esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications
## 5 Numerical Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Schemes**Sp. **Fac** & \(\mathcal{S}=6\) & \(\mathcal{S}=7\) & \(\mathcal{S}=8\) & \(\mathcal{S}=9\) & \(\mathcal{S}=10\) & \(\mathcal{S}=11\) & \(\mathcal{S}=12\) \\ \hline Traditional & \(-3\) & \(-6\) & \(-9\) & \(-12\) & \(-15\) & \(-19\) & \(-22\) \\ \hline Proposed (FFT) & \(-6\) & \(-11\) & \(-13\) & \(-16\) & \(-19\) & \(-22\) & \(-25\) \\ \hline Proposed (Hybrid) & \(-8\) & \(-11\) & \(-14\) & \(-17\) & \(-19\) & \(-22\) & \(-26\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SNR (in dB) threshold comparison. in most cases, the proposed schemes exhibit a better SINR threshold compared to the traditional scheme.
# Selected Applications of Terahertz Pulses in Medicine and Industry
## 5 Terahertz Imaging and Spectroscopy in Cultural Heritage  
An area for the fruitful application of terahertz technology is the scientific study of cultural heritage. Here, the unique properties of terahertz radiation provide both three-dimensional imaging and the materials' identification, which have had considerable impact. In particular, the non-invasive nature of the technology allows for the investigation of object d'art that is not provided by other technologies. Infra-red, Raman and X-ray fluorescence have been used, but each have their limitations. For example, the non-invasive nature of terahertz does not affect thermoluminescence dating, and the penetration is greater than infra-red, which is scattered.
# Galileo Receiver Performance Analysis with New I/NAV Improvements Live Data +
## 2 Materials and Methods  
The first step is the collection of GNSS signals via a Radio Frequency recorder in a mobile testbed vehicle in both Urban and Open Sky environments (Figure 1). The time windows were chosen in order to have a minimum of 4 visible satellites with the new I/NAV Improvement features. The data collection resulted in a total of 2 h of Urban environment data collection and 4 h of Open Sky environment with 4 to 6 satellites visible in the sky broadcasting the new I/NAV improvements. It is important to note that since the I/NAV update in the constellation is still being deployed, the performance characterization is based on live data recorded from a reduced subset of satellites. The recorded GNSS signals were replayed in the ESA Navigation Laboratory connected to a Septentrio Test User Receiver (TUR-N) able to process the new I/NAV Improvement features. All tests were performed in Single Frequency (SF) mode using E1B signal. In order to properly compute the improvement of the new features with the same satellite constellation geometry and visibility, all tests were conducted using only the satellites broadcasting the new I/NAV features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{T_{0}}\) **(s)** & **Legacy E1B I/NAV** & **Improved E1B I/NAV** \\ \hline
1 & W02 & W02 \\
3 & W04 & W04 \\
5 & W06 & W06 \\
7 & W07/W09 & W07/W09 \\
9 & W08/W10 & W08/W10 \\
11 & W00 & W17/W19 \\
13 & W00 & W18/W20 \\
15 & W00 & W16 \\
17 & W00 & W00 \\
19 & W00 & W00 \\
21 & W01 & W01 \\
23 & W03 & W03 \\
25 & W05 & W05 \\
27 & W00 & W00 \\
29 & W00 & W16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: I/NAV message subframe layout (Blue = Legacy CED, Green = RS CED, Orange = Reduced CED).  
Figure 1: Vehicle used for mobile data collection.  
For RedCED and RS performance tests, the receiver is reset via an automatic setup in cold start in order to evaluate individual satellite data demodulation time (excluding acquisition and tracking time) through a statistically significant number of points. Figure 2 presents both Data Demodulation and TTFF time, but the results in Section 3 focus only on Data Demodulation improvement using the new I/NAV features, as the overall TTFF is also impacted by the acquisition and tracking time.  
