esa-sceva/satcom-chunk-collection huggingface dataset:
# SC-SA: Byte-Oriented Lightweight Stream Ciphers Based on S-Box Substitution
## 2 SC-SA Cipher
### Avalanche Effect Analysis  
Avalanche effect testing is used in cryptographic testing to evaluate the confusion properties of encryption algorithms. A good encryption algorithm should have the characteristic that a small change in the input plaintext (such as changing one bit) will lead to a significant change in the output ciphertext. By verifying the sensitivity and randomness of the cryptographic algorithm, the security of the encryption is ensured.  
Using 10,000 random keys to generate 10,000 10 MB key sequences, the smaller the dataset tested, the greater the test error. A 10 MB key sequence can meet the encryption needs of most IoT devices. When one bit in the encryption key is randomly modified and the modified key is used to generate the corresponding 10,000 10 MB key sequences, the number of changed bits in these 10,000 pairs of sequence streams is calculated. Avalanche test flowchart are shown in Figure 8. The results are shown in Table 2. According to the table, it can be seen that the vast majority achieve a change rate of 49.5% to 50.5%, indicating the excellent avalanche effect of SC-SA.  
Figure 8: Avalanche test flowchart.  
Figure 7: Probability of swapped positions.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 4 Proposed Methodology
### Reward Function  
Our objective is to maximize the cache hit ratio. Thus, we use cache hits as the agent's reward, denoted as \(\mathcal{R}_{t_{l}}=\{r_{t_{l}}^{n_{1}},\ldots,r_{t_{l}}^{n_{N}}\}\), which includes the cache hits of each node. For a node \(n_{k}\) at time step \(t_{l}\), its cache hits for each content item are \(r_{t_{l}}^{n_{k}}=\{r_{t_{l}}^{c_{1},n_{k}},\ldots,r_{t_{l}}^{c_{C},n_{k}}\}\). Let us assume that a node \(n_{k}\)'s reward sum for all content at time step \(t_{l}\) is \(cacheHits_{t_{l}}^{n_{k}}=r_{t_{l}}^{c_{1},n_{k}}+r_{t_{l}}^{c_{2},n_{k}}+ \cdots+r_{t_{l}}^{c_{C},n_{k}}\); then, the objective function can be formulated as follows:  
\[\max \sum_{n_{k}\in\mathcal{N}}\sum_{t_{l}\in\mathcal{T}}cacheHits_{t _{l}}^{n_{k}}\] (6) s.t. \[\sum_{c_{i}\in\mathcal{C}}b_{t_{l}}^{c_{i},n_{k}}\leq\begin{cases} z,&\text{node $n_{k}$ has the caching capability}\\ 0,&\text{otherwise}\end{cases},\forall t_{l}\in\mathcal{T},\forall n_{k}\in \mathcal{N}\]  
this objective aims to maximize the cache hit ratio for the stored content while ensuring that the number of content items stored in a node does not exceed \(z\) if it is a router node with caching capability and zero otherwise. We apply this constraint because only router nodes with caching capability can cache content, while other nodes, such as source and receiver nodes, can only distribute or receive content.
## 5 Numerical Examples
### Comparative Experiments
#### 5.2.2 Experiment 2  
In Experiment 2, we analyze the influence of the variance of variables by setting \(\sigma_{f}^{2}=16n\), \(\sigma_{0}^{2}=4n\), \(\sigma_{\mathrm{s}}^{2}=36n\), \(n=1,\ 10,\ 100\). The variances of variables \(T_{ij}\) and \(S_{i}\) increase with the value of \(n\). All the other parameters are the same as Experiment 1.  
We employ the VNS algorithm in Section 4 to solve this problem. This algorithm calculates the number of participating AMRs, travel distance, and total cost. Table 9 demonstrates that when \(n=1\) or \(10\), only two AMRs are involved in the service. However, for \(n=100\), four AMRs are required. The increasing variance of service and travel times results in a higher probability of violating time windows. To avoid the violation of time windows, increasing the number of AMRs is an effective approach. Consequently, the total cost rises from 71.9 to 131.8 due to the increased cost of additional AMRs.  
