esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Context and Objectives  
Measurements of the radioelectric spectrum were carried out at the Centro Hospitalar Universitario Cova da Beira (CHUCB) to evaluate the quality of the signal available in different areas of the hospital and its surroundings, as well as identifying zones with coverage deficiencies that could compromise the effectiveness of WBANs and other wireless communication-dependent systems [1]. This study was motivated by the need to explore possibilities for providing ubiquitous cellular coverage in the hospital and surrounding context, thus ensuring that all sectors of the hospital, especially the critical ones, have ubiquitous coverage that guarantees the continuity of operations and the safety of patients [13].
## 2 Materials and Methods
### Methods
#### 2.3.6 Quantitative Analysis of Classified Areas  
After the image classification process, each pixel was assigned a value to one of the four predefined classes, where the value 1 corresponds to water, 2 to urban area, 3 to bare soil, and 4 to vegetation. The quantitative analysis was conducted by summing the pixels of each class for the analyzed years. The PlanetScope satellite images have a spatial resolution of 3 \(\times\) 3 m, which implies an area of 9 m\({}^{2}\) per pixel. To facilitate quantitative analysis, the total area of each class was converted from m\({}^{2}\) to km\({}^{2}\), allowing for a more accessible comparison of classified areas between the years 2018 and 2023.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 6 Conclusions  
In this study, a fault location and service restoration algorithm was developed and integrated for three-phase short-circuit fault detection, isolation, and restoration in MGs. The proposed approach is based on a fast-computing algorithm and the measured data from the EMS of MGs. Therefore, graph theory is used to establish _BIBC_ and _BCBV_ matrices for the corresponding network topology. The bus impedance matrix is effective and easy to build and modify for pre-fault and post-fault analyses, without the need for the time-consuming rebuilding of a bus impedance matrix due to topology changes. Consequently, the bus impedance matrix is convenient to use in pre-fault power flow analysis and the calculation of post-fault bus voltage change caused by the fault current contributed by the upstream power gird and DGs. The multi-objective function, which is composed of load shedding, switch operations, and power loss with voltage drop and ampere capacity constraints, is proposed for service restoration. These algorithms are implemented by PSO for fault location and optimal service restoration. The MG modified from the IEEE 37-bus was used as the sample system to demonstrate the feasibility of the proposed approach. The proposed approach was coded using MATLAB and executed on a Windows 10 Intel(r) Core(tm) i7-10700F CPU @2.9 GHz personal computer, and the average computing time of each scenario of the IEEE 37-bus was less than 1 s. The numerical results illustrate a maximal service restoration percentage with minimum switch operations and power loss under both operating modes. The outcomes show that the proposed approach is capable of detecting, isolating, and restoring three-phase short-circuit faults under the grid-tied and islanding operating modes of MGs.  
The original idea for the proposed algorithm for fault location and service restoration in MGs was presented by [PERSON], [PERSON]. The service restoration algorithm was coded by [PERSON]-C.L., and the fault location algorithm was coded by [PERSON]-C.M. The simulation scenarios and results were checked by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors are grateful for financial support from the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2221-E-018-004-MY2, and the Institute of Nuclear Energy Research of Taiwan through its grant no. 110A007.  
The authors declare no conflict of interest.
# RIS-Assisted Cooperative Time-Division Multiple Access
## 4 Conclusions  
This study used a RIS for cooperative transmission to maximize the number of UEs unsatisfied with the QoS in the downlink TDMA system. The simulation and numerical results show that the proposed RC-TDMA guarantees QoS for most UEs compared with a conventional TDMA in a moderate SNR range where UEs that are satisfied with QoS and UEs that are not satisfied are mixed. In addition, from the viewpoint of power efficiency, it was confirmed that effective capacity was high when power consumed in the RIS was lower than that of base stations. Therefore, it was demonstrated that RISs can be fully utilized to ensure QoS without optimally adjusting the phase shift values for passive reflecting elements.  
