esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Closed-Loop Control Algorithms
### Digital Positional PID Control Algorithm
The selection of these parameters depends mainly on the dynamics of the sensor system, which are often quite difficult to describe using mathematical models. Therefore, the optimal settings of the controller need to be determined through experiments, which is a process known as tuning [36]. Common PID tuning methods rely on heuristics, such as trial and error or tuning tables, and the tuning process is usually laborious. Consequently, most commercial controllers incorporate some automatic tuning methods [27]. Table 1 compares four PID tuning methods based on their complexity, precision, the effort to determine the optimal PID parameters, and their adaptability to changing systems. The automatic tuning method is the most effective overall, particularly for systems that may change. Given that the hardware of developed sensors is rarely identical and that sensor systems are susceptible to variability, automatic tuning methods with a high adaptability to system changes are preferred. This paper introduces an automatic tuning method, exploiting a single-neuron-based self-pre-optimization algorithm to identify the optimal PID parameters of the system efficiently and intelligently, and which is detailed in the following section.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 5 Machine Learning-Based Performance Prediction for EGNOS
### Selecting Machine Learning Features  
The first step in any machine learning-based model development is the careful selection of the input data features that will drive these models. Ideally, the data should contain all information that is available to the EGNOS system when it is in operation. However, there are some limitations, as the features need to be available for the period for which to predict. As a result, unpredictable quantities or events (such as the raw RIMS observations of the unexpected occurrences of faults in satellites or RIMS) cannot be incorporated.  
Important factors in determining the UDREI and GIVEI that can be predicted are the quality of the range measurements, as well as geometric aspects which impact the observability of satellite orbit and clock errors or ionospheric delays.  
For UDREI prediction, the following features have been selected:  
* A list of locations of the RIMS tracking the satellite;
* Satellite position;* Geometry metrics, such as DOP, number of RIMS in view;
* Elevation-dependent multipath variances;
* Weather indicators (from OpenWeather);
* Local space-weather indicators, including vertical TEC values and its gradients over time and space (IONEX files from CDDIS and CODE);
* Satellite and satellite clock generation.  
For GIVEI, the following features have been selected:  
* IGP location;
* A list of IPP locations of IPP near the IGP;
* Elevation-dependent multipath variances;
* Satellite positions.
* Satellite block and satellite clock generation;
* Global space-weather indicators (from the NOAA Space Weather Prediction Center, Space Weather Canada, GFZ German Research Centre for Geosciences, Royal Observatory of Belgium);
* Local space-weather indicators (as for UDREI).  
To train our models, we obtained most feature data, as well as UDREI and GIVEI labels from EDAS. For actual predictions, the machine learning model can use forecasted equivalents, based on GPS almanacs and weather and space-weather predictions.
## 1 Introduction
[20] obtained the critical pile inclination of batter piles by analyzing the test and numerical results. They believe that when the pile inclination is less than 30\({}^{\circ}\), the axial bearing capacity will change slightly, whereas when the inclination is greater than 30\({}^{\circ}\), the axial bearing capacity will decrease with the pile inclination. [PERSON] et al. [21] studied the influence of pile inclination on the skin-friction of batter piles through laboratory tests. However, they regarded the skin-friction in the entire pile as the same. The variation in skin-friction along the depth was not considered, which is inconsistent with the truth. In summary, previous studies have mainly focused on the bearing capacity of batter piles. The working behavior, deformation mode, internal force distribution, and development under vertical loading have rarely been reported. The relevant tests are necessary. When subjected to vertical loading, horizontal and vertical displacements of the batter pile will occur simultaneously. Two important factors have been ignored in previous studies: First, the constraint of the pile cap and mirror-like batter; second, the interaction between the symmetrical batter and the soil clamped by the pile, namely, the \"clamping\" effect. All of the above two factors will introduce a redistribution of internal force and make the working behavior more complex. Therefore, symmetrical batter piles were set as the test object in this paper. The main purpose was to further understand the load transfer mechanism and the difference from the single pile.
## 2 Methodology
### SPI (Standardized Precipitation Index)  
Standardized Precipitation Index (SPI) values can be derived not only from rainfall data collected at various stations but also from gridded precipitation datasets, such as those obtained from satellite or radar through remote sensing or simulated by meteorological models. The World Meteorological Organization (WMO) has endorsed the SPI as a key meteorological drought index for global drought monitoring due to its accessibility and computational simplicity [50]. The efficiency and ease of use in calculating and analyzing drought attributes make the SPI a practical choice, which is why it was also utilized in this study.  
