esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Analysis of the Variations
#### 3.2.4 IRT Monitoring  
One of the two tested samples was monitored with IRT during the capillary water uptake test, that is one sample by rock type and cycle. The complete setup is shown in Figure 2.  
The monitoring was performed by a FLIR SC655 operating in wavelengths between 7.5 and 14 \(\upmu\)m. The detection temperature of this device is between \(-\)40 and 150 \({}^{\circ}\)C with a sensitivity of 0.1 \({}^{\circ}\)C. The detector is an uncooled array of microbolometers. The image size is 640 \(\times\) 480 pixels and the noise signal is about 40 mK. The recorded signal is called thermosignal (TS) and it depends on temperature and emissivity, expressed in isothermal units (IUs).  
The measurements were performed in passive IRT mode, that is, without any external heat source. The risks associated with environmental variations were minimised (control of temperature and humidity, room lights off). The IRT camera recorded thermal images at a rate of 6 frames/minute for 30 min. The recordings were processed by the FLIR ResearchIR software.  
Two informations were obtained from the IRT data: the monitoring of the wet fringe along the samples and the rock cooling rate index (CRI 10, [44]), since the water rise did not reach the upper part of the samples during the first minutes, and the cooldown (from 40 \({}^{\circ}\)C to 23 \({}^{\circ}\)C) was done by exchange with the room temperature (Figure 3a).  
Figure 2: Experimental setup of capillary uptake controlled by automatic weight and IRT monitoring.  
**Wet fringe migration**  
The obtained IRT image during capillary water uptake revealed clearly the water presence on the rock (Figure 3b). However, with the rock cooling, the wet fringe and the details of the image were less and less evident with time. For that reason, a subtraction filter was applied, which produced images as the difference between the initial state and during the capillary test. In addition, an Advanced Plateau Equalisation (APE) scale was selected since it allowed us to observe more details and contrast from the rock (Figure 3c). A profile in the middle of the sample was plotted at different times to observe the water migration (Figure 3e).  
**Cooling rate index (CRI)**  
From the upper part of the sample (Figure 3a,b), the \"Temporal Plot\" function of the software gives the thermosignal evolution with time. The calculation of CRI is as follows:  
\[\text{CRI}\ =\ \frac{\text{\Delta T}}{\text{\Delta t}}=\frac{\text{T}10-\text{T} 10}{\text{t}10-\text{t}0} \tag{1}\]  
where T10 is the temperature after 10 min of cooling, T0 the initial temperature, t10 and t0 are, respectively, the times with a lapse of around 10 min. In the case of chalk, with a very fast water absorption kinetics, the CRI was calculated for the first five minutes, before the water reaches the upper half. The data used were the cooling slopes related to the square root of time. High slope values corresponded to fast cooling and low values to a slow cooling (Figure 3d).
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 3 Results
### Effect of CD on GWT  
Changes in GWT due to projected climate change were more evident in the results obtained for the GFDL and UKESM models (Table 4). In their case, the start of CD in the 1 March deadline indicated the shallowest GWT in the analyzed period. Based on the GFDL model data, GWT can increase by 27-41, 12-33, and 11-31 cm on average, as well as, for the UKESM model, by 14-34, 17-33, and 8-29 cm in wet, normal, and dry years, respectively. After this date, in the March-April period, the GWT decreased to the depth indicated for FD modeling. In CD simulations based on MPI model data, the practice start dates of 1 March and 15 March resulted in the GWT being maintained at similar depths in the adopted modeling variants. Based on the MPI model data for the start of the CD dates of 1 March and 15 March, similar results were obtained for the GWT, which can, on average, increase by 23-39, 19-36, and 16-35 cm in wet, normal, and dry years, respectively. Later start dates for withholding outflow resulted in a flattening of the water table to the FD practice.  
