esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelength Selection for Satellite Quantum Key Distribution
###### Abstract  
Current distance limitations of quantum key distribution (QKD) over fibre optic networks suggest that satellite (free-space optical) QKD networks will be required to enable global quantum communications. However, the operational availability of these systems is limited by background noise and strong attenuation caused by turbulence and adverse weather conditions. Using the decoy-state BB84 QKD protocol, we evaluate the secret key rate for a range of wavelengths, receiver sizes and initial beam waists through a variety of atmospheric conditions. We combine filtering techniques, adaptive optics, and wavelength selection to optimize the performance of satellite QKD. This study is simulation-based.  
quantum communications; satellite quantum key distribution; atmospheric turbulence; channel model +
Footnote †: journal: Applied Sciences
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Abbr.** & **References** & **Key Feature** & **Advantage** & **Difficulty** \\ \hline STFT & [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] & TF Resolution & Good for Stationary & Window Sel. \\ \hline WT & [17, 18, 19, 20, 21] & TF Localization & High Resolution & Basis Sel. \\ \hline WVD & [1, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33] & TF Analysis & High Resolution & Cross-term Issue \\ \hline EMD & [34, 36, 37, 38, 39, 40, 41, 42] & Adaptive Decomp. & Self-adaptability & Mode Mixing \\ \hline EEMD & [44, 45, 46, 47, 48, 49] & Noise Addition & Suppress Mixing & Noise Difficulty \\ \hline VMD & [69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89] & Bandwidth Opt. & Flexibility & Parameter Sel. \\ \hline MED & [131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141] & Signal Sparsity & Enhance Impact & Computational \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of signal processing techniques for structural health monitoring (SHM). classifiers. (5) Most of the studies reviewed above focus on detecting one type of fault in certain components (such as gears and bearings) or on a single signal type (such as vibration or acoustic signals). Therefore, it is important to focus on the operation of the entire machine and the processing of multi-sensor data. (6) As observed in the \"Applications\" section of this review, the combination of multiple signal processing models is becoming a trend. Existing technologies include the integration of wavelet transforms and adaptive time-frequency analysis models. Furthermore, the emergence of autoregressive deconvolution highlights the significance of model combination, bringing clarity to physical meanings. (7) Additionally, clarity in physical meaning is an important advancement in signal processing for structural health. Transitioning from data-driven approaches to optimization based on physical meanings not only improves computational accuracy but also provides a more scientific approach for handling large-scale data.
## 2 Materials and Methods
### Modeling Methods
#### 2.3.2 Support Vector Regression  
Support vector regression is an extension of support vector machines. Assuming that we can tolerate a maximum deviation of \(\varepsilon\), we will only calculate the loss if the absolute value of the difference between \(f(x)\) and \(y\) is greater than \(\varepsilon\). It has a strong learning ability for small data sets, and can solve high-dimensional nonlinear problems by transforming them into linear ones through nonlinear transformation [31]. This batch of rice data contains more than 200 pieces of data, and the data volume is suitable for the use of support vector regression. Considering the application effect of each kernel function comprehensively, the Gaussian radial basis kernel function is finally determined to be used in the model established in this paper. The prediction model of support vector regression has two unknown parameters, penalty parameter C and kernel parameter Gamma. C represents the prediction ability of the model. The larger the C, the higher the degree of the model's learning from the samples, and the worse the prediction effect of the unknown data. On the contrary, the smaller C is, the higher the fault-tolerant rate of the model is, and the worse the fitting effect of the model is, but the prediction effect is relatively good. The kernel parameter gamma determines the distribution of the low-dimensional data mapped into the higher-dimensional space.  
SVR model prediction is based on distance measurement and distance is very sensitive to different value ranges between features. Due to the different dimensionality of each feature of the data set, there is a large gap in the data size under different features, which will affect the results of data analysis. In order to eliminate the dimensionless influence among the indicators, data standardization is needed to solve the comparability among the data indicators. After the standardization of the original data, each index is in the same order of magnitude, which is suitable for comprehensive comparative evaluation. In this paper, normalization is adopted to process the data, and the formula is shown in Equation (2).  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{2}\]  
Normalization can make the data map to the range of [0, 1], so as to eliminate the adverse effects caused by the strange sample data.
# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 3 System Model and Problem Description
### Channel Model
#### 3.3.3 Task Offloading and Computing  
The ground device offloads the task to the UAV, then transfers the task block \(S_{i}\) via the UAV to \(leo_{j}\) for processing. Use \(a_{ij}=1\) to indicate that the task can be processed by the satellite \(leo_{j}\) and vice versa to indicate that task \(S_{i}\) is not offloaded to \(leo_{j}\) for processing.  
In the IoT device-UAV channel, the data offloading delay for the IoT device task \(S_{i}\) to offload the task to the UAV includes both transmission delay, propagation delay, transmission delay \(T_{im}^{tran}\) and propagation delay \(T_{im}^{prop}\), which is given by the following equation [35].  
\[T_{im}^{tran}=\frac{S_{i}}{r_{im}},\ T_{im}^{prop}=\frac{d_{im}}{c} \tag{13}\]  
where \(c\) represents the light speed. The time delay between the IoT device and the drone can be given by the following equation.  
\[T_{im}=T_{im}^{tran}+T_{im}^{prop} \tag{14}\]  
The energy consumed by the device to transmit to the drone is calculated as  
\[E_{im}=\omega_{n}T_{im} \tag{15}\]  
In the UAV-LEO channel, the delay in offloading task \(S_{i}\) from the UAV to \(leo_{j}\) includes a transmission delay \(T_{mj}^{tran}\), propagation delay \(T_{mj}^{prop}\), and computation delay \(T_{j}\), which can be expressed separately as  
\[T_{mj}^{tran}=\frac{S_{ij}}{R_{mj}},\ T_{mj}^{prop}=\frac{H_{mj}}{c},\ T_{j}= \frac{S_{ij}\beta}{f_{ij}} \tag{16}\]  
where \(S_{ij}\) is the task volume size transferred from \(S_{i}\) to \(leo_{j}\), \(\beta\) represents the process cycles taken by the CPU to execute one bit of the task volume, and \(f_{ij}\) indicates the computing frequency allocated to \(S_{ij}\) by \(leo_{j}\). The drone to satellite time delay is  
\[T_{mj}=T_{mj}^{tran}+T_{mj}^{prop}+T_{j} \tag{17}\]  
The energy used consists of the link transmission energy and the energy needed to calculate in the LEO satellite.The following equation gives the calculation of the transfer energy consumption [36].  
\[E_{mj}=\frac{P_{mj}S_{ij}}{R_{mj}} \tag{18}\]  
where \(P_{mj}\) is the uplink power of the UAV \(m\). According to the following equation, the satellite calculates the energy consumption as  
\[E_{j}=kS_{ij}\beta f_{ij}^{2} \tag{19}\]  
where \(k\) is the energy factor. The total time delay \(T\) and total energy consumption \(E\) can be expressed as  
\[T=T_{prop}+T_{tran}+T_{j},E=E_{im}+E_{mj}+E_{j} \tag{20}\]  
where \(T_{prop}\) is the total propagation time delay, \(T_{prop}=T_{im}^{prop}+T_{mj}^{prop}\). \(T_{tran}\) is the total transmission time delay, \(T_{tran}=T_{im}^{tran}+T_{mj}^{tran}\).
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### Pre-Configured Protection Schemes
#### 2.3.1 The \(1+1\) Protection Scheme and Extensions
In [66], the authors provided the mathematical formulation to evaluate the availability/unavailability of protected optical connections for the general M:N protection scheme in WDM optical networks. In [67], the authors used shared protection for multicast demands where each light-tree was protected from any single link failure in both directions by having a backup path that is link-disjointed to the path from the source to each destination on the primary tree. The failure probability of each link is already considered in the \(1+1\), \(1\):\(1\):N, and M:N  
Figure 3: The \(1+1\) protection scheme: (**a**) \(1+1\) protection configuration; (**b**) the failure occurs at the working light path; (**c**) the failure occurs at the backup light path; s: source node and d: destination node. protection schemes. In [68], the authors proposed shared-path protection with joint failure probability constraint in flexible bandwidth optical networks. They developed three different algorithms and showed that the minimum free spectrum block (MFSB) consumption algorithm not only achieved better performance in terms of blocking probability, spectrum consumption, spectrum redundancy, and hop counts, but also guaranteed a joint failure probability lower than the maximum shared spectrum block consumption algorithm. In [69], the authors proposed spectrum-aware survivability strategies with failure probability constraints under static traffic in flexible bandwidth optical networks. An integer linear program (ILP) model and two algorithms were developed. In [70], the authors proposed a sub-tree based optical multicasting scheme in elastic optical networks. In [71], they proposed the link importance incorporated failure probability measuring solution for multicast light-trees. In [72], the authors proposed distributed sub-light-tree-based multicast provisioning with a shared protection scheme. Each source-destination pair of a primary DSLT was protected by a link-disjoint backup path against any link failure.
