esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sampled Apodized Grating Waveguide Structure and Design  
### Design Principle of Sampled Apodized Waveguide Grating  
Our designed sampled grating has a principle that the sampled center wavelength (\(\lambda_{0}\)), the 3-dB reflection bandwidth (\(\Delta\lambda_{b}\)) and the wavelength spacing (\(\Delta\lambda_{s}\)) between adjacent sampled reflection lines are 1550 nm, 5 nm and 10 nm, respectively. Meanwhile, it should have a side-lobe suppression ratio of more than 20 dB. This device has a similar work principle to coarse WDM (CWDM) [29]. The wide bandwidth and wavelength spacing attributes to satisfy the transmission need amongst all end users, and it can also be utilized to realize downstream and upstream traffic. Such a work contributes to keeping the cost of the access network low and economically feasible for subscribers. In addition, \(\Delta\lambda_{s}\) is twice of \(\Delta\lambda_{b}\), which means this device can filter half of the transmission signals in a broad spectrum, and the broad spectrum can be filtered by cascading a sampled grating with a different period. Half of the signal reaches to one area and remaining half signal flows to the other area, which realizes signal separation. This contributes to realizing the signal collection of different usage. Furthermore, \(\Delta\lambda_{b}\) is approximately six times 0.8 nm. It can realize wavelength division multiplexing if narrow band signals are needed. Finally, as the bandwidth increases, the side-lobes of the reflection spectrum also increase. This will extract unwanted transmission signals and increase the crosstalk of device. This work enables the simultaneous implementation of large bandwidth and high selectivity for signals.
## 3 Results
### Statistical Analysis Results
#### 3.2.2 Game Performance  
The scores of the subjects were recorded and tallied after each game session. For the first game, the total score is 25 points, with each category having different number of items. There is one point for each of the correct answer, and no points were awarded for wrong answers. For the second and third games, their scores are based on the hit ratio and reaction time. Table 6 shows the scores, hit ratios and reaction times of the subjects, categorized by  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Session**} & \multicolumn{3}{c}{**Interaction**} \\  & & & & & **(Group \(\times\) Session)** \\ \hline  & \(F\) & \(p\)**-Value** & \(F\) & \(p\)**-Value** & \(F\) & \(p\)**-Value** \\ \hline HR & 0.3465 & 0.5609 & 46.4722 & 0.0000 * & 0.6184 & 0.6099 \\ RRI & 0.2623 & 0.6125 & 50.2898 & 0.0000 * & 0.6073 & 0.6168 \\ SDNN & 0.0597 & 0.8087 & 1.7519 & 0.1849 & 0.5464 & 0.6556 \\ pNN50 & 0.4471 & 0.5105 & 0.9443 & 0.4374 & 0.1001 & 0.9591 \\ RMSSD & 0.1383 & 0.7127 & 3.4800 & 0.0319 * & 0.2237 & 0.8790 \\ TP & 0.0791 & 0.7804 & 1.7584 & 0.1854 & 0.8876 & 0.4634 \\ LF & 0.4417 & 0.5116 & 0.9289 & 0.4426 & 0.2199 & 0.8816 \\ HF & 0.1752 & 0.6786 & 4.3286 & 0.0143 * & 0.8641 & 0.4733 \\ LF/HF & 0.0732 & 0.7887 & 7.7473 & 0.0009 * & 0.7757 & 0.5189 \\ SampEn & 0.5227 & 0.4753 & 2.2279 & 0.1124 & 1.2333 & 0.3206 \\ \hline \hline \end{tabular}  
* \(p<0.05\).  
\end{table}
Table 4: Effects of group and session on ECG features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Post-Hoc Analysis** \\ \hline HR & (2) \(>\) (1); (2) \(>\) (3); (2) \(>\) (4) \\ RRI & (2) \(<\) (1); (2) \(<\) (3); (2) \(<\) (4) \\ RMSSD & (2) \(<\) (1) \\ HF & (2) \(<\) (1); (2) \(<\) (3); (2) \(<\) (4); (4) \(<\) (1) \\ LF/HF & (2) \(>\) (1); (2) \(>\) (3); (4) \(>\) (1); (4) \(>\) (3) \\ \hline \hline \end{tabular} (1)—Rest 1; (2)—Game 1; (3)—Rest 2; (4)—Games 2&3.  