The SPI is computed based on long-term precipitation records for a designated region and timescale. These records are then adapted to fit a probability distribution, typically transformed into a normal distribution characterized by the SPI mean of \"0\" and a standard deviation of \"1\". In this study, the gamma distribution was chosen for its suitability with climatic precipitation data and its widespread application in drought studies [23; 51]. Positive SPI values indicate above-average precipitation (wet periods), while negative values denote below-average precipitation (dry periods). Because the SPI is normalized, it provides a standardized way to represent both wet and dry conditions. Table 2 summarizes commonly used SPI threshold values for defining drought severity.  
Equation (1) provides the calculation of gamma probability function of long-term precipitation.  
\[g(x)=\frac{1}{\beta^{\alpha}\tau(\alpha)}x^{\alpha-1}e^{\frac{\alpha}{\beta}} \text{for }x>0 \tag{1}\]  
where \(\beta\) represents the scale parameter, \(\alpha\) represents the shape parameter, and \(x\) and \(\tau(\alpha)\) represent rainfall amount and gamma function, respectively. Equation (2) denotes the ranges of \(\alpha\) and \(\beta\)[52].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Starting Data** & **End** & **Average (mm)** & **Standard** & **Minimum (mm)** & **Maximum (mm)** \\ \hline
08.1920 & 12.2022 & 85.49 & 46.62 & 0.6 & 339.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data statistics.  
Figure 2: Study area.  
\[a=\frac{1}{4A}\left(1+\sqrt{1+\frac{4A}{3}}\right)\text{ {and} }\beta=\frac{\overline{x}}{a} \tag{2}\]  
where \(\overline{x}\) and \(n\) defines the average precipitation and number of observations.  
\[A=ln(\overline{x})-\frac{\sum ln}{n} \tag{3}\]  
\[G(x)=\int_{0}^{x}g(x)dx=\frac{1}{\beta^{a}\tau(a)}\int_{0}^{x}x^{a-1}e^{\frac{ x}{\beta}}dx \tag{4}\]  
Equation (5) details the parameters essential for calculating the cumulative probability of non-zero rainfall events.  
\[G(x)=\frac{1}{\tau(a)}\int_{0}^{x}x^{a-1}e^{-t}dt\text{ where }t=\frac{x}{\beta}. \tag{5}\]  
Additionally, the gamma function \((G(x))\) is undefined for 0. In such cases, the cumulative probability of zero and non-zero rainfall is determined using Equation (6), denoted as \(H(x)\).  
\[H(x)=q+(1-q)G(x) \tag{6}\]  
where \(q=m/n\) (zero probability of precipitation), \(m\): number of observations with zero precipitation.
# Compression Challenges in Large Scale Partial Differential Equation Solvers
## 3 In-Memory Compression
### Scalar Quantization for Overlapping Schwarz Smoothers
The computational overhead is sufficiently small as long as conversions between arithmetic data types are performed in hardware, which restricts the possible compression factors to \(\{2,4,8\}\), for which the speedup reaches almost the compression factor; see Figure 2. With direct hardware support for finer granularity of arithmetic data types to be stored [53], an even better fit of compression errors to the desired accuracy could be achieved with low overhead. The theoretical error estimates together with typical condition numbers of local matrices \(A_{\xi}\) suggest that using a 16 bit fixed-point representation should increase the number of CG iterations by not more than 10% due to preconditioner degradation, up to an ansatz order \(p=5\). In fact, this is observed in numerical experiments, leading to a speedup of preconditioner application by a factor of up to four. With moderate ansatz order \(p\leq 6\), even 8 bit fixed point representations can be used, achieving a speedup of up to six [38]. Similar results have been obtained for non-overlapping block-Jacobi preconditioners for Krylov methods applied to general sparse systems [37] and for substructuring domain decomposition methods [59]. We would like to stress that the bandwidth reduction is the driving motivation rather than the possible speedup due to faster single precision arithmetics, in contrast to BLAS level 3 algorithms. Not only is the preconditioner application memory bound such that the data size is the bottleneck,  
Figure 2: Run times of BLAS level 2 operations on \(2048\times 2048\)-matrices for overlapping Schwarz smoothers with mixed precision. Depending on the access patterns, a speedup over the Intel Math Kernel Library (MKL) almost on par with the compression factor can be achieved [38]. but there is also a compelling mathematical reason for performing the actual computations in high precision arithmetics: Storing the inverted submatrices \(A_{\xi}^{-1}\) in low precision results in a valid, though less effective, symmetric positive definite preconditioner as long as the individual submatrices remain positive definite. Performing the dense matrix-vector products in low precision, however, will destroy the preconditioner's symmetry sufficiently to affect CG convergence, and therefore leave the well-understood theory of subspace correction methods.