## 2 Materials and Methods
### Validation  
The accuracy of the geomorphological feature mapping was assessed by comparing it to mapping results available from the National Land Survey of Iceland (NLSI) [77], and information provided by [PERSON] et al. [45].  
As for the accuracy assessment of the DEMs generated using Sentinel-1 image pairs, we used the high-resolution ArcticDEM as a reference. This is in line with the literature, which recommends that the resolution of the reference data should be at least three times higher than the resolution of the DEM elevation being evaluated [78; 79]. The ArcticDEM has known biases of several meters due to errors in the sensor models due to the satellite position and different sensors in the ArcticDEM constellation. However, its vertical accuracy is corrected by registering it to the Ice, Clouds, and Land Elevation Satellite mission (ICESat) altimetry information and the results can be used without further corrections [80]. The DEM quality assessment was conducted by comparing the statistical measures, such as the minimum, maximum, mean, and standard deviation, of the generated DEMs with those of the reference ArcticDEM. The vertical quality assessment was completed using the root mean square error (RMSE), whereas the horizontal quality assessment was completed using autocorrelation and the Moran's I index [81; 82].  
The RMSE includes both random and systematic errors introduced during data production [83], and is expressed by the following equation:  
\[RMSE=\sqrt{\frac{\sum\left(y_{i}-y_{ti}\right)^{2}}{N}} \tag{2}\]  
where \(y_{i}\) refers to the ith interpolated elevation, \(y_{i}\) refers to the ith known or measured elevation of a sample point in a reference dataset, and \(N\) is the number of sample points.  
While the RMSE is a valuable quality-control statistic, it does not provide an accurate assessment of how well each cell in the DEM represents the true elevation. The RMSE only provides an assessment of how well the DEM corresponds to the data to which it is compared [84]. The [PERSON]'s I index expresses local homogeneity by comparing the difference between neighboring pixels to the standard deviation. The [PERSON]'s I index ranges between \(+1\) and \(-1\), where \(+1\) indicates a strong spatial autocorrelation, \(0\) a spatially uncorrelated data, and \(-1\) a strong negative spatial autocorrelation. The Moran's I was calculated by adopting the \"Queen's\" rule, which takes into account the eight neighboring pixels. The generated DEMs from the InSAR analysis were first subtracted from the ArcticDEM, and then the Moran's I index was calculated on the difference map. We consider only the \"stable area\" for applying the Moran's I and RMSE measures, i.e., the area without the landslide and the landslide-dam lake.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 2 System Description  
### PNC Model  
The system is a TWR channel with PNC located in the relay that allows two users to exchange messages in a wireless network. PNC consists of three nodes: User-1, User-2 and a relay, as depicted in Figure 1. Each user has one transmitter aperture, while the relay has two directional antennas. There is no direct link between User-1 and User-2. Therefore, the relay node is used to establish a communication link for the users in two phases: multiple access (MAC) phase (first time slot), which dedicates the entire transmit node to the relay, and broadcast (BC) phase (second time slot), which dedicates the entire broadcast relay node to the users. We assume that the two user nodes operate over a half-duplex link with the same transmit power and perfect synchronization. During the MAC phase, User-1 and User-2 transmit the information bearing signals \(S_{1}\) and \(S_{2}\) to the relay node over their respective links. At the relay, the received signal \(R=S_{1}+S_{2}+W\) is mapped by a special demapping/mapping technique to produce a network-coded bit, \(Y\), as \(PNC:R\to Y\), where \(W\) represents the noise source. In the BC phase, the relay node broadcasts \(Y\) to both nodes over the corresponding link. At the respective destinations, the received signal \(Y+W\) is recovered by an exclusive-OR (XOR) operation \(\oplus\). The desirable information for User-1 and User-2 is \(\hat{b}_{2}\) as \((Y+W)\oplus b_{1}\to\hat{b}_{2}\) and \(\hat{b}_{1}\) as \((Y+W)\oplus b_{2}\to\hat{b}_{1}\), respectively.