Figure 5: Taylor graph showing a statistical comparison between observations of GFDL, MPI, and UKESM model estimates of global future climate projections of precipitation for Poznan.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Drainage Variants**} & \multirow{2}{*}{
\begin{tabular}{c} **Initial GWT** \\ **(cm b.s.l.)** \\ \end{tabular} } & \multicolumn{5}{c}{**Average GWT (cm b.s.l.)**} \\ \cline{3-8}  & & **Wet** & \multicolumn{3}{c}{**Normal**} & \multicolumn{1}{c}{**Dry**} \\ \hline \multicolumn{8}{c}{GFDL} \\ \hline \multirow{2}{*}{FD} &  
Starting CD practice on 1 March increases the number of days with GWT above the level of laying drains. For wet years, the number of days for the initial depth of 40 cm b.s.l. was highest at 67, 64, and 60 days for the GFDL, MPI, and UKESM models, respectively (Figure 6A). Also, for a depth of 60 cm b.s.l., it was 63, 60, and 53 days; for a depth of 80 cm b.s.l., it was less than 54 days for the GFDL, MPI, and UKESM models, respectively. The timing of the start of CD practice in normal years indicated a reduction in the number of days. Depending on the initial depth of groundwater when withholding runoff on the 1 March deadline, a range of 37-58 days was indicated for the GFDL model; meanwhile, it was 44-61 for MPI and 45-60 for UKESM (Figure 6B). For dry years, the number of days was 33-54 for GFDL, 40-59 for MPI, and 32-53 for UKESM (Figure 6C). The start of CD practice on 15 March indicates the following time ranges for the GFDL model: 39-41 days, 25-32 days, and 22-28 days for wet, normal and dry years, respectively. For other models including MPI (51-63 days, 44-60 days, and 39-58 days) and UKESM, it was 29-35 days, 32-38 days, and 21-28 days, for wet, normal, and dry years, respectively.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods
### Proposed Model
#### 2.2.3 Multi-Convolutional Neural Networks (MCNNs)  
The CNN has achieved great success in image recognition due to its powerful feature extraction capability [37]. The reason for its success lies in the use of local connections and weight sharing, which reduces the number of weights and makes the network easier to optimize [38]. The CNN is mainly composed of convolutional layers, pooling layers, and fully connected layers. Using one-dimensional convolution to handle time series problems can not only maintain the continuity of sequence information but also improve computational efficiency [39]. Figure 3 shows the basic structure of a 1-D CNN.  
In wind power sequence data, there are features at multiple time scales. By using multi-convolutions with various kernels, feature information can be more comprehensively extracted. This paper selects the MCNN with three convolution kernels (2, 3, 5) for feature extraction, constructing independent 1-D convolutions for each kernel. The input data undergo convolution operations simultaneously with three different kernels. For the output of each kernel, max-pooling is applied on the feature maps to further reduce the feature dimension and retain the most significant features. By performing two convolution operations for each kernel, the three parallel convolution layers of different scales fully  
Figure 3: 1-D CNN.  
Figure 2: Attention mechanism.  
extract features. These features are fused together to form a higher-dimensional feature representation. The MCNN is shown in Figure 4.  
Formula (4) represents the convolution operation:  
\[y_{i}=f(\omega_{2}\otimes X+b_{2}) \tag{4}\]  
\(y_{i}\) is the output after applying the \(i\)-th convolutional kernel. \(f\) is the activation function that introduces non-linear feature transformations to the input, with ReLU being the activation function. \(\otimes\) represents the convolution operation, \(X\) is the data tensor, \(\omega_{2}\) is the weight of the convolutional kernel, and \(b_{2}\) is the bias needed in the network learning process.  
\[\widehat{y_{i}}=pool_{\max}(y_{i}) \tag{5}\]  
\[F_{i}=pool_{\max}(conv(\widehat{y_{i}})) \tag{6}\]  
\[F=Concat(F_{1},F_{2},F_{3}) \tag{7}\]  
The output after convolution is processed by the Formula (5) for the max pooling layer, with \(\widehat{y_{i}}\) representing the pooled feature sequence. Formula (6) performs convolution and pooling operations again, and each convolution kernel finally extracts the feature \(F_{i}\). The features successfully extracted by three types of convolutional kernels are \(F_{1}\), \(F_{2}\), and \(F_{3}\), and the fused new feature \(F\) is shown in Formula (7). The fused new feature prepares for extracting the time-dependent trend of the feature sequence in the next step.