This discovery indicates that the optimal number of AMRs depends on the numerical characteristics of service and travel time, and the system can better adapt to the randomness of service and travel time by allocating more AMRs. Correspondingly, the probability of violating time windows is reduced. By understanding how variables affect system performance, decision-makers can make wise choices to improve operational efficiency and minimize interference caused by time window violations.
## 2 Materials and Methods
### NDVI Time Series Analysis: Fourier Modeling and Fitting  
To compare NDVI\({}_{\text{is}}\) with NDVI\({}_{\text{MD}}\) data at an 8-day time resolution, 8-day NDVI\({}_{\text{is}}\) time series were derived from daily in situ NDVI\({}_{\text{is}}\) time series, where days corresponding with the optimal MOD09G1 data were extracted and modeled using Fourier time series software in FORTRAN 77 (see Figure 1 for an example).  
A Fourier series expansion equation was used as a data fitting model for the NDVI\({}_{\text{is}}\) and NDVI\({}_{\text{MD}}\) 1 year time series to allow formalization of seasonal dynamics, where optimal fit was obtained when the root mean square error (RMSE) for the number of harmonics used in the model was minimal. The Fourier series expansion was defined as:  
\[NDVI(t)=\frac{a_{0}}{2}+\sum_{n=1}^{\infty}\biggl{\{}a_{n}\cos\biggl{(}\frac{n \pi t}{L}\biggr{)}+b_{n}\sin\biggl{(}\frac{n\pi t}{L}\biggr{)}\biggr{\}} \tag{5}\]  
with  
\[a_{n}=\frac{1}{L}\int_{-L}^{L}NDVI(t)\cos\biggl{(}\frac{n\pi t}{L}\biggr{)}dt \text{ for }n=0,\ 1,\ 2,\ 3,\ \ldots\infty \tag{6}\]  
and  
\[b_{n}=\frac{1}{L}\int_{-L}^{L}NDVI(t)\sin\biggl{(}\frac{n\pi t}{L}\biggr{)}dt \text{ for }n=1,\ 2,\ 3,\ 4,\ \ldots\infty \tag{7}\]  
Figure 1: Example of output from an in situ normalized difference vegetation index (NDVI\({}_{\text{is}}\)) Fourier time series (Equation (5)) for a deciduous forest site (Missouri Ozark site, USA). (**a**) NDVI\({}_{\text{is}}\) data (blue circles) and NDVI\({}_{\text{is}}\) fit (red line), (**b**) first derivative of the NDVI\({}_{\text{is}}\) time series, (**c**) integral over one year of the NDVI\({}_{\text{is}}\) time series, and (**d**) histogram of the normalized residuals of the NDVI\({}_{\text{is}}\) fit of NDVI\({}_{\text{is}}\) time series data points.  
The first derivative and integral over the NDVI time series, which were obtained using Equations (8) and (9), represent the analytical expression in Equation (5).  
\[\frac{dNDVI(t)}{dt}=\sum_{n=1}^{\infty}\frac{n\pi}{L}\Big{\{}b_{n}\cos\Big{(} \frac{n\pi t}{L}\Big{)}+a_{n}\sin\Big{(}\frac{n\pi t}{L}\Big{)}\Big{\}} \tag{8}\]  
\[\int_{n=1}^{\infty}NDVI(t).dt=\frac{a_{0}t}{2}+\sum_{n=1}^{\infty}\Big{\{} \frac{L}{n\pi}(a_{n}\sin\Big{(}\frac{n\pi t}{L}\Big{)}+b_{n}\cos\Big{(}\frac{n \pi t}{L}\Big{)})\Big{\}} \tag{9}\]  
where \(\mathrm{a}_{0}\), \(\mathrm{a}_{1}\), \(\mathrm{a}_{2}\) are the Fourier series coefficients; \(L\) is the number of Fourier series harmonics; \(t\) is time (Day Of Year, DOY); and \(n\) is the number of Fourier terms.  
