{m21}}^{\text{Lk}}&b_{\text{m22}}^{\text{Lk}}\end{array} \right]. \tag{19}\]  
Then, by substituting (6) and (7) into (19), the calibration Equation resulting from L\({}_{\text{k}}\) is given as follows:  
\[\left[ \begin{array}{cc}0&1\\ 1&0\end{array} \right]\left[T_{12}^{\text{Lk}}\right]=z\left[ \begin{array}{cc} 1&1\\ \Gamma_{\text{R11}}&\Gamma_{\text{R21}}\end{array} \right]\left[ \begin{array}{ cc}1&0\\ 0&x\end{array} \right]\left[W_{\text{Lk}}\right]\left[ \begin{array}{cc}1&0\\ 0&y\end{array} \right]^{-1}\left[ \begin{array}{cc}1&1\\ \Gamma_{\text{R12}}&\Gamma_{\text{R22}}\end{array} \right]^{-1} \tag{20}\]  
where the matrix \([W_{\text{Lk}}]\) is fully known from measurements as shown below. \[ \begin{array}{c}\left[W_{\text{Lk}}\right]\\ =\left[\begin{array}{cc}w_{11}^{\text{Lk}}&w_{12}^{\text{Lk}}\\ w_{21}^{\text{Lk}}&w_{22}^{\text{Lk}}\end{array}\right]\\ =\left[\begin{array}{cc}a_{\text{m11}}^{\text{R11}}&a_{\text{m11}}^{\text{R21 }}\\ b_{\text{m11}}^{\text{R11}}&b_{\text{m11}}^{\text{R21}}\end{array}\right]^{-1} \left[\begin{array}{cc}a_{\text{m11}}^{\text{Lk}}&a_{\text{m12}}^{\text{Lk}} \\ b_{\text{m11}}^{\text{Lk}}&a_{\text{m12}}^{\text{Lk}}\end{array}\right]\left[ \begin{array}{cc}a_{\text{m21}}^{\text{Lk}}&a_{\text{m22}}^{\text{Lk}}\\ b_{\text{m21}}^{\text{Lk}}&a_{\text{m22}}^{\text{Lk}}\end{array}\right]^{-1} \right]\left[\begin{array}{cc}a_{\text{m22}}^{\text{R12}}&a_{\text{m22}}^{ \text{R22}}\\ b_{\text{m22}}^{\text{R12}}&b_{\text{m22}}^{\text{R22}}\end{array}\right]\end{array}  \tag{21}\]  
Figure 3: Two-port calibration. The calibration Equation (20) from the two-port standard L\({}_{\rm k}\) can also be expanded, and the results are given as follows:  
\[\left\{ \begin{array}{c}t_{21}^{\rm Lk}+t_{22}^{\rm Lk}\Gamma_{\rm R12}=w_{11}^{ \rm Lk}z+w_{21}^{\rm Lk}xz\\ t_{21}^{\rm Lk}y+t_{22}^{\rm Lk}\Gamma_{\rm R22}y=w_{12}^{\rm Lk}z+w_{22}^{\rm Lk }xz\\ t_{11}^{\rm Lk}+t_{12}^{\rm Lk}\Gamma_{\rm R12}=w_{11}^{\rm Lk}\Gamma_{\rm R11}z +w_{21}^{\rm Lk}\Gamma_{\rm R21}xz\\ t_{11}^{\rm Lk}y+t_{12}^{\rm Lk}\Gamma_{\rm R22}y=w_{12}^{\rm Lk}\Gamma_{\rm R1 1}z+w_{22}^{\rm Lk}\Gamma_{\rm R21}xz\end{array} \right.. \tag{22}\]  
Equations (13), (15), (20), and (22) are established to solve the unknowns in the calibration algorithm.
## 2 Materials and Methods
### Architecture of DF-UHRNet
#### 2.6.2 Attention Module  
Accurately identifying the open ocean is a precondition of sea ice type classification; however, summer ice lanes can provide more information relevant to sea ice classification, which can improve overall identification accuracy. As an important part of deep learning processing, the attention module has been widely used in semantic segmentation models applied to remote sensing. Simulating human perception helps the computer select the key information from the current target.  
In this experiment, we wanted the feature attention to focus more on representations such as the texture and backscattering intensity of the sea ice. For this, the Convolutional Block Attention Module (CBAM) [30], which is embedded between the decoder and the encoder, helped to reconstruct the multi-scale features extracted by the encoder module and amplified the effective feature channel weights, such that the model could pay more attention to the representational features that distinguished the sea ice categories, such as spatial and texture features. The structure of the attention mechanism is shown in Figure 6.  
