esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Modeling Outage Frequency for Wireless Systems  
Safety-related applications are frequently implemented in a fail-safe or redundant way. In the case of a not very time-critical function, this could be realized with a retransmission mechanism. In the case of a time-critical, it is recommended to use redundant transmission over different channels or media, which avoids a possible bursty loss of a single channel. Let us assume a system which transmits a signal four times per second with a uniformly distributed outage probability \(1-p=q\). We may assume the system to be slightly dangerous (S1) after one second of not receiving a signal (\(P(X\geq 4)=1-\sum_{k=0}^{3}P(X=k)\)) and heavily dangerous (S2) after five seconds of not receiving a signal (\(P(X\geq 20)=1-\sum_{k=0}^{19}P(X=k)\)).
# The Potential of Aquafaba as a Structure-Shaping Additive in Plant-Derived Food Technology
## 1 Introduction  
As the world's population increases, so does the need for food [1]. In order to ensure food reserves and guarantee their appropriate quality and safety, new technological solutions are being sought [2; 3]. High hopes are associated with the use of plants and waste. Estimates indicate that four and a half times less land is needed to produce plant products than to produce food of animal origin [3; 4]. Six liters of water are needed to produce 1 kg of wheat, while 2500-6000 L are needed for 1 kg of meat [5]. Therefore, it is justified to develop food technology based on the use of plants. Production of legumes, rich in protein, is a partial alternative to animal sources food production. The greatest quantity of vegetable protein at the lowest production cost is obtained when growing soybeans, peas and beans [6; 7]. According to FAOSTAT data, in 2021 the countries of the European Community produced about 483,000 tons of chickpeas, and 10 years earlier about 537,000 tons. This shows that a consistently high production of these legumes is maintained. Presumably, this will generate larger amounts of aquafaba, which until recently was treated only as post-production waste now, in line with the zero-waste trend, it can be used in many ways [8; 9].  
Currently, healthy eating is the right way of human development and is becoming more popular. People are more likely to reach for functional food, but they also carefully choose products from store shelves. Low-calorie or low-fat foods are very popular [10]. It should also be noted that more and more people, for various reasons, choose a meat-free diet [11; 12; 13]. These reasons include economic, health, religious, ethical or environmentalconsiderations [14]. The growing demand for new and functional food products, especially of plant origin, contributes to the expansion of the assortment of food producers [15]. Both food technologists and scientists are constantly looking for new technologies and new possibilities to shape good quality food with the desired textural and physicochemical properties [16].  
Aquafaba is a raw material that responds to the previously mentioned needs; it is reasonable to conduct research devoted to it. So far, researchers have conducted research in the field of understanding the chemical composition, physicochemical properties and use of aquafaba in products, mainly as a substitute for eggs [17].  
The aim of this review is to present the current state of knowledge on aquafaba and to indicate potential directions for the development of food technology, with particular emphasis on the possibility of shaping the texture in the technology of food of plant origin, mainly in baking.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 1 Introduction
This style was appropriate for averting ship strikes and maintaining the mooring line. Response-based hydrodynamic models (SAMERS) were suggested by [PERSON] et al. to predict the excursion and mooring power of the FPSO over a lifetime of year N [13; 14]. They computed the responses from an offshore floating-based platform exposed to arbitrary waves, current loads, and wind. Hybrid versions of the ANN-FEM technique have been implemented by [PERSON] et al. [15] for a long-term time-based history to predict the retort of the FPSO and for the indiscriminate dynamic-based response of ladder tubes as well as for mooring lines. [PERSON] et al. [16] have developed an ANN-based surrogate model for predicting the response of offshore structures. They used FEM instead of dynamic analysis. The authors employed the line tension of FPSO to find the test model, and the results demonstrated that ANN is highly successful in precisely and swiftly anticipating responses. [PERSON] et al. [17], [PERSON] et al. [18], [PERSON] and [PERSON] [19], and [PERSON] and [PERSON] [20] used ANNs to forecast the hydrodynamic forces caused by waves and currents on thin cylinders. With an ANN-based technique, the current study proposes a nonlinear prediction of spar platform responses under numerous environmental-related factors, such as the wave period and wave height. By using entirely new environmental forces as inputs for the ANN, it was found that the response of the spar platform time history could be predicted more accurately. The findings demonstrate that the ANN technique is proficient and can ominously shorten the computation time required to predict response time histories.