# Implementation of Fault-Tolerant Control for
## 6 Experimental Results  
In this section, the real implementation of fault-tolerant control with AFTC-SMC and the proposed AFTC-SSMC is described, and the experimental results were compared and discussed in the case of both single and the multiple joint faults.
## 5 Conclusions and Future Work
### Future Work  
Although the proposed signal segmentation technique accomplished its objective of locating the portion of the linear axis stroke where the misalignment fault was introduced, there are still some limitations that can be used to generate new research opportunities. These have been identified as follows:  
* The technique was applied to a single type of misalignment. Given that a vertical misalignment may occur in the assembly of the linear axis, a future direction is to apply the technique and evaluate the fault's effect on the linear axis. Furthermore, the work can be extended by inducing both misalignment types at the same time and evaluating the vibration signal through the segmentation technique.
* The analysis was focused on time domain statistical features to evaluate if the segmentation technique can enhance their usability when the system is operating under a faulty condition. Future exploration should be conducted that apply machine learning methods or feature reduction methods such as principal component analysis on the segmented portions of the signal to build models and diagnose faults.
* The technique was applied in a controlled testbed environment with a single known induced fault. However, in industry CNC machines, it may exhibit various faultsat a time. Therefore, a future direction should include applying the segmentation technique to industry data and evaluate anomalous segments to localize the possible fault and diagnose what fault it is.
* For the presented studies, percentage error limits at 10% and 20% were hard set for the analysis to identify the changes on the features. This may be considered as a limitation since it constrains the system's operation. Future research work should be focused on utilizing the segmented signal and repair data condition to establish dynamic operational threshold limits to decrease the possibility of false alarms.  
Conceptualization, A.H.C.; methodology, A.H.C.; software, A.H.C.; validation, A.H.C.; formal analysis, A.H.C.; investigation, A.H.C. and J.M.D.; resources, S.C.V.; data curation, A.H.C.; writing--original draft preparation, A.H.C.; writing--review and editing, J.M.D.; visualization, A.H.C. and J.M.D.; supervision, S.C.V.; project administration, S.C.V.; funding acquisition, S.C.V. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) under the CANRIMT Strategic Research Network Grant NETGP 479639-15.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 1 Introduction
### Trend
In the expansion stage, chaotic public key cryptography algorithms were extended to other aspects of cryptography, such as identity-based public key encryption schemes, key exchange protocols, digital signature schemes, provable security, etc. In the early schemes of chaotic public key cryptography, [PERSON] proposed a chaotic public key algorithm based on cellular automata, which was not practical due to its limitations [22]. [PERSON] et al. proposed a distributed dynamical model chaotic synchronization public key encryption scheme [40], but it did not adhere to the principle of \"all secrets are embedded in the keys\" and had potential vulnerabilities against digital attacks. Subsequently, [PERSON] proposed a public key cryptography algorithm based on multiple chaotic systems [27], but it also suffered from security and efficiency issues. Public key cryptography based on multiple chaotic systems and Chebyshev polynomials remains of high value in chaotic public key cryptography research and is the focus of this project. Currently, [PERSON] et al. has further extended Chebyshev-based public key cryptography and proposed new encryption schemes [41], [PERSON] has proposed new identity-based encryption and signature schemes [33], and [PERSON] et al. has proposed a three-party key exchange protocol based on Chebyshev polynomials and proved its security in the standard model, driving the advancement of chaotic public key cryptography theoretical research [34]. [PERSON] et al. proposed a new multi-server authentication scheme based on Chebyshev chaotic mappings, which is low in computation and communication costs and has been widely cited by scholars [42]. [PERSON] et al. proposed a new cryptographic technique based on improved Chebyshev mapping and verified the effectiveness of the expected algorithm [43]. [PERSON] et al. proposed a new chaotic Chebyshev polynomial-generating function and applied it to image encryption with good resistance against various attacks [44]. In recent years, chaotic public key cryptography has achieved some new research results [45; 46; 47; 48].