The use of five harmonics led to a Gaussian frequency distribution of residuals, where the standard deviation (SD) is at its minimum with extreme number of harmonics (\(L\)). In the optimization of fit, SDs were determined by fitting a normalized Gaussian model to the normalized frequency distribution of residuals (Figure 1d).
## 3 Results and Discussion
### Composition and Morphology of MAO Layer
Under other similar conditions when the concentration of sodium silicate is higher than 12 g L\({}^{-1}\), the growth rate of the film becomes extremely fast, resulting in the abrupt change of the film roughness and thickness. The thickness reaches 28 \(\upmu\)m, while the roughness becomes 7 \(\upmu\)m.  
In order to find the abrupt change point of the relationship between the concentration of sodium silicate and the roughness and thickness of the MAO layer more accurately, MAO layers were prepared at 11, 12, 13, 14 and 15 g L\({}^{-1}\) sodium silicate concentrations. It can be seen from Figure 4 that 12 to 14 g L\({}^{-1}\) is the abrupt range of the effect of sodium silicate concentration on the roughness and thickness of the MAO layer. Figure 3: Influence of sodium silicate concentration (8–16 g L\({}^{-1}\)) on porosity, thickness, and roughness of the MAO film: (**a**) porosity factor and (**b**) roughness and thickness.
## 2 Data Quality Analysis
### DQA5: Almanac Applicable Period  
Almanac data could be applied for an extended period of time, but they do have applicable period limitations. The DQA5 use a method similar to DQA3 to find the applicable period of almanac data for both systems, and the test data used in DQA5 is over a month. The almanac is to provide rough satellite locations and the satellite Doppler shift information. The acquisition process can be expedited if the initial Doppler shift guess is close to the true value. The Doppler shift is the relative movement between the satellite and receiver. For a fixed GNSS user, the velocity could be assumed to be due to the satellites only. The maximum Doppler shift for a conventional GNSS receiver could be set to be the maximum radial velocity of the satellites. With the assumptions made in our previous research [19], the maximum radial velocity of GPS was about 1427 m/s, and that of BDS was about 1440 m/s. Finally, DQA5 uses the satellite velocity, position, and elevation angle errors to determine the applicable period of almanac data. Table 5 shows the statistical results for the same almanac used for over five weeks. The velocity errors of both systems at the fifth week are significantly larger than the threshold. Additionally, the BDS IGSO satellites have the largest velocity errors than the other satellites, and the BDS IGSO satellite positioning errors are significantly larger than the other satellites. In addition to the satellite velocity and position errors mentioned above, the satellite elevation error has a more serious impact on the GNSS receiver. The BDS IGSO satellite elevation angle error at the fifth week was over 2\({}^{\circ}\), and this 2\({}^{\circ}\) error might influence a GNSS receiver to search proper satellite in view. As shown in Table 5, the GPS almanac data could be applied for more than five weeks. In contrast, the almanac data for BDS IGSO satellite need to be updated more frequently to provide correct information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & \multicolumn{3}{c}{**BDS**} \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Max** & 4982.2 & 4028.6 & 4448.9 & 3463.3 \\
**Mean** & 1537.8 & 1960.9 & 1937 & 1273.9 \\
**Std** & 716.4 & 834.9 & 831.7 & 575.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DQA4 statistical result of satellite position difference between using new almanac and new ephemeris (unit: m).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & \multicolumn{3}{c}{**BDS**} \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Max Vel. Error (m/s)** & 18.71 & 2.75 & 147.73 & 10.50 \\
**Max Pos. Error (m)** & 161,347 & 328,923 & 2,188,705 & 81,091 \\
**Max Ele. Error (\({}^{\circ}\))** & 0.427 & 0.292 & 2.633 & 0.189 \\ \hline \hline \end{tabular}
\end{table}