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 4 Through Metal Communication System
### Outside Subsystem  
The outside subsystem is shown in Figure 13(a) and is made of a Tx subsystem, Rx subsystem and microcontroller, the ATtiny104 Xplained Nano from Microchip Technology. The Tx subsystem includes a transistor-based mixer, part number BC547 from Fairchild, and a power amplifier, part number TDA7391 from STMicroelectronics, to transmit the power and data signals through the metal plate. The Rx subsystem uses a low noise amplifier to recover the signal and an envelope detector together with a comparator for demodulation. The low noise amplifier, part number MAX4475 is from Maxim Integrated configured for gain >400. The envelope detector was based on a passive design, and the comparator uses the built-in module of the microcontroller. When the Tx subsystem delivers its full power in the WPT mode, the low noise amplifier is turned off to avoid overloading. In the Rx mode, the low noise amplifier is turned on while the power amplifier is off.
# Suppressing Salt Transport through Composite Pervaporation Membranes for Brine Desalination
## 3 Results
### Incorporation of GO into PVA Matrix
#### 3.2.1 Characterizations
It took similar time (\(\sim\)200 s) for both films to achieve equilibrium during the immersion process. This clearly indicates that less salt is adsorbed by the membrane with GO incorporated: i.e., the GO incorporation considerably decreased the membrane's salt solubility in the PVA films. From the above characterizations on both composite membranes and freestanding films with GO incorporated into PVA matrix, a good dispersion of GO in PVA was observed by SEM and XRD results; and the hydrogen bonding formed between PVA and GO is detected from FT-IR and DSC analysis. The EWC and NaCl diffusivity results showed that the incorporation of impermeable GO decreased the size of the free volume in PVA matrix and also the water and salt solubility in the matrix. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Freestanding** \\ **Films** \\ \end{tabular} & \begin{tabular}{c} **Tg (\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **EWC in Milli-Q** \\ **Water (\%)** \\ \end{tabular} & \begin{tabular}{c} **EWC in 100 g/L** \\ **NaCl (\%)** \\ \end{tabular} & \begin{tabular}{c} **NaCl Diffusivity** \\ **(10\({}^{-6}\) cm\({}^{2}\)/s)** \\ \end{tabular} \\ \hline \begin{tabular}{c} **PVA only** \\ **PVA0.1 GO** \\ **PVA0.2 GO** \\ **PVA0.3 GO** \\ \end{tabular} & \begin{tabular}{c} 48 \\ 59 \\ 60 \\ 60 \\ \end{tabular} & \begin{tabular}{c} 49.6 \(\pm\) 7 \\ 25.35 \(\pm\) 1.41 \\ 38.15 \(\pm\) 2.05 \\ 35.35 \(\pm\) 4.37 \\ \end{tabular} & \
\begin{tabular}{c} 38.1 \(\pm\) 6.3 \\ 28.06 \(\pm\) 1.07 \\ 34.98 \(\pm\) 1.66 \\ 30.68 \(\pm\) 0.54 \\ \end{tabular} &
\begin{tabular}{c} 0.64 \(\pm\) 0.13 \\ 0.712 \(\pm\) 0.001 \\ 0.549 \(\pm\) 0.066 \\ 0.526 \(\pm\) 0.005 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermal and swelling property of the freestanding films (PVA0.1 GO denoted membrane with 0.1 wt. % of GO in PVA matrix). PVA: polyvinyl alcohol. GO: graphene oxide.
## Abstract  
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and network security maintenance. The objective of this paper is to enhance the prediction accuracy of cellular network traffic in order to provide reliable support for the subsequent base station sleep control or the identification of malicious traffic. To achieve this target, a cellular network traffic prediction method based on multi-modal data feature fusion is proposed. Firstly, an attributed K-nearest node (KNN) graph is constructed based on the similarity of data features, and the fused high-dimensional features are incorporated into the graph to provide more information for the model. Subsequently, a dual branch spatio-temporal graph neural network with an attention mechanism (DBSTGNN-Att) is designed for cellular network traffic prediction. Extensive experiments conducted on real-world datasets demonstrate that the proposed method outperforms baseline models, such as temporal graph convolutional networks (T-GCNs) and spatial-temporal self-attention graph convolutional networks (STA-GCNs) with lower mean absolute error (MAE) values of 6.94% and 2.11%, respectively. Additionally, the ablation experimental results show that the MAE of multi-modal feature fusion using the attributed KNN graph is 8.54% lower compared to that of the traditional undirected graphs.  