# Development Status and Service Performance Preliminary Analysis for BDSBAS
###### Abstract  
The BeiDou global navigation satellite system (BDS-3) provides positioning, navigation and timing services for global users, moreover, it provides BDS satellite-based augmentation system (BDSBAS) single-frequency (SF) and dual-frequency multi-constellation (DFMC) services for users in China and its surrounding areas. The BDSBAS SF service is in accordance with Radio Technical Commission for Aeronautics (RTCA) standard protocol (RTCA MOPS) and augment GPS constellation, while the BDSBAS DFMC service is in line with SBAS L5 DFMC standard protocol and is aimed at supporting any combination of BDS/GPS/Galileo/GLONASS constellations, including only a single constellation operation. We introduced the development status of the BDSBAS system, including the system architecture and navigation user algorithms. Based on the GPS measurements, the accuracy, integrity and availability of the BDSBAS SF service were evaluated, and with the BDS measurements, the accuracy of the BDSBAS DFMC service was preliminarily analyzed. The integrity and availability of the BDSBAS DFMC service will be discussed in future work as some of the DFMC integrity parameters are still under discussion for optimization. The results show that, for BDSBAS SF service, the horizontal and vertical position accuracy were about 1.0 m and 2.0 m (95%), respectively, which were improved by 39% and 33%, respectively, compared with the GPS SF position accuracy. For BDSBAS DFMC service, the horizontal and vertical position accuracy were about 0.6 m and 1.2 m (95%), respectively, which were improved by about 25% and 20% compared with the BDS dual-frequency position accuracy. No system integrity risk event was detected during the testing period for BDSBAS SF service. The average availability of the BDSBAS SF service was about 98% which was mainly affected by the availability of ionospheric grid delay corrections.  
BDSBAS SF service; BDSBAS DFMC service; accuracy; integrity; availability +
Footnote †: journal: remote sensing
## 2 Related Works
### Cycle-Consistent Adversarial Network  
The cycle-consistent adversarial network (CycleGAN) is a generative adversarial network (GAN) architecture for image-to-image translation tasks. This approach has been widely used in various domains, including but not limited to image style transfer, domain adaptation, and super-resolution. The key idea of CycleGAN is to train two generator-discriminator pairs, with each pair consisting of a generator and a discriminator. One generator aims to translate an image from the source domain to the target domain, while the other generator aims to translate an image from the target domain back to the source domain. The discriminator in each pair is trained to distinguish the translated images from the real images in the corresponding domain. The cycle consistency loss is introduced to force the translated image to be transformed back into the original image.  
Figure 1 illustrates how CycleGAN works in one direction.  
CycleGAN is focused on the application of style transfer in computer vision. For example, [PERSON] et al. [24] originally proposed CycleGAN for unpaired image-to-image translation, where there was no one-to-one mapping between the source and target domains. This method has been widely used in tasks such as colorization, super-resolution, and style transfer. Based on CycleGAN, different models have been proposed for face transfer [25], Chinese handwritten character generation [26], image generation from text [27], image correction [28], and tasks in the audio field [29; 30; 31].  
One of the highlights of CycleGAN is the implementation of two consistency losses in addition to the original GAN loss: identity mapping loss and cycle consistency loss. The identity mapping loss implies that the source data should not be changed during transformation if they are already in the target domain. The cycle consistency loss comes with the idea of back translation: The result of back translation should be the same as the original source. These two loss functions cause the CycleGAN model to keep great consistency during its transfer procedure; thus, it is possible to handle unpaired images and achieve outstanding results.