Table 5: DQA5 satellite velocity (vel.), position (pos.), and elevation angle (ele.) in fifth week.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.3 Prediction Interval Coverage Probability (PICP)  
PICP is an important index that evaluates the reliability of the formulated PIs. It can be expressed as Equation (21)  
\[PICP=\frac{1}{m}\sum_{i=1}^{m}q_{i}, \tag{21}\]  
where \(m\) is the number of observations in the dataset and \(q_{i}\) is defined in Equation (22):  
\[q_{i}=\begin{cases}1,&\text{if}\quad y_{i}\in Q^{\alpha}(x_{i}),\\ 0&\text{if}\quad y_{i}\
ot\in Q^{\alpha}(x_{i})\end{cases} \tag{22}\]
## 7 Approaches to Prevent and Manage Infections Caused by Biofilms
### Antimicrobial Coatings  
Antimicrobial coatings are crucial for reducing biofilm development in many settings, including medical equipment and healthcare environments. These coatings operate via two principal mechanisms: release-based and non-release techniques [315]. Coatings that release agents progressively discharge antimicrobial substances, like silver ions, antibiotics, or quaternary ammonium compounds (QACs). These compounds impede microbial growth upon contact, delivering a prolonged antimicrobial action that diminishes the probability of biofilm formation. The regulated release system guarantees prolonged effectiveness while reducing the need for frequent surface reapplication [316; 317; 318].  
Conversely, non-release coatings inhibit microbial adherence by altering surface characteristics to resist attachment. These alterations often include hydrophobic, superhydrophobic, or zwitterionic surface designs that impede bacterial adhesion and hinder the early phases of biofilm development. Non-release coatings act as a passive but effective barrier against biofilm formation by focusing on the crucial adhesion phase [319; 320; 321].  
Antimicrobial coatings are especially crucial in medical equipment, where biofilm-related infections pose substantial difficulties. Devices, including catheters, implants, and wound dressings, are susceptible to bacterial colonization, potentially leading to chronic infections and device malfunction. Silver-based coatings have shown effective in producing microbicidal surfaces that prevent pathogen adhesion and proliferation, diminishing biofilm development on urinary catheters and orthopedic implants [322; 323].  
Antibiotic-embedded polymer coatings provide targeted medication delivery, guaranteeing elevated concentrations of antimicrobial medicines at the possible infection site. This targeted administration improves therapy effectiveness while reducing systemic adverse effects. In addition to medical equipment, antimicrobial coatings are used on frequently touched surfaces in hospital environments, including bedrails and doorknobs [324]. These coatings successfully diminish contamination by pathogens such as _S. aureus_ and _P. aeruginosa_, substantially decreasing the risk of nosocomial infections and the development of DSB [324; 325].  
Their durability and broad-spectrum antimicrobial activity, even against MDR, make these coatings highly efficient in maintaining hygienic conditions. By reducing the need for frequent cleaning and dependence on chemical disinfectants, these coatings enhance infection control and reduce maintenance costs and environmental impact. This combination of targeted efficacy, extended protection, and cost-effectiveness positions antimicrobial coatings as critical in combating biofilm-associated challenges in healthcare and other high-risk environments [326; 327].