\end{table}
Table 5: Post hoc comparison for features with significant differences on the main effects.  
their cognitive status. It also shows the statistical analysis for the two groups based on their game performance.  
From the statistical results, there was a significant difference between the two groups in terms of their scores from Game 1, hit ratios and reaction times from Games 2 and 3. The group with MCI have significantly lower scores and reaction times compared to the cognitively normal group. These results are similar to a previous study [28], making the serious games a possible evaluation for cognitive impairment among elderly people.
## 3 Experiments and Comparison
### Qualitative Analysis of the Correction Effect of the Algorithms
It can be seen that [PERSON] [16], [PERSON] [17], and [PERSON] [18] have certain effects on horizontal stripe, but they all blur some texture details of the image. [PERSON] [18] has the best nonuniformity correction result on the smooth image, but the bluriest image texture details. [PERSON] [17] and [PERSON] [16] have an average effect on horizontal strip removal, although their harm to picture detail texture is not as severe as [PERSON]'s [18]. The damage to image texture details caused by the algorithms of [PERSON] [16], [PERSON] [17], and [PERSON] [18] is mainly due to the fact that they all process the image's horizontal high-frequency information after wavelet transform, and the algorithms eliminate the stripes while also destroying the part of the image with the high-frequency information. In contrast, the algorithm presented in this paper linearly corrects the image in the spatial domain and does not process the image in the frequency domain. Consequently, it destroys fewer image texture details and outperforms the algorithms of [PERSON] [16], [PERSON] [17], and [PERSON] [18] in terms of the nonuniformity correction effect. Finally, in order to better verify the nonuniformity correction effect of this algorithm, real infrared images containing non-uniform strips generated by long-wave infrared focal plane linear array scan detectors are used for experiments to compare the effect of each algorithm. Since the number of columns of a frame of infrared focal plane linear array scan image is about 50,000, in order to show the image's denoising effect, this paper intercepts the scanning image with an image resolution of 3053 \(\times\) 4096. The algorithm in this paper uses 1500 columns of scan images to calculate the linear correction coefficients through guided filtering. The size of the smoothing guided filtering window is 12 \(\times\) 1. The size of the guided filtering window for horizontal stripe extraction is 1 \(\times\) 100, and guided filtering parameters \(\varepsilon\) are 0.16. The algorithms of [PERSON] [16], [PERSON] [17], and [PERSON] [18] all correct the 3053 \(\times\) 4096 images directly. The real scan image in Figure 9 shows that the scene is the sky, and the horizontal stripes caused by nonuniformity are plainly visible in the clouds. The effects of the [PERSON] [16], [PERSON] [17], and [PERSON] [18] algorithms are not satisfactory in the high-resolution scan image, which contains a large number of horizontal stripes. The correction impact of the proposed algorithm is clearly superior to all others, and the horizontal stripes in the high-resolution cloud image are effectively erased.
# VoI-Based Optimization of Structural Assessment for Spatially Degrading RC Structures
## 3 Practical Implementation of the Calculation of the VoI  
### Approximating the Posterior Distribution  
In Section 2 and Figure 1, it is indicated that the prior distributions \(f^{\prime}\) of the model parameters are updated to posterior distributions \(f^{\prime\prime}\) based on the available monitoring data. For this purpose, Markov Chain Monte Carlo (MCMC) sampling is generally used. However, the application of MCMC requires a considerable computational effort. When the posterior distribution has to be derived for different possible monitoring outcomes, as is the case when calculating the VoI, this can lead to a very high computational burden. To overcome this issue, approximate methods can be used to estimate the posterior distributions. Since for the calculation of the VoI the sign of the resulting VoI and the relative difference between different monitoring alternatives are most important, an approximation that leads to an error that is small compared to this difference in VoI is justified.  