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 2 Setup Design and Realization
### Setup for TC-002 and TC-003: Dynamic Accuracy Test  
For these tests, simulated signals are used. For TC-002, an assessment of a dynamic scenario 25 min long was created. The scenario starts with a static phase of 12 min; then, the user starts moving with a constant acceleration till it reaches 48 knots; then, the user maintains a constant velocity, and after that, the velocity is reduced to zero within 5 s. Finally, the simulation is concluded with a static part. For TC-003, the same scenario is used, but a different speed is set. The user starts moving with a constant acceleration till it reaches 24 knots; the user maintains a constant velocity for 3 min, during which the DUT is subject to deviations of 2 m on either side of the straight line at a period of 12 s; finally, the velocity is reduced to zero. The pass-fail criterion in this case is also based on 95% horizontal errors. The reference position is provided by the simulator.
## 3 The Design of _HeaLow_ and Implementation
### Offloading Decision Process in Availability Checker  
In _HeaLow_, a device (a worker) may process a workload by itself and finish the processing by the workload's deadline. Otherwise, the workload is offloaded to one of the device's neighbor devices, and the selected neighbor performs the processing for the workload owner. As explained briefly in the previous subsection, the availability checker decides whether workloads are offloaded or not. The proper decision making is important in _HeaLow_, so various factors should be comprehensively considered for appropriate decisions. For this reason, in this subsection, we give the detailed explanation about the offloading decision process in the availability checker.  
Figure 2: The workload processing time in the worker thread.
## 4 Analytical Results
### Fiber Length Effect  
Figure 4 presents the log(BER) as a function of fiber length for the IM/DD Fast-OFDM-CDMA system compared to AO-OFDM-CDMA and OCDMA-WDM at 16 simultaneous users.  
As demonstrated by Figure 4, the three PON configurations fulfill the FEC limit for a fiber length up to 180 km. Further, at an SSMF length of 100 km it is shown that the Fast-OFDM-CDMA system outperforms AO-OFDM-CDMA and OCDMA-WDM by \(10^{-1.5}\) and \(10^{-1}\) in terms of BER, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Signal bit-rate & 40 Gb/s \\ Modulation order & 16 QAM \\ Cyclic prefix length & 0.125 \\ Data subcarrier number & 64 \\ Launched power, operating wavelength & 10 dBm, 1550 nm \\ SMF attenuation & 0.2 dB/km \\ Default SSMF length & 80 km \\ CW laser spectral width (\(\Delta\lambda\)) & 0.15 MHz \\
2D-HC OCDMA code length & 49 \\
2D-HC OCDMA code weight & 4 \\
2D-HC OCDMA chip period & \(8.06\times 10^{-13}\) \\
2D-HC average probability hit of chips (H) & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analytical system parameters.  
Figure 3: BER versus simultaneous users.  
Finally, the Fast-OFDM-CDMA and AO-OFDM-CDMA systems present the same BER at a fiber length equal to 200 km.
## 2 Materials and Methods
### Velocity Calculation  
To calculate the averaged sampling distance for individual segments of the measurement, the speed of the train must be known. This can be obtained from the known information on the sampling rate \(f_{S}\) and the physical distance between the sensors \(d\) by calculating the shift between two sensor signals. The velocity \(v\) can be determined from the longitudinal distance \(\Delta x\) and time distance \(\Delta t\) between two corresponding data points:  
\[v=\frac{\Delta x}{\Delta t} \tag{1}\]  
Figure 1: Experimental setup on the train with a measurement box, including four laser triangulation sensors (numbered 1 to 4) for distance measurements.  
The time interval between two corresponding data points is calculated by the known sampling frequency of the measurement:  
\[\Delta t=\frac{1}{f_{S}} \tag{2}\]  
The longitudinal distance \(d\) between two sensors is defined by the chord structure and is known from the setup:  
\[\Delta x=\frac{d}{N_{x}} \tag{3}\]  
where \(N_{x}\) represents the phase shift in the form of a number of data points. The shift between the sensor signals is shown in Figure 2.  
