esa-sceva/satcom-chunk-collection huggingface dataset:
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### Methodology
#### 3.2.4 RC and Video Barrage Jamming
(✓) indicates successful jamming. Figure 7: GNSS barrage jamming (fc = SR/6) signal spectrum. The frequency on the X-axis ranges from approximately 1.5720 GHz to 1.57888 GHz. This experiment assessed the impact of GNSS jamming on different drones. A jammer was set up at varying distances from the drone, and its ability to maintain a satellite connection was measured. The effectiveness of the jamming was evaluated by observing whether the drone could connect to satellites despite the interference. The primary consequence of GNSS jamming for all drones is the failure of the return-to-home function. When GNSS jamming occurs, the drone loses its ability to determine its location. Without the return-to-home function, the drone can be lost because operators usually control it from a considerable distance and often without direct visual contact with it. Different models of UAVs behave differently when jammed. The DJI and Husban models both experienced a loss of connection, but all the drones remained in the state commanded by the last signal without making any further corrections. However, in terms of safety, the Autel and ZLL models reacted better to the loss of connection by immediately performing a landing procedure. This is a safety feature that causes the drone to immediately land and return to the ground in the event of a loss of communication with the controller, thus avoiding scenarios in which the drone goes into uncontrolled flight.
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
## 4 Errors in Measuring the Electrical Length  
The use of load reflection to account for random deviation of the electrical length is based on controlling the node shift of the mixed standing and travelling wave. As shown earlier, the accuracy of such control is significantly affected by the presence of random microwave discontinuities along the path. That introduced reflection creates an additional wave shift and therefore introduces a measurement error of the electrical length.
# Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
## 2 Materials and Methods
### Computational Domain and Boundary Conditions  
For the seafloor terrain, this study focuses on dune terrain, which is widely distributed in various environments [18; 19; 20]. The formation and evolution of these dunes are influenced by hydrodynamic conditions including tides, currents, and waves, sediment levels, water depth, and topography. Scholars have investigated the evolution mechanisms of dunes to varying degrees [21; 22; 23]. The selection of dune shapes and proportions is based on references such as Omidygeaneh [21] and [PERSON] [24]. Taking into account the scale of the AUV, the computational domain is established with a total length of 8 L, a height of 1.5 L, and a width of 1.5 L. The establishment of the computational domain and coordinate system is illustrated in Figure 2. The boundary conditions are set with the inlet as a velocity inlet and flow direction along the positive \(x\)-axis, and the outlet is set as a pressure outlet. The bottom and the AUV surface are considered a no-slip wall, and the other surfaces are symmetric planes.  
Transient simulation is performed in the calculation, and the time step is set between 0.005 s and 0.01 s depending on the flow velocity. The density of water in the computational domain is 997.561 kg/m\({}^{3}\), and the dynamic viscosity is \(8.887\times 10^{-4}\) Pa \(\cdot\) s.
# Investigation into the Yaw Control of a Twin-Rotor 10 MW Wind Turbine
## 3 WT Yaw Mechanism  
The active (forced) yaw method is an automatic control method that uses electrical or hydraulic motors meshed with electrical drives, sets of gears, and bearings to align the rotor-nacelle assembly toward the wind flow direction [39]. It is mainly used in medium and large wind turbines, as it can track the maximum power according to the wind direction measurement feedback. In this work, the TRWT yaw mechanism included multiple, separately excited DC motors that took the same input signal to rotate the boom carrying the two WT rotors into alignment with the flowing wind direction.
## 2 Ikeda Map Used as a Basis for a Pseudo-Random Generator
### Description of the Ikeda Map  
The two-dimensional Ikeda map can be calculated using the following equations:  
\[x_{n+1}=1+u(x_{n}\cos t_{n}-y_{n}\sin t_{n}) \tag{1}\]  
\[y_{n+1}=u(x_{n}\sin t_{n}+y_{n}\cos t_{n}) \tag{2}\]  
where \(u\) is parameter and:  
\[t_{n}=0.4-\frac{6}{1+x_{n}^{2}+y_{n}^{2}}. \tag{3}\]  
The Ikeda attractor exists when \(u\geq 0.6\), as shown in Figure 1.  
Its bifurcation diagram (in the classical way with respect to parameter \(c_{3}\)[19]) is shown in Figure 2. This diagram allows us to discover the existence of a period doubling cascade to chaos. The diagram of the Lyapunov exponent (LE) [20] is plotted in Figure 3. It can be seen that the highest value of the LE is 0.18947. One can choose initial values in the interval \([-0.5,1.5]\).  