According to [22; 23], when a large amount of data is available, the posterior Probability Density Function (PDF) can be asymptotically approximated by a normal distribution, centered around the Maximum A Posteriori (MAP) point and with a covariance matrix \(\hat{\mathbf{\Sigma}}_{po}\). For a set of parameters \(\mathbf{\theta}\) to be estimated, with prior probability \(f^{\prime}(\mathbf{\theta})\), the MAP estimate is given by Equation (1) [22; 23].  
\[\hat{\mathbf{\vartheta}}^{MAP}=\underset{\mathbf{\theta}}{\text{argmin}}\big{(}F_{MAP }\big{)}\;with\;F_{MAP}=-\log(L(\mathbf{\vartheta}|\mathbf{y}))-\log\big{(}f^{\prime}( \mathbf{\theta})\big{)}=F_{ML}+F_{MAP_{F}} \tag{1}\]  
Here, \(L(\mathbf{\vartheta}|\mathbf{y})\) is the likelihood function accounting for data \(\mathbf{y}\). When this likelihood function is Gaussian, the first term \(F_{ML}\) corresponds to a generalized least squares objective function. The term \(F_{MAP_{F}}\) corresponds to a regularization term based on the available prior information.  
The approximate posterior covariance matrix \(\hat{\mathbf{\Sigma}}_{po}\) is computed as the inverse Hessian of the MAP objective function \(F_{MAP}\), evaluated at the MAP point according to Equation (2).  
\[\hat{\mathbf{\Sigma}}_{po}^{-1}=\
abla_{\mathbf{\theta}}^{2}F_{MAP}|_{\mathbf{\theta}= \hat{\mathbf{\vartheta}}^{MAP}} \tag{2}\]  
In many optimization algorithms, this Hessian is computed as a by-product in the solution of the optimization problem to solve for the MAP point.
## 3 Materials and Methods
### Data Analysis Approach
#### 3.5.5 Training of Machine Learning Models  
The machine learning models, including Support Vector Machine (SVM), Random Forests, XGBoost, and XGBoost (version 0.4.2), were trained to predict team performance based on the various features extracted from the dataset [86; 87; 88; 89; 90; 91]. XGBoost and XGBoost are libraries for gradient boosting, a technique that builds predictive models by combining multiple weak learners to improve accuracy and handle structured data efficiently. Random Forest is another ensemble learning method that operates by constructing multiple decision trees during training and outputting the mean prediction of the individual trees for regression tasks. On the other hand, SVM is a non-probabilistic binary linear classifier that constructs a hyperplane in a high-dimensional space, which can be used for classification or regression tasks. Additionally, K-Means clustering, an un-supervised machine learning  
Figure 4: Visualization of the performance slope calculation (example).  
algorithm that groups data points into a predetermined number of clusters based on similarity, was employed to identify potential hidden patterns or groupings within the data [92]. Due to the continuous nature of our target variable, which represents team performance, this was framed as a regression problem. Hyperparameter tuning for each model was conducted using both randomized and grid search techniques. An 80/20 train-test split strategy was employed to ensure robust model evaluation.  
The models were evaluated based on several key performance metrics, including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R\({}^{2}\). The MAE and RMSE provide measures of the average magnitude of prediction errors, while the R\({}^{2}\) represents the proportion of variance in the target variable explained by the model [91].
## 4 Identity Management and Access Control Based on Blockchain under Edge Computing for IIoT
### Lightweight Key Exchange Based on Self-Certified Public Key
Without the Ephemeral-Key Reveal(.) query, cannot get information about (or) because (or) is only used in one session. The probability of success for \(S\) is the following:_  
\[Pr(S)\geq\frac{2P_{2}(\lambda)}{(s(\lambda)^{2}h_{2}(\lambda))}\]  
_where \(P_{2}(\lambda)\) is the probability of occurrence of Case 2. (2)._  
**Case 3. \(S\) _randomly selects two participants \(\hat{A}\), \(\hat{B}\) and one session sid. \(S\) sets \(B_{1}=V\) and \(B_{2}=\frac{sP}{rV}\) as the long-term public key of \(\hat{B}\), and the corresponding long-term private key that \(S\) does not know. \(S\) randomly assigned for the long-term private key of the remaining \(n(\lambda)-1\) party. With at least \(\frac{1}{n(\lambda)}^{2}\) probability, \(M\) selects \(\hat{A}\) and \(\hat{B}\), respectively, as the owner and counterpart of the session sid, at the same time, selecting sid as the test session. When \(S\) has the long-term private key of the participant, which is activated by \(M\), \(S\) can simulate  
may choose to store these attributes on the blockchain, and use the identity information stored on the blockchain to identify the correct network terminal. Using attribute-based credentials allows users to display only the credentials they choose. Data hash table is often used in existing access control schemes. However, Bloom filters have the advantage of space and time, and their space advantage is that their volume does not increase with increasing elements. The time advantage is that it does not rely on the loop structure to determine if an element is in the collection [43]. Thus, the bloom filter is utilized in our scheme. The details are shown in the Figure 6.  