The phase shift (number of data points) between the respective sensors is marked with the parameters \(N_{12}\), \(N_{23}\) and \(N_{34}\). To determine the phase shift, the PCC (Phase Cross-Correlation) described by [PERSON] [23] can be used, which was originally intended for an application in seismology. The basis is the theoretical assumption that all four sensors measure an identical profile on the same line with minor deviations to successfully achieve a correlation. Before determining the phase shift, the data are filtered with a high-pass filter to remove the long-wave components of the signals. If all known parameters are applied, this results in the velocity \(v\):  
\[v=\frac{f_{S}\cdot d}{N_{x}} \tag{4}\]  
The velocity values calculated using this approach are verified with two on-board velocity recordings of the train.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Parameters for Measuring the Performance and Quality of the Data Transmission Network  
[PERSON] and [PERSON] [41] define QoS as the speed and reliability level of delivery from various types of data loads in a communication network. This parameter indicates the ability of the network to provide better conditions for the traffic service. Several metrics are necessary to obtain QoS from networks, for example, delay, throughput and the number of lost packets.
# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 5 Discussion  
### Validity and Scenario Adaptability of Data Acquisition Scheme  
The multi-angle data acquisition scheme proposed herein achieves full-area coverage of the side walls and vaults in plunge pool corridors via the mechanical arm system integrated with the inspection robot. The 5\({}^{\circ}\) overlapping region design in the horizontal direction and stratified shooting strategy with five images in the cross-sectional direction effectively address the issue of detection blind spots arising from traditional single-view acquisition, ensuring the integrity and diversity of crack samples. In comparison to manual handheld device acquisition, this scheme notably enhances the standardization and efficiency of data collection while eliminating the influence of human perspective bias on subsequent model training. Furthermore, data augmentation techniques, including random flipping and color adjustment, expand the original dataset from limited samples to 4075 images, effectively mitigating the typical small-sample learning issue in water conservancy engineering and enhancing the model's robustness to complex backgrounds.
# A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters
###### Abstract
Since only terms with odd \(n\) values are expected in a conventional FV model, some researchers have been reticent with regard to the use of \(n\)-even terms notwithstanding their beneficial effect on the model fitting capabilities [10; 11]. Apart from these widely popular heuristic approaches, several models derived from previous knowledge of the device have been proposed [12; 13; 14]. In particular, the kernel structure of the model published in [14] was derived on basis of the available equivalent-circuit information of a typical FET device with thermal power feedback. This circuit-knowledge based Volterra model is referred to here to as the CKV model. Like the FV model, the CKV model contains only conventional odd-order terms, which impose a limitation to its accuracy and a pertinent question arises: can the univariate FV and CKV models also be made richer with some kind of \"beneficial\" circuit-knowledge derived terms? In the present paper, a novel structure with beneficial terms is demonstrated following a procedure based on a circuit knowledge perspective. First, an internal mechanism which is responsible for the signal envelope generation is identified and this new subnetwork is included in the CKV approach to augment its kernel structure. A basic example of the mentioned enhancement was advanced in [15], for a simple case of the bivariate CKV model due to the space limitations in a conference paper. In addition to the benefits of these terms in a general CKV model, this new communication also reveals the enhancement of the FV model when the bivariate structure is included. The existence of the internal envelope waveform provides a rationale to its use as a second control signal in a double Volterra series proposal [16]. In Section 2, a general bivariate model is derived from the conventional baseband FV model, the bivariate FV, with an enhanced regressor set. Additionally, the demonstration goes further into the PAcircuit knowledge to provide a less complex bivariate Volterra series model, the bivariate CKV. Compressed-sensing techniques are applied to identify the optimum set of active regressors of the proposed models in different experimental scenarios. In Section 3, the modeling performance of this approach is contrasted with the conventional Volterra models in the case of two amplifiers, a class AB and a class J. For comparison, the results for the GMP and the simplified model presented in [15] are also included. Finally, the aptitude of the bivariate models in the design of DPDs to linearize PAs is considered in Section 4 and a summary is presented in the last section.
