esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Parametric Double S-Shaped Functions
### Hyperbolic Tangent Functions  
The Parametric Double Hyperbolic Tangent Function (PD_HyTg_F) is composed of 2 Parametric Hyperbolic Tangent functions, each characterized by its own set of parameters:  
\[f(x) =p_{0}+f_{1}(x)+f_{2}(x), \tag{108}\] \[f_{1}(x) =p_{1}\left(\left(\tanh(\left(x-p_{2}\right)p_{3}\right)+1.0)/2.0 \right),\] (109) \[f_{2}(x) =p_{4}\left(\left(\tanh(\left(x-p_{5}\right)p_{6}\right)+1.0)/2.0 \right), \tag{110}\]  
where  
* \(f(x)\) is the value of the Parametric Double Hyperbolic Tangent Function at argument \(x\), returned by the IDL function pd_hytg_f,
* \(f_{1}(x)\) is the value of the first Parametric Hyperbolic Tangent Function at argument \(x\), using parameters \(p_{1}\) to \(p_{3}\) and provided as the output positional parameter phtf1, and
* \(f_{2}(x)\) is the value of the second Parametric Hyperbolic Tangent Function at argument \(x\), using parameters \(p_{4}\) to \(p_{6}\) and provided as the output positional parameter phtf2. The 7 PD_HyTg_F model parameters have the following meanings:
* \(p_{0}\) = Base value of \(f(x)\), i.e., asymptotic value when \(x\rightarrow-\infty\).
* \(p_{1}\) = Amplitude of \(f_{1}(x)\) (positive for an increase, negative for a decrease), independently from the base value.
* \(p_{2}\) = Phase shift along the \(x\) axis for the inflection point of \(f_{1}(x)\): a positive (negative) value shifts the function to the right (left) of \(x=0\).
* \(p_{3}\) = Slope of \(f_{1}(x)\) at the inflection point.  
\(p_{4}=\) Amplitude of \(f_{2}(x)\) (positive for an increase, negative for a decrease), independently from the base value. \(p_{5}=\) Phase shift along the \(x\) axis for the inflection point of \(f_{2}(x)\): a positive (negative) value shifts the function to the right (left) of \(x=0\). \(p_{6}=\) Slope of \(f_{2}(x)\) at the inflection point. Note that the current codes expand upon and supersede the model description contained in [102]. The partial derivatives of the PD_HyTg_F model with respect to the 7 parameters are as follows: \[\partial f(x)/\partial p_{i} =\partial p_{0}/\partial p_{i}+\partial f_{1}(x)/\partial p_{i}+ \partial f_{2}(x)/\partial p_{i},\] (109) \[\partial p_{0}/\partial p_{i} =\left\{\begin{array}{ll}1.0&\text{ if }i=0\\ 0.0&\text{ if }i>0\end{array}\right.,\] (110) \[\partial f_{1}(x)/\partial p_{1} =(\tanh((x-p_{2})\;p_{3})+1.0)/2.0,\] (111) \[\partial f_{1}(x)/\partial p_{2} =-(p_{1}\,p_{3})/(2.0\,\cosh^{2}((x-p_{2})\;p_{3})),\] (112) \[\partial f_{1}(x)/\partial p_{3} =(p_{1}\,(x-p_{2}))/(2.0\,\cosh^{2}((x-p_{2})\;p_{3})),\] (113) \[\partial f_{2}(x)/\partial p_{4} =(\tanh((x-p_{5})\;p_{6})+1.0)/2.0,\] (114) \[\partial f_{2}(x)/\partial p_{5} =-(p_{4}\,p_{6})/(2.0\,\cosh^{2}((x-p_{5})\;p_{6})),\] (115) \[\partial f_{2}(x)/\partial p_{6} =(p_{4}\,(x-p_{5}))/(2.0\,\cosh^{2}((x-p_{5})\;p_{6})),\] (116)  
and all other partial derivatives (e.g., \(\partial f_{1}(x)/\partial p_{0}\), \(\partial f_{1}(x)/\partial p_{5}\) or \(\partial f_{2}(x)/\partial p_{3}\)) are null.