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 2 Methodology
### Mid-Level Saliency Maps Generation by Bilateral Absorbing Markov Chain
#### 2.3.4 Background-Based Saliency Map via Absorbing Markov Chain  
In this part, we select background-based absorbing nodes based on the above background seed screening mechanism. As is presented in Figure 7a, we mirror edge nodes \(\Omega^{\prime}_{edge}\) and some non-edge background nodes as virtual absorbing nodes, and all nodes in the image as transient nodes. The non-edge background nodes are randomly composed of 50% \(\Omega_{A}\), 50% \(\Omega_{B}\) and 50% \(\Omega_{C}\). The number of absorbing nodes is \(r\). Then, the background-based affinity matrix \(\textbf{W}_{L}^{B}=\left[w_{ij}^{L}\right]_{N\times r}\) can be obtained with Formula (24). Furthermore, the learned transition matrix is defined as  
\[\textbf{P}_{B}=\left[\begin{array}{cc}\textbf{Q}_{B}^{N\times N}&\textbf{R}_ {B}^{N\times r}\\ 0^{r\times N}&\textbf{I}_{B}^{r\times r}\end{array}\right] \tag{25}\]  
where \(\textbf{Q}_{B}^{N\times N}=\textbf{D}_{B}{}^{-1}\textbf{W}_{L}\), \(\textbf{R}_{B}^{N\times r}=\textbf{D}_{B}{}^{-1}\textbf{W}_{L}^{B}\), \(\textbf{D}_{B}\) is the sum of the matrix \(\textbf{D}_{1}\) and \(\textbf{D}_{2}\), \(\textbf{D}_{1}=diag\Big{\{}d_{1}^{\textbf{W}_{L}},d_{2}^{\textbf{W}_{L}}, \ldots,d_{N}^{\textbf{W}_{L}}\Big{\}}\) is the degree matrix of \(\textbf{W}_{L}\), and \(d_{i}^{\textbf{W}_{L}}=\sum\limits_{i=1}^{N}w_{ij}^{L}\). \(\textbf{D}_{2}=diag\Big{\{}d_{1}^{\textbf{W}_{L}},d_{2}^{\textbf{W}_{L}}, \ldots,d_{N}^{\textbf{W}_{L}}\Big{\}}\) is the degree matrix of \(\textbf{W}_{L}^{B}\), and \(d_{i}^{\textbf{W}_{L}}{}^{B}=\sum\limits_{i=1}^{r}w_{ij}^{L}\). According to Formula (11), we can calculate the absorption probability matrix \(\textbf{B}_{B}=\textbf{N}_{B}\textbf{R}_{B}\), where \(\textbf{N}_{B}=\left(\textbf{I}-\textbf{Q}_{B}\right)^{-1}\). Based on the above work, the saliency of the node \(i\) is defined as  
\[S_{bg}(i)=1-\sum\limits_{j=1}^{r}b_{ij} \tag{26}\]  
The background-based saliency map \(S_{bg}\) is shown in Figure 1. Then, we mirror the nodes with the saliency value greater than the threshold \(th\) as the foreground-based absorbing nodes, which is illustrated in Figure 7b. The number of absorbing nodes is \(k\).  
\[th=\Big{(}mean\Big{(}S_{bg}\Big{)}+\max\Big{(}S_{bg}\Big{)}\Big{)}/2 \tag{27}\]
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 3 Experimental Data and Evaluation Indicators
### Experimental Datasets  
To fully investigate the forecasting performance of these model aforementioned in this contribution, the clock bias of the BDS-2 satellites with the first day to the second day of the 737 weeks (16-17 February 2020) released by the International GNSS Monitoring and Assessment System (iGMAS) were selected as experimental data.  
In order to make the research results provide some references for PPP, these data were interpolated into the clock bias of the 30 s intervals before modeling and predicting. There are three types of BDS-2 satellites: the GEO satellites, the IGSO satellites, and the MEO satellites, with a total of 15 satellites, containing 5 GEO satellites, 7 IGSO satellites, and 3 MEO satellites. The clock bias of two satellites with continuous and complete data with no clock jumps and outliers were selected from each type of satellite for the experiments. The selected satellites are C 01 (GEO), C 05 (GEO), C 10 (IGSO), C 16 (IGSO), C 11 (MEO), and C 12 (MEO), respectively, with a total of six satellites. As of February 2020, information about these satellites is listed in Table 1[40].
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 5 Explanation and Evaluation of Results
### Performance of TL Method over Complex Fault Patterns  
A similar explanation could be expected for the improvement in APU diagnostic accuracy from the 2D-CNN TL method, but, as a former section demonstrated, the multiple fault modes brought additional confusion between the cases. Hence, it is worth analysing how well the TL method works on the complex fault patterns for APU multiple fault modes.  