## 4 Conclusions  
Based on this study of the flame-retardant properties and combustion behaviors of EVA/MH/PCS/DVB composites, the results show that the flame-retardant MH/PCS/DVB showed high flame-retardant efficiency in the LOI test and had a good effect on the control of smoke release, which was due to the improved strength of the residual char due to cross-linked PCS. In addition, the thermal stability and the residues of the MH and EVA/MH composites changed little due to PCS/DVB in comparison with PCS, which indicates that the improved flame retardancy of the EVA/MH/PCS/DVB composites lies in the condensed phase. The PCS cross-linked by DVB can leave more products in the residue during combustion and acts as a binder that can effectively join magnesium oxide particles together; thus, an intensive residual char with a special structure forms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **EVA/MH (Atomic \%)** & **EVA/MH/PCS/DVB (Atomic \%)** \\ \hline Mg & 22.36 & 19.5 \\ O & 56.28 & 54.44 \\ C & 21.36 & 18.97 \\ Si & - & 7.09 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Contents of elements in EVA/MH and EVA/MH/PCS/DVB in XPS testing.  
Figure 16: Schematic diagram of the char formation process of the EVA composite.  
**Funding:** This research was funded by the Key Projects of the Natural Science Foundation of Heilongjiang Province (ZD2020E007).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
Accordingly, this network structure is capable of effectively representing and detecting the key input data features and exhibits high stability and convergence capability. Therefore, the 14,700-8-3-1 network structure was applied in all subsequent experiments. For illustrative purposes, we applied both the training and testing datasets in the training process, and the accuracy rates obtained at each iteration according to Equation (7) are presented in Figure 8. As can be seen, the prediction accuracy obtained for both the training and testing datasets steadily increases as the value of the loss function continues to decrease according to the results discussed above, and the model accurately identifies the lithofacies types in both the training and testing datasets after around 3000 iterations. Details regarding the sample types and prediction accuracy obtained for the eight samples in the testing dataset are listed in Table 4. To mitigate the risk of overfitting and not properly testing, we applied L2 regularization and incorporated dropout procedures for both the input and hidden layers during the training process. Furthermore, when tested on different segments of the same drilling data, we consistently achieved an accuracy rate of over 92% for this binary classification problem, compared to a prediction accuracy rate of 31.86% \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network** & **Loss at 20,000** & **Iterations When** & **Training Time (s)** \\
**Structure** & **Iterations** & **Loss = 0.05** & **Training Time (s)** \\ \hline
14,700-3-1 & 0.051492 & — & 27.469723 \\
14,700-8-3-1 & 0.002734 & 4792 & 41.872812 \\
14,700-8-5-3-1 & 0.031424 & 13,354 & 44.347052 \\
14,700-10-10-8-5-1 & 0.000713 & 3383 & 60.278994 \\
14,700-20-10-8-3-1 & 0.000726 & 3332 & 87.465936 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Loss values obtained under different neural network structures. Figure 7: Loss function values (Equation (3)) plotted with respect to the number of training iterations for different neural network structures. for SVM methods [27], 82.43% for multiclass SVM methods [23], 84.5% in multi-agent collaborative learning architecture methods [28], and 88.32% in HEMs [3]. We note from the table that the trained model required only 2.85 ms to classify all eight samples in the testing dataset, which is negligible compared to the 49 s required to collect the 4900 data points for a single sample at the applied sampling frequency. This enables the near real-time analysis of underground lithofacies during the drilling process after data collection.