## 3 Results
### MGO Content
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Honey Samples** & **Floral Origin** & **Geographical Origin** & **MGO (mg/kg)** \\ \hline  & & **Mean \(\pm\) SD** & **8.49 \(\pm\) 0.35** \\ \hline
71 & & Apulia & 15 \\
72 & & Apulia & 6.6 \\ \hline
73 & & Sardinia & 5.2 \\
74 & & Tuscany & 6.9 \\ \hline
75 & & Tuscany & 14.1 \\ \hline
77 & & Piedmont & 5.8 \\ \hline
78 & & Abruzzo & 6.8 \\ \hline
79 & & Veneto & 15.2 \\ \hline
80 & & Campania & 5.9 \\ \hline  & & **Mean \(\pm\) SD** & **8.87 \(\pm\) 4.12** \\ \hline
81 & & & 7.7 \\
82 & & Apulia & 6.9 \\
83 & & Apulia & 4.7 \\
84 & & & 4.6 \\ \hline
85 & & Basilicata & 4.5 \\
86 & & Basilicata & 2.8 \\ \hline
87 & & Calabria & 0.4 \\
88 & & Calabria & 2.9 \\ \hline
89 & & Sicily & 6.7 \\
90 & & Sicily & 3.9 \\ \hline  & & **Mean \(\pm\) SD** & **4.51 \(\pm\) 2.2** \\ \hline
91 & & Abruzzo & 9.9 \\ \hline
92 & & Apulia & 5.9 \\
93 & & Emilia Romagna & 5.7 \\ \hline
94 & & Emilia Romagna & 5.7 \\ \hline
95 & & Aosta Valley & 6.6 \\
96 & & Aosta Valley & 5.5 \\ \hline
97 & & Trentino Alto Adige & 9.2 \\
98 & & Trentino Alto Adige & 5.7 \\ \hline
99 & & Tuscany & 6.2 \\
100 & & Tuscany & 7.8 \\ \hline  & & **Mean \(\pm\) SD** & **6.84 \(\pm\) 1.58** \\ \hline
101 & & & 18.4 \\
102 & & Apulia & 11 \\
103 & & & 6.5 \\
104 & & & 6.3 \\ \hline
105 & & & 12.9 \\
106 & & Campania & 7.8 \\ \hline
107 & & & 9.9 \\
108 & & Calabria & 10.4 \\ \hline
109 & & Emilia Romagna & 8.8 \\
110 & & & 7.2 \\ \hline  & & **Mean \(\pm\) SD** & **9.92 \(\pm\) 3.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)between 6.3 and 18.4 mg/kg. Cherry and almond honeys showed the highest upper ranges of MGO levels--23 and 24.1 mg/kg, respectively. The statistical analysis is reported in Table 4.  
Citrus honey showed the lowest concentration of MGO (\(P<0.01\)). Cherry and almond honeys, instead, showed the highest concentration of MGO (\(P<0.01\)). Moreover, the concentrations of MGO observed in coriander, honeydew and multifloral honey were lower than detected in cornflower and eucalyptus honey (\(P<0.01\)). To our knowledge, this is the first study of MGO assessment in Italian cherry and almond honeys, which showed higher values of MGO compared to the other honey types analyzed. In agreement with previous studies carried out on different types of honey [37; 48; 49; 50], this study confirms that although the concentrations are much lower than in manuka honey (200-800 mg/kg), MGO is frequently also found in other types of honey. In addition, the presence of MGO in variable concentrations within the range 6.9 to 24.1 mg/kg in cherry and almond floral honey could confirm that this high variability may be linked to the flora involved, to seasonality, to geographical area, as well as to the susceptibility of this parameter to different factors.
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
###### Abstract  
To address the problems that most convolutional neural network-based image defogging algorithm models capture incomplete global feature information and incomplete defogging, this paper proposes an end-to-end convolutional neural network and vision transformer hybrid image defogging algorithm. First, the shallow features of the haze image were extracted by a preprocessing module. Then, a symmetric network structure including a convolutional neural network (CNN) branch and a vision transformer branch was used to capture the local features and global features of the haze image, respectively. The mixed features were fused using convolutional layers to cover the global representation while retaining the local features. Finally, the features obtained by the encoder and decoder were fused to obtain richer feature information. The experimental results show that the proposed defogging algorithm achieved better defogging results in both the uniform and non-uniform haze datasets, solves the problems of dark and distorted colors after image defogging, and the recovered images are more natural for detail processing.  
image dehazing: convolutional neural network; vision transformer; hybrid feature fusion +
Footnote †: journal: Medical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China  
proposed the dark channel prior defogging algorithm (DCP), which solved the atmospheric scattering model based on the prior knowledge that foggy images have a channel pixel value with a low value. This method is simple and effective, but for images containing large bright white areas such as snow, sky, and white buildings, the effect of defogging is not ideal, and the recovered image will produce serious color distortion. Subsequently, [PERSON] et al. [6] used a sky segmentation algorithm to de-fog the sky region to a certain extent to avoid the distortion of the sky region. [PERSON] et al. [7] proposed a defogging algorithm based on boundary constraint and regularization of the transmittance map, which solved the problem of low brightness of the defogging image, but color distortion still occurred in some areas of the defogging image. Although the above traditional image defogging methods have made great progress and shown good results, most of them rely on various prior information. Since these a priori and assumptions are introduced for specific scenes, the quality of the fog-free images obtained when the a priori does not hold is poor, and the features need to be extracted manually, which still has great limitations.