Conceptualization, H.S.; methodology, B.K.; formal analysis, H.S.; software, H.S. and B.K.; validation, H.S. and B.K.; writing--original draft preparation, H.S.; writing--review and editing, B.K.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1051075).  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)
#### 2.3.4 Scenario 4. Cruise Ship  
The simulation test scenario assumed that experts would perform a series of simulation trials consisting of the safe passage of the cruise ship Lixica along the determined section of the Swinoujscie-Szczeccin fairway. The ship passage consisted of the withdrawal of the ship from Kanal Grabowski to Przesmyk Orli turning basin, starboard turn and entry into a straight fairway section called Przekop Mielenski, towards the port of Swinoujscie (Figure 6).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Displays** & **Series** & **Number of Trials** \\ \hline Visual display & 3\_Visual & 12 \\ ECDIS & 3\_ECDIS & 12 \\ Pilot\_max & 3\_Pmax & 12 \\ Pilot\_precise & 3\_Pprecise & 12 \\ AV & 3\_AV & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Display types and number of trials for the sea ferry scenario.  
During the experiment, the navigator was required to achieve the lowest possible value of the distance from the ship's hull to the east shore of Gryfia Island. The maneuver was necessary to keep the ship's stern away from Wielka Kempa island as adjacent waters are not navigable.  
The detailed assumptions of Scenario 4 were as follows:  
* Objective to be achieved: the lowest possible value of the distance from the ship's hull to the east shore of Gryfia Island.
* Main ship parameters:  
* LOA = 251.25 m, breadth = 28.8 m, draught = 6.84 m;
* Displacement = 59,058 t;
* Block coefficient = 0.649;
* Main engine: 4 \(\times\) diesel, total power = 31,500 kW;
* Max speed = 22.5 kn;
* Type of propulsion: 2 \(\times\)cpp, outwards;
* Number of rudders: 2;
* Max rudder angle = 45.0 deg;
* Number of thrusters: 4;
* Bow thruster power = 4200 kW;
* Stern thruster power = 2800 kW.
* Navigational-maneuvering conditions:
* the initial position of the ship: Kanal Grabowski, next to Przesmyk Orli turning basin;
* the ship's final position: Przekop Mielerski, as far as Trymerski Basin towards Swinoujscie;
* initial SOG: longitudinal astern v\({}_{\rm w}\) = 4.0 kn, transverse v\({}_{\rm P}\) = 0.0 kn;
* heading: HDG = 228\({}^{\circ}\).
* The criteria used for assessment:
* Primary: the value of the minimum distance of ship's hull from the navigational obstruction (east shore of Gryfia Island);
* Secondary: efficiency of maneuvering--time length of maneuvers.
* The moment of measuring significant parameters: after the passage ends, the least recorded value of the cruise ship distance from the east shore of Gryfia Island, determined from a single passage.  
The types of displays used, the name of the series and the number of trials in a given series of the Scenario 4 are presented in the Table 5.  
Figure 6: The situational-navigational plan of Scenario 4 for cruise ship passage. Red—initial position, black—destination.
## 2 Materials and Methods
### YOLOv11-OAM Architecture Overview
#### 2.4.1 ODConv Module  
Omni-Dimensional Dynamic Convolution (ODConv) [33; 34] is a dynamic convolution mechanism designed to improve the adaptability of feature extraction layers. Conventional convolutions use fixed kernels across all inputs, which limits their ability to capture diverse spatial patterns and object structures--particularly in UAV imagery with varying canopy textures and target densities. ODConv addresses this limitation by generating content-adaptive kernels along four dimensions: spatial position, input channel, output channel, and kernel index.  
Unlike earlier dynamic convolutions such as CondConv and DyConv, which reweight kernels along a single dimension, ODConv applies attention across four orthogonal axes. In this way, it generates context-aware convolutional weights tailored to the input feature map. This enables the simultaneous capture of fine-grained local details and global semantic context.  
As illustrated in Figure 5, ODConv first applies global average pooling to extract contextual information. Four parallel attention branches--channel, output channel, spatial, and kernel--then produce multi-dimensional attention weights. These weights are used to construct a dynamic convolutional kernel, which is subsequently applied through standard convolution to generate spatially adaptive feature maps.  
In YOLOv11-OAM, ODConv modules are selectively integrated into the middle and deep stages of the backbone network, where high-level semantic and spatial features are extracted. By dynamically adapting convolutional behavior to local context, ODConv enhances the representation of irregular canopy structures, blurred object boundaries, and inter-class ambiguity--common challenges in UAV orchard imagery. Prior studies have likewise shown that dynamic convolution can improve detection robustness in remote sensing small-object tasks under dense and overlapping conditions [45]. This integration therefore offers stronger capability for distinguishing morphologically similar species in complex orchard environments.  