The scenario where 34 cases existed in the training set was taken as the scenario of interest, as this was where the accuracy of the 2D-CNN TL solution with ImageNet first reached above 75%. The accuracy for all testing cases, as well as the separate accuracy for the single fault cases and multiple fault cases for the TL method and non-TL baseline, is summarised in Table 5. From Table 5, it is clear that, firstly, multiple fault cases were much more challenging than single fault cases since the accuracy over multiple fault cases was considerably lower than the single faults. Secondly, the TL solution based on 2D-CNN improved the accuracy over both single fault cases and multiple fault cases compared to the non-TL baseline. To determine how the TL method makes better predictions for the multiple fault modes, the predictions from the TL method and the non-TL method were studied for each individual case, and the ones where only the TL method could predict correctly were focused on. These include several cases of the fault mode F11 that the non-TL method wrongly classified as F12, while the TL baseline predicted them correctly. Since the F12 cases have only one more faulty component, it is remarkably similar to the F11 cases. Figure 16 plots a pair of F11 and F12 cases in which the fault profiles are very similar. The similarity of the F11 case with the F12 case numerically causes misclassification for the non-TL baseline. However, Grad-CAM visualisation shows that despite the similarity in the general pattern, the transferred 2D-CNN shows different concentration regions around the minor pattern differences between the two cases, which suggests that it can detect the two cases as having different patterns. Hence, the application of the TL solution based on 2D-CNN has a better capacity over complex fault patterns, owing to the possibility of detecting minute pattern differences.  
Although the behaviour of the 2D-CNN TL method to detect minute pattern differences cannot always be expected, the demonstration of such behaviour in both ECS (discussed in Section 5.2) and APU proves that it has an advantage over the non-TL method and can provide an explanation for the accuracy of the improvement.  
Figure 15: Plot of the misclassified case in the testing set with the confusing case in the training set and same-label case in the training set.
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 2 Materials and Methods
### Multi-Point Hemispherical Grid Model for Multi-GNSS Multipath Correction
Then, \(v_{p}^{s}\) could be express by the parameters of the grid points, as follows [26]:  
\[v_{p}^{s}=\alpha_{1}Q_{p1}^{s}+\alpha_{2}Q_{p2}^{s}+\alpha_{3}Q_{p3}^{s}+\alpha_ {4}Q_{p4}^{s} \tag{4}\]  
where \(\alpha_{1}\sim\alpha_{4}\) is the interpolation coefficient obtained using a bilinear interpolation method when four grid points are involved [27], and \(\alpha_{1}\sim\alpha_{3}\) can be obtained by using a trigonometric interpolation method when three grids are involved [28; 29]. The residuals of each satellite on each station can be expressed using the grid point parameters involved, and the double-differenced residual \(v_{pq}^{st}\) can be expressed as follows:  
\[v_{pq}^{st}=\left(v_{p}^{s}-v_{p}^{t}\right)-\left(v_{q}^{s}-v_{q}^{t}\right), \tag{5}\]  
Then, the normal equation corresponding to the parameters of the grid points that need to be estimated can be constructed using (4) and (5). In addition, the theoretical maximum value of the multipath error is 1/4 cycle of the wavelength according to (3) for the grid point parameter \(Q_{1}\), so the following virtual observation equation can be added to each grid point parameter. \[Q_{1}=0 \tag{6}\]  
Because there is a certain correlation between the surrounding environment of the station corresponding to the adjacent grid point., if the grid point parameters \(Q_{1}\) and \(Q_{2}\) are adjacent to each other, the virtual observation equation can be added according to their correlation. \[Q_{1}-Q_{2}=0, \tag{7}\]  
The above two kinds of virtual observation equations of (6) and (7) can further improve the accuracy and reliability of the modeling results. First, the double-differenced residuals with ambiguity-fixed solutions are added to the parameter solution and establish the observation equation according to the weight of the precision of the carrier phase. Then, the above two types of constraints for each grid point and neighboring grid points are added, which can be established according to the weight of 1/4 cycle of wavelength and empirical threshold of the multipath error between the grid points. Finally, the parameters of MHGM for each station are calculated as a whole in the estimator. Based on this principle, the established model is an integrated model for multi-GNSS, without considering the diversity of the different systems and constellations.