## 3 Results and Discussion
### Influence of Al Powder Concentration on Infrared Emissivity  
Emissivity was the main parameter to assess the stealth influence of the coating in the far infrared band. The influence of the Al powder concentration on infrared emissivity is exhibited in Figure 3, from which we can find that the infrared emissivity value of the coating lessened gradually with the Al  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{
\begin{tabular}{c} **Sum of Squares of** \\ **Deviations** \\ \end{tabular} } & \multirow{2}{*}{**Freedom**} & \multirow{2}{*}{**F Ratio**} & \multicolumn{2}{c}{**F Critical**} & \multirow{2}{*}{**Significance**} \\  & & & & **Value** & \\ \hline Al powder (\%) & 10.240 & 1 & 1.638 & 161.000 & — \\ Silica slurry (\%) & 0.250 & 1 & 0.040 & 161.000 & — \\ UV drying time & 6.250 & 1 & 1.000 & 161.000 & — \\ (min) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: F tests.  
Figure 2: Influence of the Al powder concentration on gloss.  
powder concentration increasing. As the Al powder concentration is augmented from 10.0% to 40.0%, the coating infrared emissivity lessened from 0.649 to 0.083. As the Al powder concentration was 35.0%, the infrared emissivity was 0.128, which was also very low. This was because the augmentation of the Al powder concentration could enhance the reflection of the visible light from the coating, thus weakening the reflection of the invisible light and reducing the infrared emissivity [22].
## 3 Results
### Sensors of HM and HMGT Systems  
Sensors are the linchpins of aircraft health monitoring and management systems, essential to the modern aviation maintenance ecosystem. They enhance safety, underpin reliability, and ensure cost-effectiveness by enabling a transition from reactive fixes to proactive, data-driven strategies.  
The predictive maintenance paradigm, a leap beyond traditional reactive strategies, relies heavily on sensor data. The power of sensors is amplified through their integration with cloud-based technologies and artificial intelligence. AI algorithms, trained on this information, can make predictive assessments, scheduling maintenance proactively to minimize disruption and cost. This integration also facilitates a dynamic feedback loop where data from executed maintenance inform future AI predictions, creating an ever-improving system of health management. Thus, sensors act not only as data collectors but as the foundational elements of an adaptive learning network, crucial to the aircraft's continual operational readiness.  
Sensors for aviation health monitoring and management systems can be classified based on their function, the type of data they collect, their location on the aircraft, and their role in the health monitoring system (Figure 4).  
Figure 4: General taxonomy of aviation sensors.  
Sensors play an indispensable role in the health monitoring and management systems of aircraft. By continuously measuring and collecting data on various parameters, sensors enable the detection of potential issues, predictive maintenance, and overall performance monitoring.