cellular network traffic prediction; deep learning; graph neural network; multi-modal feature fusion; attention mechanism
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 3 Methodology
### Sample Extraction  
Since PIREPs are point-based data, spatial samples should be extracted to develop empirical icing detection models. In this study, a sample extraction method was used to obtain icing and non-icing samples from the COMS and Himawari-8 data. Once resampling of the input data was conducted, icing and non-icing samples were extracted using a cold cloud top temperature mask and spatial buffering (Figure 2). The cold cloud top mask uses the IR1 channel from COMS, and CH13 from Himawari-8 with a threshold of 273 K brightness temperature, since IR1 and CH13 (i.e., ~11 \(\upmu\)m) represent cloud top temperature [3; 40]. Since point-based icing PIREP data typically have a spatial significance of ~20 km in radius [10], a buffer with a radius of 20 km from an icing PIREP location was used to extract the icing pixels in this study. For non-icing samples, a more conservative buffer size (i.e., a radius of 15 km) was used to ensure the reliability of the training data, as much more non-icing PIREP data exists compared to icing PIREP data.  
Figure 1: A flow chart of the proposed machine-learning-based icing detection approach.  
Through these processes, a total of 53,760 icing and 117,148 non-icing pixels for COMS, and 5506 icing and 12,352 non-icing pixels for Himawari-8, respectively, were extracted as reference data. The reference data were divided into five groups based on icing and non-icing cases, not individual pixels, and a five-fold cross validation was conducted to evaluate the machine-learning-based icing detection models.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Routing in MANETs
#### 3.1.3 Hybrid Routing Protocols  
Hybrid routing protocols, as the name suggests, attempt to leverage the strengths of both proactive and reactive approaches. They establish proactive routing zones around each node, where routing information for frequently accessed destinations is readily available, minimizing delay for local communication. However, when a node needs to send data packets outside its zone (similar to a reactive protocol), it initiates a route discovery process to find the optimal path to the destination. This hybrid approach offers a balance between the route maintenance overhead of proactive protocols and the potential delays inherent in reactive protocols [19].
## 3 Methods  
### Magnetotelluric Data Acquisition and Processing  
An MT survey was carried out in the Asal rift between 2007 and 2008 by the Icelandic company ISOR [12]. The geographical distribution of the MT stations covers a large part of the Asal Rift (Figure 1). Phoenix MTU-5 systems were used to record the data, i.e., the temporal variation of the three components of the magnetic field (\(H_{x}\), \(H_{y}\) and \(H_{z}\)) and the two components of the telluric field (\(E_{x}\) and \(E_{y}\)). In order to reduce random noise in the MT signals, a remote reference station located 10 km away from the study area was used [20]. The directions x, y and z correspond, respectively, to the north, to the east and to the depth. The data collection time was 48 h. For each site, time series were converted from time domain to frequency domain impedance tensor with the Fourier transformation method [20]. Then, a least square technique estimated was used in order to reduce and remove the noise related to the electric and magnetic fields that are not naturally induced but resulting from human activities. The frequency range of the data varies from 12.7 \(\times\) 10\({}^{-3}\) Hz to 400 Hz and corresponds roughly to a skin depth of the electromagnetic fields up to 24 km in the Earth if a resistivity of 20 \(\Omega\)m is assumed.  
Horizontal and orthogonal components of electric and magnetic fields are related to the impedance tensor by the following equation:  
\[\begin{bmatrix}E_{x}\\ E_{y}\end{bmatrix}=\begin{bmatrix}Z_{xx}&Z_{xy}\\ Z_{yx}&Z_{yy}\end{bmatrix}\begin{bmatrix}H_{x}\\ H_{y}\end{bmatrix} \tag{1}\]  
where  
\[\mathbf{Z}=\begin{bmatrix}Z_{xx}&Z_{xy}\\ Z_{yx}&Z_{yy}\end{bmatrix}\]  
is the complex impedance tensor.