For SSP, the receiver is reset via an automatic setup in warm start where a coarse time estimate is provided to the receiver after each reset (for the legacy time retrieval, the provided coarse time estimate has no effect). As these upgraded satellites broadcast both legacy and new words, it is possible for the receiver to choose which words to process. The RS, RedCED, and SSP tests' reset starting times are chosen randomly in order to evenly distribute the entry points of the I/NAV subframe.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment
### Key Performance Indicators  
There are three key performance indicators (KPIs) considered in this work: (1) Convergence time; (2) Voltage quality; and (3) Communication delays.  
The first KPI, convergence time, is a measure for how long it takes the algorithm to solve the voltage problem. Voltage quality reflects how well the control algorithm can mitigate the voltage rise (or drop) problems. Finally, the communication delays depend on the communication infrastructure. Below follows an explanation of how each of these KPIs is quantified in practice.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 3 The Proposed Method
### Anchor Argument Matching
This method is not required to reduce the set IOU threshold but increases some IOUs between anchor and target through maximization, which is showed in Figure 2. On the premise that other modules are not affected, if more anchors match the target, the probability of obtaining high performance after NMS is greater. With the increase in the number of auxiliary copies (from different angles), which is \(x\) in Equation (2), it will be fairer for targets from any angle. Therefore, the targets with wide aspect ratios are treated equally by the network regardless of their angles. Moreover, theoretically, as our method is fairer for targets with arbitrary angles, it can reduce the demand for random rotation in target detection with a high aspect ratio. The factors affecting the IOU value between two rotating rectangular boxes include position, area ratio, aspect ratio and angle. If only the IOU threshold is changed, the feasible range of these four quantities will be affected at the same time. The method in this paper only affects the feasible range of the angle.
## 2 Materials and Methods
### Increased Time Characteristics  
The time characteristics were increased in order to overcome missing data problems. Time was added as an additional variable using trigonometric transformations, such as sinusoidal functions to represent the cyclic variations of solar irradiance, thus improving the model's prediction. The transformations were applied according to Equations (3)-(6).  
\[\text{hour\_sin}=\sin\left(\frac{2\pi\cdot\text{h}}{24}\right) \tag{3}\]  
\[\text{minute\_sin}=\sin\left(\frac{2\pi\cdot\text{min}}{60}\right) \tag{4}\]  
\[\text{month\_sin}=\sin\left(\frac{2\pi\cdot\text{month}}{12}\right) \tag{5}\]  
\[\text{day\_of\_year\_sin}=\sin\left(\frac{2\pi\cdot\text{day\_of\_year}}{365}\right) \tag{6}\]  
These temporal features may permit the model to capture the periodic variations inherent in the solar irradiance data more effectively, thus improving the forecasting accuracy.  
Figure 3: Correlation matrix between different meteorological variables.
## 3 Methodology
### Graph Optimization  
#### 3.2.1 GCN-Based Graph Node Optimization  
The high-quality phrase graphs that are currently being constructed have nodes that only consider shallow semantic information and their representation ability is not strong. The graph nodes need to be updated by a graph convolutional neural network to extract deep semantic information. This operation facilitates the clustering or subsequent processing of the graph.  