## 1 Introduction
Therefore, we use a well-known alternating direction method of multipliers (ADMM) algorithm to obtain an iterative solution to the problem. The ADMM blends the idea of the augmented Lagrangian method (ALM) with the dual decomposition method [54; 55] to achieve an improved performance. The designed waveforms ensure an improved detection probability and bit error rate (BER) for radar and communications parts, respectively. Finally, the simulation results validate the efficiency of the proposed design in terms of convergence, approximation of the desired waveform, and beampattern synthesis. This paper is organized as follows. Section 1 gave a brief introduction of the study field and a review of the relevant literature. The signal model is explained in Section 2. Section 3 explains how the waveform has been formulated mathematically as non-convex optimization. In Section 4, the design problem is manipulated such that ADMM can be applied to it. The simulation results are provided in Section 5. Finally, the discussion of this paper is summarized and concluded in Section 6. Notations: The notations \(\mathbb{R}\) and \(\mathbb{C}\), used in this paper, represent the real and complex sets, respectively, while \(\Re(\cdot)\) and \(\Im(\cdot)\) denote the real and imaginary parts of the argument, respectively. The notation \(\otimes\) represents the Kronecker product, \(|\cdot|\) represents the absolute value of the argument, \(\|\cdot\|_{2}\) represents the \(l_{2}\) norm, and \(\mathbf{I}_{N}\) represents the \(N\times N\) identity matrix. Table 1 provides a list of the abbreviations used in this paper. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline ADMM & Alternating Direction Method of Multipliers \\ ALM & Augmented Lagrangian Method \\ ATC & Air Traffic Control \\ BER & Bit Error Rate \\ CM & Constant Modulus \\ CMC & Constant Modulus Constraint \\ CRSS & Communication-Radar Spectrum Sharing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations and their full forms.
## 3 Results and Discussion
### Effect of Ionic Strength  
The effect of ionic strength was investigated by cycling desorptive solutions with 0.1 mol L\({}^{-1}\) Cl\({}^{-}\) and SO4\({}^{2-}\) ranging from 0 mg L\({}^{-1}\) to 6000 mg L\({}^{-1}\). As shown in Figure 4b, the desorption efficiency first increases and then decreases with an increase in the ionic strength from 0 to 6000 mg L\({}^{-1}\). The highest desorption efficiency is obtained when the SO4\({}^{2-}\) concentrations are 2000 and 4000 mg L\({}^{-1}\), with values that are two times higher than that of 6000 mg L\({}^{-1}\) SO4\({}^{2-}\), meaning that the desorption efficiency is strongly dependent on ionic strength. This phenomenon results from the fact that the EDL is compressed under a higher ionic strength [55; 56], resulting in a lower diffuse layer potential (\(\psi\)). The electrode potential can be approximately represented by the diffuse layer potential; the desorption efficiency, therefore, decreases with an increase in ionic strength due to the lower cathode potential based on the electrodesorption mechanism. In addition, the decrease in the desorption efficiency in desorptive solutions without SO4\({}^{2-}\) is probably due to the lower solution conductivity.
# Mechanical Properties of Desert Sand-Based Fiber Reinforced Concrete (DS-FRC)
## 3 Results and Discussion
### Elastic Modulus  
The Elastic Modulus is an important mechanical parameter of engineering materials for the elastic deformation analysis of structure. It describes the initial elastic response of DS-FRC specimens. Therefore, it was essential to evaluate the Elastic Modulus of DS-FRC for verifying its engineering applicability. In this experiment, the effect of S/B and DSRR on the Elastic Modulus of DS-FRC under  
Figure 2: The schematic diagrams of test methods: (**a**) the uniaxial tensile test; (**b**) the tensile specimen and the position of extensometer; (**c**) the uniaxial compression test.  
the uniaxial compression test were studied. The results show that the Elastic Modulus of DS-FRC were about 13-20 GPa, which were lower than that of the ordinary concrete due to the increased porosity caused by the addition of fiber, the small particle size of aggregates and the low S/B, which is in line with previous research [55]. The Elastic Modulus of DS-FRC decreased with the increase of DSRR. Especially, it was only 13 GPa at the DSRR of 100%. As is shown in Table 1, the relatively low content of silica in desert sand may have had a certain effect on the reduced Elastic Modulus of DS-FRC. When the DSRR was low, few changes of the Elastic Modulus were observed with the increase of S/B. The values of the Elastic Modulus were approximately 18-19 GPa.  
The Poisson's ratio, referring to the ratio between lateral strains and longitudinal strains, ranged from 0.18 to 0.25, which was also smaller than the ordinary concrete, attributed to the bridging effect of fibers. It restricted cracks opening in the lateral direction.