## 2 Material and Methods
### Evaluation Metrics  
Four different evaluation metrics, namely, R\({}^{2}\), R, RMSE, and MAE, were used in this study. These metrics are widely used in the literature to compare the performances of the models proposed for predicting regression problems [40,49]. Equations of evaluation metrics are given in Equations (48)-(51).  
\[R^{2}=1-\left(\frac{\sum_{j}\left(Y_{j}-\hat{Y}_{j}\right)^{2}}{\sum_{j}\left(Y _{j}-\hat{Y}\right)^{2}}\right) \tag{48}\]  
\[p\left(Y,\hat{Y}\right)=\frac{1}{n-1}{\sum_{j=1}^{n}}\left(\frac{Y_{j}-\mu_{Y} }{\sigma_{Y}}\right)\left(\frac{\hat{Y}_{j}-\mu_{\hat{Y}}}{\sigma_{\hat{Y}}}\right) \tag{49}\]  
\[\text{RMSE}=\sqrt{\left(\frac{1}{n}\right)\times\sum_{j}\left|Y_{j}-\hat{Y}_{ j}\right|^{2}} \tag{50}\]  
\[\text{MAE}=\frac{1}{n}{\sum_{j=1}^{n}}\left|Y_{j}-\hat{Y}_{j}\right| \tag{51}\]
## 4 Prototype Fabrication and Measurements  
### Measurement Setup  
To assert the full-wave simulated results of the proposed FSS shield, a prototype consisting of \(48\times 35\) unit cells was fabricated on a 1.55 mm thick FR-4 substrate, as depicted in Figure 7a. Techniques like the waveguide [28], anechoic chamber [29], and free space [30] can be used to evaluate the electromagnetic performance of an FSS surface, depending on the specific application, frequency range, and required measurement accuracy. Since FSSs interact with spatial electromagnetic waves in free space, a free-space measurement setup was employed in this article to ensure more realistic conditions and achieve more accurate results, as reported in [31].  
Figure 7b illustrates the measurement setup that consists of a pair of horn antennas positioned at a far-field distance on either side of the absorbing screen. The antennas, with an operating bandwidth of 6-18 GHz, are fixed at the same height and serve as transmitting and receiving antennas. In addition, they are connected to the ShockLine MS46322B vector network analyzer (VNA) to measure the transmission response of the FSS filter. The experimental setup is calibrated through the aperture in the absorbing screen without the FSS sample to account for the cable and free-space propagation losses. These losses are later subtracted from the measured results to eliminate the effect of attenuated microwave signals. Subsequently, the fabricated FSS array is fixed in the aperture of the  
Figure 7: (**a**) A photograph of the fabricated FSS prototype with a zoomed view; (**b**) schematic diagram of the measurement setup inside the anechoic chamber.  
absorbing screen and its transmission responses in terms of \(|S_{21}|\) (dB) are measured for different incident angles.  
\[|S_{21}|\text{(dB)}=|S_{21}\text{(with FSS)}|\ -|S_{21}\text{(without FSS)}|, \tag{4}\]  
Moreover, the shielding effectiveness of the FSS is determined using the magnitude of the measured \(S_{21}\) in dB, expressed as  
\[SE\text{(dB)}=-|S_{21}|, \tag{5}\]
## 4 SMART DShot: ML-Enhanced Architecture
### ML Algorithm Framework
#### 4.2.1 Core Algorithm Suite
Figure 5: SMART DShot ML algorithm framework showing four synergistic timing correction algorithms with their mathematical foundations and intelligent selection strategy for real-time optimization. The Hybrid Adaptive Timing Correction (HATC) implements meta-learning for real-time algorithm selection based on observed error characteristics [32; 33]. Dynamically assigns weights to the KFTC, RLSTC, and FLTC outputs depending on the state of the system. The HATC control output is computed as follows:  
\[u_{\text{HATC}}=\sum_{i\in\{K,R,F\}}\alpha_{i}(s)\cdot u_{i} \tag{4}\]  
where \(u_{\text{HATC}}\) is the final hybrid timing correction output, given by Equation (4), \(u_{i}\) is the output of algorithm \(i\) (with \(i\in\{\text{KFTC},\text{RLSTC},\text{FLTC}\}\)), \(\alpha_{i}(s)\) is the state-dependent weighting factor for algorithm \(i\), and \(s\) represents the current state of the system or the observed error condition. The algorithm selection criteria are as follows: KFTC is preferred when the error variance \(\sigma^{2}\) exceeds the threshold \(\tau_{1}\), RLSTC is selected when the absolute value of the linear trend \(|\Delta\mu|\) exceeds \(\tau_{2}\), FLTC is activated when the nonlinearity metric \(\kappa\) exceeds \(\tau_{3}\), and in mixed conditions a dynamically weighted combination is used.