## 2 Analysis Methods
### Equation of Motion of a Bottom-Fixed Structure  
In this study, the bottom-fixed structure was composed of a finite number of three-dimensional structural beam elements. The governing equation of motion of the structure is expressed as Equation (15).  
\[\mathbf{M}_{s}\mathbf{\ddot{q}}+\mathbf{C}_{s}\mathbf{\dot{q}}+\mathbf{K}_{s} \mathbf{q}=\mathbf{F} \tag{15}\]  
where \(\mathbf{M}_{s}\), \(\mathbf{C}_{s}\), and \(\mathbf{K}_{s}\) are the mass, damping, and stiffness matrices of the structure, respectively. \(\mathbf{q}\) is the displacement vector of the structure. \(\mathbf{F}\) denotes the force vector acting on the structure, such as the hydrodynamic force, self-weight, and buoyancy.  
The elements of the structure are slender bodies in which the width is much smaller than the length. The hydrodynamic force acting on the moving slender body can be obtained using the Morison equation [36].  
\[d\mathbf{F}=(C_{M}-1)\rho_{w}dV\big{(}\mathbf{\ddot{v}}-\mathbf{\ddot{q}} \big{)}+\frac{1}{2}\rho_{w}C_{D}dA\big{|}\mathbf{\dot{v}}-\mathbf{\dot{q}} \big{|}\big{(}\mathbf{\dot{v}}-\mathbf{\dot{q}}\big{)} \tag{16}\]  
where \(dA\) and \(dV\) are the infinitesimal area and volume of the body, respectively. \(d\mathbf{F}\) is the wave force acting on the infinitesimal element; \(C_{M}\) and \(C_{D}\) are inertia coefficient and drag coefficient, respectively. \(\mathbf{\dot{v}}\) is the velocity vector of water particle. The force acting on each element can be obtained by integrating Equation (8) over the entire element length.  
The equation of motion presented in Equation (15) can be decoupled into \(N\) independent single degree of freedom (SDOF) systems using modal analysis as follows:  
\[\mathbf{I\ddot{p}}+diag\big{(}2\beta_{j}\omega_{j}\big{)}\mathbf{\dot{p}}+diag \Big{(}\omega_{j}^{2}\Big{)}\mathbf{p}=\mathbf{\Phi}^{T}\mathbf{F} \tag{17}\]  
where \(\mathbf{I}\) is the identity matrix, \(\mathbf{\Phi}\) is the modal matrix, \(\mathbf{p}\) is the modal displacement vector (\(\mathbf{q}=\mathbf{\Phi}\,\mathbf{p}\)); \(\beta_{j}\) and \(\omega_{j}\) are the damping coefficient and natural frequency of the \(j\)th mode, respectively. The mode superposition method can reduce the computational time significantly, but the accuracy of the response depends on the number of modes.  
In this study, two rigid floating bodies were hinged to a bottom-fixed elastic foundation structure with massless rigid arms. The constraint forces as defined in Equation (3) caused by the floating-body motions act on the hinge points of the structure. These constraint forces act as external forces at the nodal points of the structure and are included in \(\mathbf{F}\) of Equation (17). The behavior of the foundation structure can affect the position, velocity, and acceleration of the hinge points, and eventually affect the motion response of the connected floating bodies. These coupling effects are discussed in Section 3.2.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Choice of Parameters  
Example (7) was chosen as a basic time series for demonstration, since its trend is complex enough and is not separable from the periodic component using Basic SSA.  
6.3.1. Choice of Threshold \(T_{0}\) for Pre-Processing in autoIOSSA + FOSSA  
The autoIOSSA + FOSSA method is the only one of the considered methods for which it is necessary to specify groups of components as an input. Specifying the input grouping is a difficult task because initially, if there is no separability, the components are mixed. The study confirmed that the autoIOSSA + FOSSA method is unstable to the choice of the low-frequency contribution threshold \(T_{0}\) for the initial grouping. We demonstrated this using the threshold \(T_{1}=0.5\) (Section 6.3.2 will show that this is a reasonable choice of \(T_{1}\)).  