Figure 1: Plot of the Ikeda map.  
Figure 2: Bifurcation diagram of the Ikeda map.  
The chaotic functions realised on a software program will always show dynamical degradation [21]. With respect to [22; 23], we designed a pseudorandom byte generator based on two Ikeda functions with a different parameter \(u\), combined with the logical XOR function. All program experiments described in this paper were carried out with double precision IEEE floating-point numbers [24]. It is possible to replace the XOR with feedback with a carry shift register (FCSR) which will randomly switch the Ikeda functions' output values.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
###### Abstract  
Fifth-generation (5G) networks are the fifth generation of mobile networks and are regarded as a global standard, following 1G, 2G, 3G, and 4G networks. Fifth-generation, with its large available bandwidth provided by mmWave, not only provides the end user with higher spectrum efficiency, massive capacity, low latency, and high speed but is also a network designed to connect virtually everyone and everything together, including machines, objects, and devices. Therefore, studies of such systems' performance evaluation and capacity bounds are critical for the research community. Furthermore, the performance of these systems should be investigated in realistic contexts while considering signal strength and restricted uplink power to maintain system coverage and capacity, which are also affected by the environment and the value of the service factor parameter. However, any proposed application should include a multiservice case to reflect the true state of 5G systems. As an extension of previous work, the capacity bounds for 5G networks are derived and analyzed in this research, considering both single and multiservice cases with mobility. In addition, the influence of different parameters on network performance, such as the interference, service factor, and non-orthogonality factors, and cell radii, is also discussed. The numerical findings and analysis reveal that the type of environment and service factor parameters have the greatest influence on system capacity and coverage. Subsequently, it is shown that the investigated parameters have a major impact on cell performance and therefore can be considered key indicators for mobile designers and operators to consider in planning and designing future networks. To validate these findings, some results are evaluated against ITU-T standards, while others are compared with related studies from the literature.  
capacity bounds; 5G networks; propagation environments; COST-231 Hata model; performance analysis +
Footnote †: journal: Applied Sciences
# Phase-Sensitive 2D Motion Estimators Using Frequency Spectra of Ultrasonic Echoes
## 2 Materials and Methods
### Estimation of Mean Frequency
To obtain cross spectra expressed by Equations (3)-(6) and (11), ultrasonic echoes in two frames, i.e., a frame set, are required. In the present study, as shown in Figure 2, the interval between two frames was set at 4, and the number of frame sets used for temporal averaging was also set at 4. If we assume that a typical target is the carotid arterial wall, its typical motion velocity is about 5 mm/s [37]. Under a condition of lateral and axial sampling intervals of 0.1 mm and 0.02464 mm, respectively, the frame rate required to avoid aliasing at the maximum frequency available in the frequency spectrum is \((5\,\mathrm{mm}/\mathrm{s})/(0.02464\,\mathrm{mm})=202.9\,\mathrm{Hz}\). In the present study, the pulse repetition frequency was 5208 Hz, and a frame interval of 4 corresponded to a frame rate of 325.5 Hz, which was higher than 202.9 Hz. Figure 1: Illustration of experimental setup. In the subsequent experimental validation, the estimator defined by Equation (15) with the lateral and axial angular frequencies determined by Equations (16) and (17) was called as the conventional 2D motion estimator. The estimator defined by Equation (10) with the lateral and axial angular frequencies determined by Equations (20) and (21) was called as the proposed paired 1D motion estimators. The estimator defined by Equation (15) with the lateral and axial angular frequencies determined by Equations (20) and (21) was called as the proposed 2D motion estimator.
# An Agent-Ensemble for Thresholded Multi-Target Classification
## 2 Problem Description  
We consider the binary classification problem where the positive class consists of \(L\) different target-types and the negative class consists of background clutter. We assume that we have either trained or are provided with \(L\) different classification agents, each of which is trained to distinguish between one of the \(L\) different target-types and clutter. Specifically, we assume that training for agent \(j\) uses target-type \(j\) exemplars as the positive class, clutter exemplars as the negative class and does not use target exemplars from any target class \(i\), \(i\
eq j\).  
When presented with a test feature vector, \(\mathbf{v}\), each agent \(j,\ j=1,...L\) provides as its output a real-valued score, \(x_{j}=g_{j}(\mathbf{v})\). The agent classification functions, \(g_{j}(\mathbf{v})\), should be designed such that one can infer a degree of confidence that \(\mathbf{v}\) belongs to target-type \(j\) vs. clutter from the output score. For example, the agents could be binary support vector machines or likelihood-based generative classifiers. We define the vector \(\mathbf{x}=[x_{1},x_{2},...,x_{L}]^{\mathsf{T}}\), where superscript T indicates matrix transpose, as the concatenated agent-scores for test sample \(\mathbf{v}\).  