In our system, the blockchain network is utilized to represent the transfer permissions of access resources from one edge center to another. Specifically, we recommend storing the representation of the right to access resources in the blockchain, allowing this right to be managed through the transaction among the edge center in the blockchain network. The main advantages of the method are as follows. The access rights of resources are initially defined by the owners of resources (manufacturing terminals) and are stored in the blockchain through policy creation transactions (PCT) of blockchain. Access rights to resources can easily be transferred from one edge center to another without the intervention of the edge network terminals. Therefore, any edge center can check the operator's authority. The access right can be transferred from the current holder (such as Oi) to another Oj through right transfer transaction (RTT) in the blockchain. RTT is created by Oi, so no intervention of network terminals is required in any transfer of rights. When passing rights through RTT, Oi can only modify the variable conditions that regulate their rights by restricting them. In addition, policy updates can be done through policy update transaction (PUT). Because all transactions have timestamps, these changes are obvious and traceable.
## 3 Common Latitude Power Series Expansions with Geodetic Latitude as Variable
### Power Series Expansion of Geocentric Latitude with Geodetic Latitude as Variable  
According to the geodesy theory, the geocentric latitude \(\phi(B)\) is related to the geodetic latitude \(B\) by  
\[\tan\phi=\left(1-e^{2}\right)\tan B. \tag{15}\]  
The power series expansion of the reduced latitude with geodetic latitude as the variable is  
\[\phi(B)=B+n_{2}\sin 2B+n_{4}\sin 4B+n_{6}\sin 6B+n_{8}\sin 8B+n_{10}\sin 10B. \tag{16}\]  
The coefficients in Equation (16) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 5.  
In sum, compared with the common latitude expansions based on eccentricity \(e\), when the common latitude is expanded based on the third flattening \(n\), the coefficients of the power series expansions of common latitude have fewer digits and are more concise. In particular, the coefficients of the rectifying latitude, geocentric latitude, and reduced latitude are greatly simplified and the number of terms is almost halved when expanded in a power series based on the third flattening \(n\), which increases the efficiency of the calculation and allows the relationship between different latitudes to be expressed more intuitively.
## 1 Introduction
### Description of the AztechSat-1 Nanosatellite  
The project name, AztechSat-1, pays homage to the Aztec culture and represents the technological challenge it embodies. The AztechSat-1 is a nanosatellite belonging to the 1U CubeSat type category. It was developed by a multidisciplinary group of students from the Universidad Popular Autonoma del Estado de Puebla (UPAEP) in the state of Puebla, Mexico, in collaboration with the Mexican Space Agency (AEM) and the National Aeronautics and Space Administration (NASA). The satellite was launched on 5 December 2019, aboard a Falcon 9 launcher from Cape Canaversal and deployed from the International Space Station (IEE) using the NanoRacks CubeSat Deployer (NRCSD) on 19 February 2020, as shown in Figure 1. It remained operational until its re-entry on 10 December 2021.  
Figure 1: AztechSat-1 deployed from the ISS on 19 February 2020 [14].  
The primary objective of AztechSat-1, an academic nanosatellite, was to demonstrate communication between a nanosatellite and the GlobalStar constellation at a frequency within the L band (1611.25-1618.725 MHz), aiming to improve data transmission to Earth. Additionally, it aimed to promote amateur radio activities using ultra-high frequency (UHF) frequencies of 435 MHz for upload and 437 MHz for download. Figure 2 shows the mission operation diagram.  