Figure 5: Structure Diagram of the ODConv Module. (Note: The arrows indicate the flow of information, while the circles denote element-wise operations (“\(\times\)” for multiplication and “+” for addition). The colored blocks (blue, gray, green, pink, and brown) are used only for visual distinction; they all represent the same type of weight/attention factors).
## 3 Deep-Learning-Based Channel Estimation Methods
### Overview of Neural Networks  
As a branch of artificial intelligence technology, deep learning uses neural networks as models, so it is necessary to briefly introduce neural networks before introducing deep-learning-based channel estimation methods. Neural networks, which mimic the neuronal network of the human brain, consist of input, hidden, and output layers with numerous interconnected neurons through the weights in each layer. By adjusting these weights through learning, neural networks can process input information and produce output. According to the application of deep learning in various fields, the structure of the basic network is different, primarily including the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), etc. Most deep learning models can be combined or optimized based on these four networks. Next, we will introduce these common neural networks.
## 3 Results and Discussion
### Results for Time Domain  
Transient responses are here intuitively introduced to the observed dispersion characteristics of guided waves in the liquid-filled pipe system. Although the excitation source has a certain effect on the time-domain waveform, the inherent characteristics of the waveform are independent of the source. We use the cosine envelope pulse function as the excitation source, as the pulsed sound source represents a non-directional and isotropic monopole point sound [45]. In this study, we assume an acoustic point-source at the origin of the coordinates, and the point-source function is  
\[x_{0}(t)=\left\{\begin{array}{l}\frac{1}{2}\Big{[}1+\cos\frac{2\pi}{T_{c}}( t-\frac{T_{c}}{2})\Big{]}\cos 2\pi f_{0}(t-\frac{T_{c}}{2}),(0\leq t\leq T_{c})\\ 0,(t\leq 0,t\geq T_{c})\end{array}\right. \tag{36}\]  
Its transform is given by  
\[X(f)=\frac{1}{4\pi}A(f)e^{\mathrm{i}nfT_{c}} \tag{37}\]  
where \(T_{c}\) is the pulse length (\(T_{c}=0.5\) ms), and f0 is the frequency of the sound source (\(f_{0}=25\) kHz). The Fourier-Bessel and Hankel transforms are frequently used as a tools for solving numerous scientific problems, and have become highly useful in the analysis of the wave field. The \(u_{FE}\) and \(u_{rPS}\) can be solved via (4) and (18). The two-dimensional Fourier transform of \(u_{r}\) can generate the radial displacement time-domain curve.  
The dispersion curves of guided waves in the liquid-filled pipe embedded in infinite and finite porous media are illustrated in Figures 2 and 3, respectively. Figure 2 shows only two modes, \(\alpha\) and L(0,1)\({}_{E}\), at the frequency 25 kHz. Figure 4 indicates three modes, \(\alpha\), L(0,1)\({}_{E}\) and L(0,1)\({}_{P}\), when the frequency increases from 0.02 to 0.06 MHz. However,  
Figure 6: Dispersion curves of guided waves in water-filled pipe embedded in (**a**) infinite porous medium, and (**b**) finite porous medium. The thickness of the finite porous medium is \(d=0.05\) m. Porosity is \(\beta=0.1\). Red and blue lines represent the different pore fluids of water and engine oil, respectively.  
there are more than three different modes at any frequency when the frequency increases from 0.06 to 0.5 MHz, and the character of each mode is unique. To provide an illustrative example, we consider the pipe to be infinite, and analyze the time-domain waveform with regard to the signal acquisition, porosity, and thickness of the finite porosity medium layer. In this subsection, the acquisition locations are at the liquid-pipe interface in the infinite porous medium and at the external interface of the finite porous medium layer, which are 3 m away from the excitation source.