## Appendix B The Evaluation of Forecasting Prediction Performance  
The determination of quantitative measures is crucial for assessing the precision and the reliability of the HML and the ML models. These measures provide a precise evaluation of the correlation between the predicted and the actual values, establishing the capabilities of each prediction model. Using distinct data subsets (training, validation, and independent testing datasets), we computed relative error (Equation (A1)), average relative error (ARE, Equation (A2)), root-mean-square error (RMSE, Equation (A3)), and relative root-mean-square error (RRMSE, Equation (A4)). Additionally, we assessed the performance of the FV HML model by computing the coefficient of determination (R\({}^{2}\), Equation (A5)), the performance index (PI, Equation (A6)), and the a20-index (Equation (A7)) that indicates the percentage of predicted values within \(\pm\)20% error range of actual values (m20). These equations compute the prediction performance metrics for the HML models optimized by GO as follows [26; 58; 59; 60]:  
Relative error (RE):  
\[RE_{i}=\frac{Err_{i}}{FV_{m_{i}}}\] (A1)  
Average relative error (ARE):  
\[ARE=\frac{\sum\limits_{i=1}^{n}RE_{i}}{\eta}\] (A2)Root-mean-square error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(Err_{i})^{2}}\] (A3)  
Relative root-mean-square error (RRMSE):  
\[RRMSE=\frac{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(Err_{i})^{2}}}{FV_{av}}\] (A4)  
Coefficient of determination (R\({}^{2}\)):  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}(Err_{i})^{2}}{\sum\limits_{i=1}^{n}\left( FV_{p_{i}}-\frac{\sum\limits_{i=1}^{n}FV_{m_{i}}}{n}\right)^{2}}\] (A5)  
Performance Index (PI):  
\[PI=\frac{RRMSE}{1+R}\] (A6)  
An index represents the percentage of predictions that fall within the error range of \(\pm\)20% (a20-index):  
\[a20-index=\frac{m20}{n}\] (A7)
## 2 Data and Methods  
### Physical Oceanography and Data Introduction  
Kuroshio is the strongest warm current in the Northwest Pacific (NP) [21; 22; 23]. Due to the difference in intensity and position of Kuroshio in different seasons, the range and intensity of the KEF also show obvious seasonal differences [24; 25]. Kuroshio and its extension have attracted much attention as ocean phenomena [26], but the changes in the underwater acoustic environment caused by Kuroshio and its extension on sound propagation have not been effectively analyzed. In this paper, our study area is selected within the range shown in Figure 1a. The KEF divides the sea area into three water masses, namely, the low temperature and low salinity water mass in the north of the frontal zone, the high temperature and high salinity water mass in the south of the frontal zone, and the frontal transitional water mass between them.  
The data source used in this paper is the daily mean temperature, salinity, and depth data (reanalysis data) output by HYCOM [27,28] (the Hybrid Ocean Model) (since the ocean front in winter is significantly different from that in summer, we used the daily mean data in January, February, July, and August). The type is NetCDF (Network Common Data Format) with a horizontal resolution of 0.08\({}^{\circ}\) and vertical stratification of 40 layers. The speed of sound in seawater changes with temperature, salinity, and depth, and empirical Equations are usually used to express the relationship among them. A simplified empirical Equation was used to calculate the sound speed:  
\[c=1449.2+4.6*T-0.55*T^{2}+0.00029*T^{3}+(1.34-0.01*T)*(S-35)+0.016*D \tag{1}\]  
where \(c\) refers to the speed of sound, \(T\) the temperature, \(S\) the salinity, and \(D\) the depth. This empirical Equation was established by [PERSON] and [PERSON] [29]. As an experimental summary of a large number of sea sound speed measurement data, the empirical Equation has proven the accuracy of the Equation to be used directly. Since the sea depth is generally deep and the seabed terrain is relatively flat in the study area, we uniformly set the sea depth at 5000 m to eliminate the impact of the terrain on the sound propagation. Through the above settings, the 144\({}^{\circ}\) E was selected as the characteristic section (the direction is 38\({}^{\circ}\) N-36\({}^{\circ}\) N) to obtain the sound speed distribution of the KEF in different periods (Figure 2). It was found that due to the difference in time, the position and intensity of the KEF also vary greatly.
# Ultrafast and Wideband Microwave Photonic Frequency-Hopping Systems: A Review
###### Abstract  
The increasing demands to enhance information security in data transmission, providing countermeasures against jamming in military applications, as well as boosting data capacity in mobile and satellite communication, have led to a critical need for high-speed frequency-hopping systems. Conventional electronics-based frequency-hopping systems suffer from low data rate, low hopping speed, and narrow hopping-frequency bandwidth. Unfortunately, those are important aspects to facilitate frequency-hopping in emerging microwave systems. The recent advancement of microwave photonics--the use of light to process microwave signals--provides promising solutions to tackle the challenges faced by electronic frequency-hopping systems. In this paper, the challenges of achieving real-time frequency-hopping systems are examined. The operation principles and results of various microwave photonics-enabled frequency-hopping systems are comprehensively discussed, which have wide hopping-frequency bandwidth and frequency-hopping speed from nanoseconds to tens of picoseconds. Lastly, a bio-inspired jamming-avoidance system that could potentially be used for adaptive frequency-hopping is also introduced.  
microwave photonics; frequency-hopping; photonic signal processing +
Footnote †: journal: Applied Sciences
## 2 Method
### Theoretical Basis of EFSDAF
#### 2.2.1 Endmember Determination and Spectral Unmixing for Landsat Images at \(T_{B}\)  
Endmember extraction and spectral unmixing of Landsat images at \(T_{B}\) are the first step and key step for EFSDAF in this paper due to the heterogeneity of land surface with more than one land cover type in Landsat pixels. In this study, a globally representative spectral linear mixture model (SVD model) [34; 35; 36] shared by Small for Landsat images was selected as endmembers that were classified into three types: substrate (S), vegetation (V), and dark surface (D) [20], and as a linear method, the fully constrained least squares (FCLS) [37] method was applied to the spectral unmixing. The abundance \(A_{F}^{B}\big{(}x_{ij},y_{ij},m\big{)}\) calculated by FCLS varies from 0 to 1, and the sum of \(A_{F}^{B}\big{(}x_{ij},y_{ij},m\big{)}\) is 1.
# Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of _Cannabis_ Species
## 3 Results and Discussion
### Antibacterial Activity of Cannabis
[108] have reported that _Cannabis_ essential oils proved to reduce the virulence of the food contaminant _Listeria monocytogenes_, with a possible application in the food-processing industry. Furthermore, the inhibitory activity of _C. sativa_ seed extracts was observed in _S. aureus_ biofilm formation, which indicates that these extracts could have enormous potential as preservatives in both the food and cosmetics industries [109]. More recently, cannabinoids were found to be more effective in reducing the bacterial colony count in dental plaque when compared with commercial toothpaste such as Oral B and Colgate, which suggests that _C. sativa_-derived compounds, especially CBD, could be used for oral care applications [110]. Therefore, further extensive screening of _Cannabis_ essential oils from different varieties is necessary and should be undertaken to identify the samples with the most promising biological value. [PERSON] et al. [109] assessed the in vitro antimicrobial activity of methanol and n-hexane extracts of _C. sativa_ leaves against _Bacillus cereus_, _B. subtilis_, _E. coli_, _P. aeruginosa_, and _Salmonella_ species. Methanol extract revealed better antibacterial activity than the n-hexane extract. Other, studies discovered that crude alkaloids extracted from _Cannabis_ leaf present antibacterial effectiveness against bacterial strains representative of skin, mouth, and ear microflora and against the \(\beta\) strain of _E. coli_[110].
###### Abstract  
In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance.  
nearest neighbor algorithm; bidirectional long and short-term memory model; traffic flow Article
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 5 Method Analysis
### Domain Gateway Node Selection Algorithm
#### 5.2.3 Feasibility Analysis
This is because whether the message is broadcast from outside the domain to the inside, or from inside the domain to outside, the data packets need to be forwarded by the core-layer nodes to other nodes within and between domains. For example, even if the edge node becomes a domain gateway node, the data packets of transactions and blocks still need to be forwarded by the core-layer node to the edge domain gateway node. Although the core-layer node cannot perceive the specific information of the data packet, it still needs to incur the cost of bandwidth and communication to forward the data packet. At the same time, after the core-layer node becomes a domain gateway node, based on the matching of the multicast tree topology and the underlying network topology, the broadcast delay can be reduced, and the profit from the accounting of intra-domain ICN nodes can be increased by \(1+\tau(t_{1})\). Therefore, by comparing the sizes of the profit functions, it can be concluded that the optimal strategy for core nodes is to compete for the domain gateway node. On the other hand, for edge nodes, if they become domain gateway nodes, not only do they need to incur additional bandwidth and communication expenses \(O\), but also the mismatch between the multicast tree topology, with themselves as the multicast source, and the underlying network topology will result in a greater broadcast delay. This may cause the blocks packaged by themselves to become orphaned and discarded, thereby reducing the profit of accountability brought by becoming a domain gateway node by \(1-\tau(t_{2})\). Since the delay of intra-domain ICN nodes is small, it can be assumed that the profit obtained by the domain gateway node from prioritizing block packaging is less than the additional bandwidth and communication expenses \(O\). Furthermore, when both edge nodes and core nodes compete for the domain gateway node, the probability \(P(c)\) of core nodes becoming the domain gateway node is much greater than the probability \(1-P(c)\) of edge nodes becoming the domain gateway node. Therefore, by comparing the sizes of the profit functions of edge nodes, it can be concluded that the optimal strategy is for edge nodes to not compete for the domain gateway node. Due to the fact that, in this game model, the optimal strategy for any player's strategy set is the strategy combination \((P_{1}\)competing, \(P_{2}\)non-competing), this game model possesses a Nash equilibrium point, which is the optimal strategy combination. Consequently, it can be concluded that the optimal strategy for core-layer nodes is to compete for the domain gateway node, and the optimal strategy for edge nodes is to not compete for the domain gateway node, which ensures the maximization of the profit for both core-layer and edge nodes.