First the nodes of the high-quality phrase graph are denoted as \(\left\{n_{1},n_{2},\ldots,n_{M}\right\}\), where \(M\) is the number of nodes. The node number is matched with the semantic embedding of the compound high-quality phrase in \(\left.TEDHP=\left\{TEHP_{1},TEHP_{2},\ldots,TEHP_{n}\right\}_{M}\right.\), i.e., \(n_{i}\) is the \(i\)th compound high-quality phrase semantic embedding, including the phrase embedding and the corresponding time-embedding feature \(t_{i}\). Let the high-quality phrase graph be \(G=\left(N,E\right)\), where \(N=\left\{n_{1},n_{2},\ldots,n_{M}\right\}\) is the set of nodes in \(G\) and \(E\) is the set of edges in \(G\). When performing graph convolution on \(G=\left(N,E\right)\), the time embedding in node \(n_{i}\) is not considered, i.e., another graph \(\hat{G}=\left(\bar{N},E\right)\) is constructed, where \(\bar{N}=\left\{n_{1}-t_{1},n_{2}-t_{2},\ldots,n_{M}-t_{m}\right\}\). A graph convolution update is performed on \(\hat{G}=\left(\bar{N},E\right)\) to extract the deep semantic features of the nodes and obtain a high-quality phrase graph with deep semantic features. The update process is given by Equation (8) [22]:  
\[\hat{G}^{l+1}=Relu\left(D^{-\frac{1}{2}}\widetilde{A}D^{-\frac{1}{2}}\hat{G}^ {l}W^{l}\right) \tag{8}\]  
where, for a GCN with \(L\) layers, \(l\in[1,L]\), \(\hat{G}^{l}\) is the state at graph input, and \(\hat{G}^{l+1}\) is the output at layer \(l\). \(D\) is the degree matrix of the graph \(\hat{G}^{l}\), \(\widetilde{A}=A+I\) is the adjacency matrix with added self-connections, and \(A\) is the adjacency matrix of \(\hat{G}^{l}\). \(I\) is the identity array. \(Relu\) is the activation function, and \(W\) is a parameter in the graph convolutional neural network.  
After the GCN convolution operation, a new high-quality phrase graph \(G^{\prime}=\left(\hat{N}^{\prime},E\right)\) is obtained, where \(\hat{N}^{\prime}=\left\{\hat{n}_{1}+t_{1},\hat{n}_{2}+t_{2},\ldots,\hat{n}_{M }+t_{M}\right\}\), and \(\hat{n}_{i}\) denotes the deep semantic embedding feature of the \(i\)th node in the new high-quality phrase graph.
# Adaptive Model-Free Coupling Controller Design for Multi-Axis Motion Systems
###### Abstract  
In this study, we introduce an adaptive model-free coupling controller while using recurrent fuzzy neural network (RFNN) for multi-axis system to minimize the contour error. The proposed method can be applied to linear or nonlinear multi-axis motion control systems following desired paths. By the concept of cross-coupling control (CCC), multi-axis system is transferred into a nonlinear time-varying system due to the time-dependent coordinate transformation; tangential, normal, and bi-normal components of desired contour. Herein, we propose a model-free adaptive coupling controller design approach for multi-axis linear motor system with uncertainty and nonlinear phenomena. RFNN establishes the corresponding adaptive coupling controller to treat the uncertain system with nonlinear phenomenon. The stability of closed-loop system is guaranteed by the Lyapunov method and the adaptation of RFNN is also obtained. Simulation results are introduced in order to illustrate the effectiveness.  
adaptive control; contour error; multi-axis; coupling; motion control +
Footnote †: journal: Applied Sciences
## 3 Residue Number System and Multiple Error Correction
### Modular Projection Method  
The modular projection method is used to correct erroneous residual digits of numbers in the RRNS representation. Let an error be detected in a number \(X^{\prime}\) written in the RRNS. The \((n,\ k)\)-RRNS allows correcting \(t=(n-k)/2\) erroneous residual digits [7]. According to the classical modular projection method, \(t\) or fewer errors are corrected in the following steps:  
1. Constructing \(C_{n}^{t}\) modular projections. A modular projection is obtained by deleting \(t\) remainders in the original RRNS representation of the number. Deleting different sets of \(t\) remainders, we construct different modular projections.  
Obtaining the weighted representation (weighted characteristic) of each modular projection. The weighted representation of a number is its magnitude in the WNS. The weighted characteristic of a number depends on the inverse RNS-to-WNS conversion method.
3. Comparing the weighted representation (weighted characteristic) of each modular projection with the value (weighted characteristic) of the dynamic range. A modular projection whose weighted representation (weighted characteristic) does not exceed that of the dynamic range contains the correct remainders. Note that there can be \(C_{n-i}^{t-i}\) correct modular projections, where \(i\) is the number of erroneous digits, \(i=1,\ldots,t\). In this case, the remainders deleted when constructing each correct projection will be erroneous.