We further assume that we are given a training dataset of score/label pairs \(\{(\mathbf{x}_{i},y_{i})\}\), where \(\mathbf{x}_{i}\in\mathbb{R}^{L}\) is the vector of agent score outputs and \(y_{i}\in\{-1,1,2,...,L\}\) is the corresponding class label, with \(-1\) indicating clutter and \(1,2,...,L\) indicating that the \(i^{\text{th}}\) sample is representative of the respective target-type. The purpose of this training data is to train a combiner function that takes the output of the \(L\) agents and produces a single score that we can threshold to classify the test sample as either target or clutter. We note that these training scores should be unbiased, i.e., computed via cross-validation or on a holdout set of data that is independent of the data used to train the agent classifiers.  
A combiner for the agent-classifiers produces a label output \(\hat{y}(\mathbf{x})\). Since the primary goal is to identify a test sample as target vs. clutter, the fusion combiner returns either \(\hat{y}(\mathbf{x})=1\) for target or \(\hat{y}(\mathbf{x})=-1\) for clutter. In order to focus on the low-PFA region that is of interest in alert-driven automation systems, the learning objective for the combiner maximizes a partial area under the curve (PAUC) measure [5; 6] that is limited to a low-PFA region of the ROC curve.
## 3 Optimized Joint Signal Processing Schemes  
In this section, we propose the optimized radar signal processing and communication signal processing schemes. At the radar receiving end, an improved target range and velocity estimation method based on information compensation and sparse signal reconstruction is introduced. At the communication receiving end, an improved low-complexity detection method based on sequential Monte Carlo is explored. Figure 3 shows the process of the proposed schemes. This figure displays the progressive relationship between different notations, which will be introduced in the next subsection.
# Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
## 3 Results
### Evaluation Indicators
#### 3.3.3 Root Mean Square Error  
Root mean square error (_RMSE_) is a widely used statistical measure for assessing the differences between predicted and actual values. It calculates the square root of the average of the squares of the differences between predictions and actual observations. By squaring the errors, _RMSE_ emphasizes the impact of larger errors, making it particularly sensitive to outliers:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}} \tag{16}\]
## 3 The Proposed Scheme
### Cycle Formation  
The main idea in CBDAS is to construct a cyclic chain by linking all cell heads so that sensed data can be disseminated in two directions along the chain to the BS. Once the cell head of each cell is determined  
Figure 1: The logical grid structure.  
for the first round, it starts finding its downlink cell head clockwise in the neighboring cell by using Algorithm 1. After determining its downlink, the cell head with cell ID [\(C_{x}\), \(C_{y}\)] sends a _Cycle_forming_ packet containing its own cell ID to the downlink. After receiving the packet, the downlink cell head saves the information and replies with a _Cycle_reply_ packet containing its own cell ID to the uplink cell head from which it receives the _Cycle_forming_ packet. Likewise, the uplink cell head saves the information in the _Cycle_reply_ packet. In this way, the cell head in each cell caches the information of its uplink and downlink. The cell heads of the whole network are finally linked together to form a Hamiltonian cycle, as shown in Figure 2. As a result, the data can be transmitted both clockwise and counterclockwise simultaneously in CBDAS.  
Figure 2: A cyclic chain.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction
### Uniform Distribution of End Nodes  
The assumption that an ADR scheme uses uniform distribution of the end nodes around the gateway and that all the end nodes can use all SFs and TPs suggests that all end nodes in the network are capable of reaching the gateway with every SF and every TP setting. This does not function in actual networks where specific end devices can only utilize a subset of the configuration parameters determined by the distance to the gateway. Further study could investigate randomly distributed end nodes.  
Different optimization models are implemented in the ADR schemes. Where machine learning algorithms are utilized, one could use fuzzy logic or mathematical optimization and vice versa. Algorithms that consider network congestion, packet collision probabilities, the use of multiple gateways could improve the ADR. Different propagation models, randomly distributed end nodes, packet loss ratio and non-orthogonality of SFs are issues to be considered in the models to improve performance.