## 4 Experimental and Results
### Diversity Analysis  
HierTTS has better generative diversity due to modeling five levels of latent variables. To verify the improvement of the model in generative diversity, we generated 100 samples using different systems for the same text (text to be synthesized: ). Figure 4 shows example histograms of each prosody feature for the generated speech samples. For the duration, among the baseline models, only VITS has duration diversity. Other models can only generate a deterministic duration for the same text. However, compared to VITS, HierTTS is more diverse in duration. HierTTS can speak both quickly and slowly, but in VITS, durations tend to be aggregated to some centralized locations in the distribution. In terms of fundamental frequency diversity, PortaSpeech and VITS can generate diverse fundamental frequencies, and their fundamental frequency diversity is still inferior to HierTTS. As shown in the left and right panels of Figure 4, HierTTS covers the range of VITS and PortaSpeech. This means that VITS and PortaSpeech cannot learn the completed distribution, and HierTTS can better fit the real prosodic distribution.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System** & **MOS** \\ \hline FastSpeech2 + HiFiGAN & \(3.84\pm 0.15\) \\ MultiGST + HiFiGAN & \(4.05\pm 0.13\) \\ PortaSpeech + HiFiGAN & \(3.06\pm 0.13\) \\ VITS & \(3.93\pm 0.11\) \\ HierTTS & \(4.32\pm 0.12\) \\ \hline GroundTruth & \(4.56\pm 0.13\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MOS evaluation of HierTTS and baselines with 95% confidence intervals.
## 2 Results
### Quantitative Analysis of Four Compounds from Two Perilla Cultivars  
The HPLC-DAD analytical method established in our previous studies [19; 27] was applied to the simultaneous determination of four compounds in the SC-CO\({}_{2}\) extracts of PFCA and PFC (Figure 5). The four components were separated on a reverse phase analytical column using a gradient solvent system of acetonitrile and water. The ultraviolet wavelength used for detection was 254 nm.  
Figure 4: Effects of **1** and **2** on NO production in RAW 264.7 cells. Data are presented as means \(\pm\) SD (\(n=6\)). \({}^{\#}\)\(p<0.01\) vs. negative control. \({}^{*}\)\(p<0.05\) vs. the LPS-alone group.  
The linearity of this analytical method was evaluated based on the correlation coefficient (\(r^{2}\)) value of the calibration curves of each compound. The calibration curves of the four compounds were obtained by the assessment of the peak areas of the standard solutions at four different concentrations. The calibration curves showed a high degree of linearity with an \(r^{2}\) > 0.9991 over the concentration ranges 20\(-\)100 \(\mu\)g/mL for **1** and **3**, 20\(-\)164 \(\mu\)g/mL for **2**, and 100\(-\)1000 \(\mu\)g/mL for **4** (Table 1). The limits of detection (LOD) and limits of quantitation (LOQ) for the four components were calculated using the slope of the calibration curve and the standard deviation (SD) of the intercept. The LODs and LOQs for the compounds **1-4** were in the range 0.006\(-\)0.124 \(\mu\)g/mL and 0.116\(-\)0.952 \(\mu\)g/mL, respectively. The amounts of the four compounds are listed in Table 2. The contents of **1-3** were approximately 9-, 5-, and 4-fold higher, respectively, in the SC-CO\({}_{2}\) extracts of PFCA than in those of PFC. Compound **4** was not observed in the SC-CO\({}_{2}\) extracts of PFCA; however, it was the most abundant in the SC-CO\({}_{2}\) extracts of PFC.  
Figure 5: HPLC chromatograms of (**a**) the SC-CO\({}_{2}\) extract of PFCA at 210 and 254 nm; (**b**) the SC-CO\({}_{2}\) extract of PFC at 210 and 254 nm; (**c**) the four standards: 9-hydroxy-isoegomaketone (**1**), isoegomaketone (**2**), and perilla ketone (**3**) at 254 nm and myristicin (**4**) at 210 nm.
###### Abstract
Considering the obtained results, we conclude that our approach provides reliable data collection in Smart Farming to support correct decision-making. The main contributions of this paper are:  
* An approach that introduces a Things-Fog-Cloud architecture that combines ML and Interpolation techniques to intelligently and automatically provide data reliability on SF applications. * An ML-based mechanism for outlier detection in IoT-based Smart Farming data collection. * An interpolation-based mechanism for replacing the outliers detected with inferred data. * An extensive evaluation of the proposed approach by a case study involving real data collected in a network based on a Things-Fog-Cloud and deployed in a Colombian Smart Coffee Farm. The remainder of this paper is organized as follows. Section 2 presents the related work. Section 3 introduces the proposed approach. Section 4 presents the evaluation of the proposed approach. In Section 5, we expose conclusions and future work.