4. Calculating the remainders on dividing the correct modular projection's weighted representation by the moduli corresponding to the erroneous remainders. Note that correcting erroneous remainders is not required to decode correct data from the RRNS: the weighted representation of a correct modular projection is the corrected number written in the WNS. When using the weighted characteristic of a modular projection in step 3), it is necessary to perform an additional step for decoding: obtain the weighted representation of the number from its weighted characteristic.  
For each projection, calculations are performed in parallel, which significantly reduces the total time to correct errors in RRNS codes. A critical drawback of this method is the fast-growing number of modular projections with an increase in the error multiplicity. An increase in the number of modular projections does not affect the total execution time of the error-correction procedure in RRNS codes due to the parallel computations for each projection. However, it sharply raises hardware costs [18]. This critical drawback can be overcome using the concept of maximum likelihood decoding (MLD) [19].
## 3 Results and Discussions  
### Simulation  
#### 3.1.1 Simulation Scenario Setting  
To study the transport behavior of oil spills in the basin of the Lancang River crossing pipeline section, three influencing factors, including river flow, wind (wind speed and direction), and leakage mode, were considered according to the hydrological and environmental conditions and the pipeline itself in the basin. The range of values for each of these parameters is given below:  
1. River flow. As a typical monsoon river, the Lancang River has a large variation of flow under different water periods. According to the hydrological data of the basin, the value of the river flow in this study is selected between the minimum flow in the dry season and the maximum flow in the abundant season, ranging from 500 m\({}^{3}\)/s to 7000 m\({}^{3}\)/s;
2. Wind, including wind speed and direction. Based on the wind data provided for 2021 and 2022, a rose wind map was produced, as shown in Figure 4. The average annual wind speed in the region is 2.1 m/s, and the main wind direction is southwest. The variables of wind speed were set from 1 m/s to 7 m/s, and the variables of wind direction were set in 8 directions;
3. Leakage modes. Leakage modes include fracture leakage and perforation leakage. According to the researched literature [42], when the pipeline section is fractured, from the beginning of the accident to the upstream and downstream valve closure, all the crude oil in the pipeline will be leaked into the Lancang River within 0.5 h; when the pipeline is perforated and leaked, the maintenance personnel can complete the emergency blocking operation within 2 h. The amount of leakage under the fracture leakage mode was calculated to be 2600 t; under the perforation leakage mode, the leakage hole size was set to be 5%D, 10%D, and 15%D (D is the outer diameter of the pipe of 813 mm), and the amount of leakage was 180 t, 720 t, and 1630 t, respectively.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Data Processing  
When examining the images, we noticed a mismatch in the radiometric resolutions of the sensors, with Planet images having 16-bit resolution and Sentinel-2 images having 12-bit resolution. Therefore, we scaled all cutouts to 8 bits so that all images have the same value scale per pixel. This rescaling was performed because the YOLO-series CNNs are implemented using the Ultralytics framework [12], which only supports images with 8-bit resolution and a maximum of three bands. Additionally, we only considered the visible bands, as we observed in Figure 2 that the target is well-represented in these bands. Additionally, since the goal of this work is to improve the original algorithm presented  
Figure 3: Samples of images that comprise the representative set of non-airstrips.  
Figure 2: Representation of the visible region bands of the same airstrip in the Planet and Sentinel-2 sensors.  
in [33], we used only the visible bands, as this was the best configuration reported in the original study.  
For the Planet images, we standardized the image dimensions by cropping each image to \(352\times 352\) pixels. This value was chosen because the smallest dimension of the raw images was 366 pixels in width, and the images needed to have dimensions that are multiples of powers of 2. Since 352 is a multiple of 32, it was selected to be used in CNNs. Thus, 352 is the nearest number that satisfies both criteria.  
Due to the difference in spatial resolution, Sentinel-2 images had much smaller dimensions compared to Planet images. Therefore, we performed upsampling of all Sentinel-2 images to dimensions of \(352\times 352\) using bicubic interpolation [37].
# Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization
## 3 Methods
### Exploratory Landscape Analysis Features
For example: Fitness distance correlation [39], \(FDC\), is co-linear with \(R^{2}_{Q}\) (\(\rho=0.714\)). Dispersion at 1%, \(DISP_{1\%}\), is co-linear with \(D\) (\(\rho=0.714\)). The adjusted coefficient of determination of a linear regression model, \(\bar{R}^{2}_{L}\), is co-linear with \(\bar{R}^{2}_{L1}\) (\(\rho=0.860\)). The adjusted coefficient of determination of a quadratic regression model including variable interactions, \(\bar{R}^{2}_{QI}\), is co-linear with \(\bar{R}^{2}_{Q}\) (\(\rho=0.871\)). The minimum of the absolute value of the linear model coefficients [8], \(\min(\beta_{L})\), is co-linear with \(H(\mathbf{Y})\) (\(\rho=0.865\)). The maximum of the absolute value of the linear model coefficients, \(\max(\beta_{L})\), is co-linear with \(H(\mathbf{Y})\) (\(\rho=0.915\)). Significance of second order [36], \(\xi^{(2)}\), is co-linear with \(\xi^{(D)}\) (\(\rho=0.860\)). Finally, the length scale entropy [40], \(H(r)\), is co-linear with \(H(\mathbf{Y})\) (\(\rho=0.888\)). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Feature** & **Description** & **Scaling** \\ \hline \multirow{3}{*}{Surrogate models} & \(D\) & Dimensionality of the problem & \(\log_{10}\) \\  & \(\bar{R}^{2}_{LI}\) & Adjusted coefficient of determination of a linear regression model including variable & Unit scaling \\  & \(\bar{R}^{2}_{Q}\) & Adjusted coefficient of determination of a purely quadratic regression model & Unit scaling \\  & \(CN\) & Ratio between the minimum and the maximum absolute values of the quadratic & Unit scaling \\  & & term coefficients in the purely quadratic model & \\ Significance & \(\xi^{(D)}\) & Significance of \(D\)-th order & \(z\)-score, \(\tanh\) \\  & \(\xi^{(1)}\) & Significance of first order & \(z\)-score, \(\tanh\) \\ Cost distribution & \(\gamma(\mathbf{Y})\) & Skewness of the cost distribution & \(z\)-score, \(\tanh\) \\  & \(\kappa(\mathbf{Y})\) & Kurtosis of the cost distribution & \(\log_{10}\), \(z\)-score \\  & \(H(\mathbf{Y})\) & Entropy of the cost distribution & \(\log_{10}\), \(z\)-score \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the ELA features employed in this paper. Each one of them was scaled using the listed approach.
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 1 Introduction
Such continuing inflow of quality research articles underscores the importance of global GNSS RO measurements in space weather monitoring and research. This paper extends the work of [PERSON] et al. [60] by examining the potential of F7/C2 in expanding our understanding of the various ionospheric features revealed by F3/C. [PERSON] et al. [60] overviewed the F3/C results and pointed to the prospects with F7/C2. The focus of this paper was to give the readers an overview of how these prospects are realized, highlighting the added advantages of the new F7/C2 observations. Thus, the major achievements in the ionospheric research over the last decade by using F3/C observations were briefly re-constructed based on the F7/C2 soundings wherever possible. Section 2 demonstrates the advantage of the F7/C2 configuration by comparing the distributions of RO soundings with that of the F3/C. Section 3 describes the major achievements in understanding the low-latitude electron density distribution. The advantage of F7/C2 as a space-based platform for near-real-time global plasma bubble monitoring is elaborated on in Section 4. Finally, the role of the F7/C2 observations in achieving realistic space weather nowcast and forecast is described in Section 5 based on the numerical models developed at the Space Weather Operational Office at Central Weather Bureau (CWB/SWOO) of Taiwan and at the Space Weather Prediction Center at National Oceanic and Atmospheric Administration (NOAA/SWPC).