## 1 Introduction
In this study, we analyze the effects of wind wave spectra, non-Gaussianity, and swell on the distribution of facet scattering predicted with TSM by comparing simulated results with the Sentinel-1 data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the incidence angles of 30\({}^{\circ}\)-50\({}^{\circ}\) over a wind speed range of 3-16 m/s and a wind direction range of 0\({}^{\circ}\)-360\({}^{\circ}\) under C-band VV and HH polarizations. We used the TSM to calculate the facet radar backscatter because the TSM has been the most popular analytical scattering model since it was first presented in the 1960s owing to its reliable precision and high efficiency. The remainder of this paper is organized as follows: Section 2 briefly introduces the reference data, the method used to construct the sea surface, and the model to calculate the facet scattering. In Section 3, we analyze the influence of wind wave spectra, non-Gaussianity, and swell on the performance of simulated facet scattering by comparing the simulated results with the reference data. The discussion and conclusions are presented in Sections 4 and 5, respectively.
## 3 Results
### Real-Time Static PPP Accuracy Analysis
This shows that the introduction of real-time GLONASS/BDS/Galileo multisystem PPP can effectively improve the convergence speed of real-time PPP and improve the overall positioning accuracy of real-time GPS PPP. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & **E** & **N** & **U** & **Convergence** & **E** & **N** & **U** & **Convergence** \\  & **(cm)** & **(cm)** & **Time (min)** & **(cm)** & **(cm)** & **(cm)** & **Time (min)** \\ \cline{2-8}  & \multicolumn{4}{c}{**During Rainfall**} & \multicolumn{4}{c}{**During Nonrainfall**} \\ \hline HKSL & 1.94 & 0.77 & 2.34 & 47 & 1.38 & 0.88 & 1.88 & 37.5 \\ HKWS & 1.43 & 0.83 & 2.4 & 78 & 1.31 & 0.83 & 2.06 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Real-time GPS PPP error and convergence time. Figure 3: (a) Real-time static GPS PPP error; (b) Real-time static multi-GNSS PPP error.
## 4 Examples of Critical Constructability Features in Facade Panels of the Orchard Commons Project
### Feature 2: Inclined Straight Edges  
**Constructability Constraints and Experts' Knowledge:** One important design goal in the OC project was to \"do more with less\". This entailed offering a high degree of visual diversity while minimizing the complexity. As previously mentioned, the original design suggested facade panels with curved edges. However, fabricating curved concrete units necessitates specialized custom formwork and a high degree of precision. This makes the fabrication of such panels both costly and susceptible to errors.  
Generally, using standard shapes is a common practice in the construction industry because they streamline the associated construction tasks, consequently reducing costs. Regardless of a component's dimensions, its unique shape might necessitate specialized fabrication processes, such as for formwork, assembly, and so on. Moreover, laborers require additional time to familiarize themselves with unconventional shapes. This aspect could impact both the fabrication and installation processes. For all these reasons, the practitioners on the OC project recommended avoiding curvatures for the facade panels altogether and pursuing a solution with inclined straight edges on the left and right side of the panels.  
**Constructability Feature and Computational Implementation:** To realize this constructability feature, the computational designer delineated a specific range of variations for edge inclinations, termed as amplitudes, and optimized them to fulfill the design stipulations, as illustrated in Figure 5. Through these variations, multiple design options with diverse panel shapes could be reviewed, granting the client and designers the freedom to select a fitting design alternative.
## 7 Discussion and Future Work  
### A Suitable Reference Definition  
During the processing of dynamic surveys, we realized that the reference trajectory actually looked noisier than the one from the devices. The reference trajectory was computed in DGNSS laser/rover setup, as described in Section 3 and DGNSS computations were performed using the open-source software RTKlib. Even with DGNSS, the errors of the reference rise significantly in challenging environments, due to the limited number of satellites visible and multipath errors. Consequently, the statistics tables of position errors presented in the appendices need to be reviewed with this consideration in mind.  
Nevertheless, we do not attribute such error to the receiver, but to the processing itself. As navigation algorithms inside Android devices are proprietary, it is difficult to assess the filtering algorithms and parameters. Consequently, simple DGNSS processing as performed in RTKlib is not sufficient and more advanced processing should probably be performed on the reference data to better assess the positioning capacities of each device. In our next research developments, we are planning to integrate the positioning processing (i.e., Kalman Filter) within the MimirAnalyser software to improve the reference trajectory processing.  
Moreover, the dataset would benefit from an INS coupled with the reference receiver setup. It would enable better processing for the reference trajectory, and the INS measurements could be compared against the INS recordings from the smart devices. However, INS methods require alignments of the different devices' frames, which could prove challenging. It is the main reason why using a combined GNSS/INS solution for the reference trajectories was left out of this analysis. Future work should look into the feasibility of enhancing the reference trajectories with INS measurements.