The AztechSat-1 nanosatellite is composed of an aluminum structure, an electrical power system (EPS), an on-board computer (OBC), an attitude determination and control system (ADCS), and a radio system for communication (COM) with the ground station in the amateur UHF frequency band. It also includes a communication module for inter-satellite transmission in the L band. The system is equipped with a module that integrates the computer and the radio, as well as an interconnection panel for multipurpose boards. Additionally, it features an omnidirectional antenna system for communication with the ground station and is equipped with a set of 5 solar panels to provide power.  
While the system incorporates commercial off-the-shelf (COTS) modules, the payload was specifically designed and manufactured to fulfill the mission objectives. Following its deployment, the nanosatellite powered on and initiated a countdown before transmitting its first packet of data, approximately 40 min later. Subsequently, the antennas were deployed, and the nanosatellite commenced its position control. Initially, the spacecraft was launched at an altitude of approximately 400 km with an inclination angle of 51.6\({}^{\circ}\). The AztechSat-1 orbit simulation is described in Figure 3.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 5 Mission Process Control Strategy
### Control Strategy Solution
#### 5.2.4 Solving of Control Strategy
The \(m_{j}\) has two opportunities to reduce the demand \(N_{m_{i}^{*}}(t_{c})\) after taking the demand, the first one is to reduce the demand by utilizing the redundancy time difference between \(m_{j}\) and \(m_{i}^{*}\) and the second one is to reduce the demand by utilizing the amount of compression of the own mission duration. After two demand reductions by \(m_{j}\) to the demand \(N_{m_{i}^{*}}(t_{c})\), if the demand \(N_{m_{i}^{*}}(t_{c})\) still exists, \(m_{j}\) needs to continue to sell the remaining demand to its own prior-mission until the demand is satisfied and the recursion ends. The recursive process of Algorithm 6 is shown in Figure 15. In Figure 15, the mission \(m_{4}\) serves as a seller, and the demand \(N_{m_{4}}(t_{c})\) is the mission's plan start time advance. Assume that missions \(m_{1}\) and \(m_{3}\) are controllable and the remaining missions are uncontrollable. Since both \(m_{2}\) and \(m_{3}\) are prior-missions to \(m_{4}\), \(m_{4}\) sells orders to \(m_{2}\) and \(m_{3}\). As shown in Figure 15b, for the buyer \(m_{3}\), the first reduction of the demand is made first, and the demand can be reduced once when node 6 and node 5 overlap. For mission \(m_{2}\), since there is no redundant time difference between it and mission \(m_{4}\), and it is an uncontrollable mission itself, it cannot satisfy the demand and must continue to sell to its own prior-mission \(m_{1}\). As shown in Figure 15c, after \(m_{2}\) sells to \(m_{1}\), \(m_{1}\) can satisfy the demand by compressing the mission duration, which generates the control quantity \(u_{m_{1}}(t_{c})\). After \(m_{4}\) sells to \(m_{3}\), based on the first reduction of the demand, \(m_{3}\) can reduce the demand for the second time by compressing the mission duration and can fully satisfy the demand. At this time, it generates the control quantity \(u_{m_{3}}(t_{c})\). The control quantity that satisfies the demand can be obtained by continuous forward recursion of the sale algorithm until the demand is satisfied. In summary, as long as the sale algorithm is invoked for each demanded mission in the critical path, the control quantity that satisfies the respective demand can be obtained, and the final system control quantity \(\mathbf{u}(t)\) can be obtained after all the control quantities are combined. At this point, we have finished solving the control quantity, and the whole control strategy is shown in Figure 16. The control strategy can be embedded in the control system shown in Figure 10 to realize the real-time control of the mission process and ensure that the mission is executed as planned. Figure 16: Diagram of control strategy. Figure 15: Diagram of the recursive process of the sale algorithm. (**a**) shows the missio \(m_{4}\) serves as a seller. (**b**) shows the first reduction of the demand is made first for the buyer \(m_{3}\). (**c**) shows \(m_{1}\) can satisfy the demand by compressing the mission duration after \(m_{2}\) sells to \(m_{1}\).