Typical behaviour of MSE in \(T_{0}\) is presented in Figure 3 for Example (7). One can see that the range of values of the parameter \(T_{0}\) at which autoIOSSA + FOSSA shows errors comparable to those of the autoEOSSA method, is narrow. Nevertheless, we choose \(T_{0}=0.2\) for further numerical experiments.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 5 DGNSS Positioning  
In this section, we assess the PRC modeling performance through DGNSS positioning using three methods, and their accuracies are compared. The three methods are (1) point positioning without PRC; (2) DGNSS with modelled PRC; and (3) DGNSS with real PRC generated at the base station. The mathematical model used for positioning is as follows [28]:  
\[R_{r}^{s}(t)=\varrho_{r}^{s}(t)+\Delta\varrho_{r}^{s}(t)+\Delta\varrho^{s}(t)+ \Delta\varrho_{r}(t) \tag{1}\]  
\[PRC^{s}(t)=-\Delta\varrho_{r}^{s}(t)-\Delta\varrho^{s}(t)-\Delta\varrho_{r}(t) \tag{2}\]  
where, \(R_{r}^{s}(t)\) is the code range at receiver \(r\) to satellite \(s\) measured at epoch \(t\), \(\varrho_{r}^{s}(t)\) is the geometric range between receiver and satellite, \(\Delta\varrho_{r}^{s}(t)\) is the satellite-receiver-dependent range bias (e.g., atmospheric refraction effects), \(\Delta\varrho^{s}(t)\) is the purely satellite-dependent range bias (e.g., satellite clock error, orbit error), \(\Delta\varrho_{r}(t)\) is the purely receiver dependent range bias (e.g., receiver clock error, multipath), and \(PRC^{s}(t)\) is the pseudorange correction for satellite \(s\) at epoch \(t\). DGNSS position solutions were obtained by least-squares estimation at every epoch and no weights were given to the measurement.  
Positioning was conducted at the IGS station DAEJ located in Daejeon, South Korea, which was considered a rover and located approximately 133 km away in a southeasterly direction from the reference station at Inha University. The rover site is equipped with a Trimble NetR9 receiver and a TRM59800.00 antenna. The data sampling interval was 30 s.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 2 Methods  
The light field polarization is easily described by use of the four element Stokes vector, \(I\), as in, for example, Bohren and Huffman [22]. The four components of \(I\) are prescribed relative to some reference plane, which here we choose as the plane defined by the viewing direction and the nadir direction. Consider an electromagnetic wave of angular frequency \(\omega\). If the unit vector \(\hat{\ell}\) is parallel to, and the unit vector \(\hat{r}\) is perpendicular to, the reference plane (such that \(\hat{\ell}\times\hat{r}\) is in the direction of propagation), the electric field can be written as \(\overset{\rightarrow}{E}=E_{\ell}\hat{\ell}+E_{r}\hat{r}\), with:  
\[E_{\ell}=E_{\ell 0}\exp[i(\omega t+\delta_{\ell})], \tag{1a}\] \[E_{r}=E_{r0}\exp[i(\omega t+\delta_{r})] \tag{1b}\]  
where \(E_{\ell 0}\) and \(E_{r0}\) are real. The four components of the Stokes vector are then defined as:  
\[I\equiv E_{\ell}E_{\ell}^{*}+E_{r}E_{r}^{*}=E_{l0}^{2}+E_{r0}^{2} \tag{2a}\] \[Q\equiv E_{\ell}E_{\ell}^{*}-E_{r}E_{r}^{*}=E_{l0}^{2}-E_{r0}^{2},\] (2b) \[U\equiv E_{\ell}E_{r}^{*}+E_{r}E_{\ell}^{*}=2E_{l0}E_{r0}cos\delta\] (2c) \[V\equiv i(E_{\ell}E_{r}^{*}-E_{r}E_{\ell}^{*})=2E_{l0}E_{r0}sin\delta \tag{2d}\]  
where \(\delta\equiv\delta_{r}-\delta_{\ell}\). A derived parameter that we will use in our comparison is the degree of linear polarization, \(DOLP\), defined as:  
\[DOLP=\sqrt{\frac{Q^{2}+U^{2}}{I^{2}}} \tag{3}\]Another useful parameter is the angle of the plane of polarization, \(\chi\), which is defined as:  
\[\chi=\frac{1}{2}\text{arctan}\bigg{(}\frac{U}{Q}\bigg{)} \tag{4}\]  
We measured the Stokes vector over the upwelling hemisphere using the PolRADS and DPOL cameras and simulated it using a Monte Carlo-based radiative transfer (RT) code. Data were available from three field campaigns: Hawaii, USA, in December 2005; the Ligurian Sea in March 2009; and the New York Bight in May 2009.