The CBAM consists of a channel attention mechanism and a spatial attention mechanism, set up in series. The channel attention mechanism passes the input feature map through the global maximum pooling module and the global average pooling module and then feeds it into a shared network consisting of a multi-layer sigmoid mechanism and a hidden layer that scales the output features after characteristic stacking (summation operation). The result is fed into the sigmoid activation function to obtain F\({}^{\prime}\), the input features that will be used for generating the spatial attention mechanism. This acts to carry  
Figure 5: Overall structure of the DF-UHRNet network.  
out the spatial compression of the feature map, which yields a one-dimensional vector. One of two types of pooling is used to aggregate the spatial information of the feature map, which is then sent to the multi-layer perceptual machine species for parameter sharing. The channel attention features are generated by compressing the spatial dimensions of the semantic features and merging them, element by element, with the summation.  
The channel attention mechanism focuses on the parts of the feature map that have the most important influence. The spatial attention mechanism, on the other hand, takes the feature map that is output by the channel attention mechanism as its input and performs cascade operations on the features extracted by global maximum pooling and global average pooling. The features obtained are convolved to produce the channel feature map, and then a new feature map F\({}^{\prime\prime}\) is obtained after processing using the sigmoid activation function, which multiplies the previous output. The final result, i.e., the features, is obtained by combining the previous feature map with these features.
## 2 Materials and Methods
### Equipment  
To assess the impact force, a force plate was used as the target. It was padded with a training shield to protect the participants from hitting the force plate directly and mounted onto a stable structure (AMTI, model MC12-2K, 2000 series, Watertown, MA, USA). The force plate dimensions were 305 mm \(\times\) 406 mm \(\times\) 79 mm. The force plate was synchronized in time and space to Noraxon (MR 3.18, Scotssdale, AZ, United States). Acceleration data were obtained by mounting a wireless IMU sensor, Ultium, manufactured by Noraxon, which was synchronized with the force plate. The sampling rate of the IMU sensors was 2000 Hz. The sensors were equipped with acceleration-only attachments, allowing for measurements up to 4000 g. The sensor was attached to a side of the foot, near the lateral malleolus.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
###### Abstract  
Dynamic adaptive streaming over HTTP (DASH) is an effective method for improving video streaming's quality of experience (QoE). However, the majority of existing schemes rely on heuristic algorithms, and the learning-based schemes that have recently emerged also have a problem in that their performance deteriorates in a specific environment. In this study, we propose an adaptive streaming scheme that applies online reinforcement learning. When QoE degradation is confirmed, the proposed scheme adapts to changes in the client's environment by upgrading the ABR model while performing video streaming. In order to adapt the adaptive bitrate (ABR) model to a changing network environment while performing video streaming, the neural network model is trained with a state-of-the-art reinforcement learning algorithm. The proposed scheme's performance was evaluated using simulation-based experiments under various network conditions. The experimental results confirmed that the proposed scheme performed better than the existing schemes.  
dynamic adaptive streaming over HTTP (DASH); quality of experience (QoE); reinforcement learning; online learning +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Identification of Potential Predictors and Their Association with CKD  
The Cox hazard model is a model used to predict risk between pairs of subjects within the same cohort, and the area under the ROC curve (AUC) is a graphical representation showing the discriminatory ability of a classification problem. Based on the hazard ratio, \(p\)-value < 0.05, AUC, and domain knowledge, 13 variables (including the metabolic indices) were considered the most significant predictors of CKD, as shown in Table 2. Among the predictors, serum creatinine had the highest discriminatory capacity for CKD with an AUC of 0.909 (95% CI, 0.903-0.915), followed by gender with an AUC of 0.756 (95% CI, 0.745-0.768). Patient demographic data (age and gender) and creatinine were the most important predictors of CKD and are often used in clinical practice to estimate the glomerular filtration rate (GFR) for CKD diagnoses [15]. We compared the association of the metabolic indices with CKD against their original components such as BMI, WC, Triglyceride, Cholesterol, HDL-cholesterol, and LDL-cholesterol. Compared with the original components, our proposed metabolic indices (including TgG-Index, TG/HDL, and VAI) were more associated with CKD risk. However, the association of the TgG-Index, TG-HDL, and VAI with CKD can also differ by gender due to differences in lipid decomposition and distribution, which change according to age. The current findings were also similar to those