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
## 4 Observation Data Sets and Detection Results
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Receiver Manufacturer**} & **Number of Station Files** & **Number of RINEX-2 Files with** \\  & **Processed** & **L2 Quarter Cycle Problem** \\ \hline AOA & 1 & - \\ Ashtech & 34 & - \\ CHC & 2 & - \\ ITT & 15 & - \\ Javad & 110 & 19 (17.3\%) \\ JPS & 9 & - \\ Leica/NovAtel & 686 & 37 (5.4\%) \\ Septentrio & 168 & - \\ TPS & 111 & 16 (14.4\%) \\ Trimble/Champion & 1488 & - \\ \hline Sum & 2624 & 72 (2.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection Results by Receiver Manufacturer. Figure 5: Geographical distribution of stations providing RINEX-2 files with L2 quarter cycle problems.
# Population Bias on Tornado Reports in Europe
## 2 Tornado and Population Datasets  
Tornado reports were obtained from the European Severe Weather Database. The ESWD collects information on severe storms over Europe (i.e., tornadoes, severe wind, large hail, heavy rain, heavy snowfall, damaging lightning) using a citizen-science approach [9] and through collaborations with national weather services and volunteer severe weather spotter networks. Before the inclusion in the ESWD, each report is verified and receives a quality control level. In this article, tornado reports with a quality control level Q0+ (i.e., validated with meteorological data such as radar and/or satellite imagery) have been used. Unlike the United States tornado database that contains only reports for tornadoes, the ESWD also contains reports for waterspouts [2]. The ESWD include information about the surface type (e.g., land, forest, sea, lake) over which tornadoes have been observed and the surface types crossed during the event. Thus, all the waterspouts that moved inland were included in the analyses presented in this article.  
Data on population density in Europe were obtained from Eurostat [10]. The data were extracted for NUTS3 regions. NUTS (Nomenclature of territorial units for statistics) classification is a system for dividing the economic territory of European Union and the United Kingdom. The NUTS 2021 [11] classification valid from 1 January 2021 contains 1166 regions at NUTS3 level. NUTS3 level represent small regions for specific diagnoses. For example, for Romania there are 42 NUTS3 regions (41 counties and Bucharest, the capital city). In previous studies, for the United States, was argued that the rural population density at the county level is a more appropriate measure for tornado reporting compare with total population density [12]. Here, we follow [6] and use the total population density as population tends to be distributed over much of the counties and not concentrated in isolated towns. Based on the data availability and overlap with the tornado dataset, for each NUTS3 region the population density was average for the period 2006-2019 and the area for the period 2006-2015. These data together with the number of tornadoes reported between 2006-2020 at NUTS3 level were included in the Bayesian model.
## 5 Conclusions  
This study analyzed actual signal in space data, signals, and measurements for GPS and BDS. In order to remove undesired data and provide more accurate positioning results for both systems, each QA established the different statistical result. The satellite observations are independent for each analysis, with different means and standard deviations. The threshold is derived from the Gaussian distribution, which approximates the distribution of satellite observations. In order to establish a suitable threshold for each analysis, the threshold is defined as \(\mu+k\sigma\), where \(\mu\) and \(\sigma\) are the mean and  
Figure 24: Pseudorange residuals for all BDS satellites.  
Figure 25: BDS satellite orbit error (compared with precise orbit correction).  
standard deviation of the established statistical results, respectively, and \(k\) is a value that can be defined by the user for different positioning applications. By using satellites that pass each QA's threshold, unusual measurement errors can be avoided.  
Three quality analyses were used to determine the performance difference between GPS and BDS. More than one month of GNSS data were used for verification. It was found that the performance of BDS is almost the same as that of GPS most of the time. BDS provides more stable code-phase measurements and signal strength than those of GPS. However, sometimes BDS broadcasts irregular ephemeris and almanac data information. The satellite orbit and clock errors cannot be determined using a conventional GNSS receiver. The proposed quality analyses can be used to detect these anomaly errors. Finally, the suggestions given in this study will facilitate the integration of GPS and BDS.  
This work was supported by the Taiwan Ministry of Science and Technology under research grant NSC 101-2628-E-006-013-MY3. Authors gratefully acknowledge the support.  
In this research article, all contributions were equally made by all the authors.  
The authors declare no conflict of interest.
## Appendix A
### TCN Model Definition  
Analogous to an MLP \(f(X\in\mathbb{R}^{N_{0}};L,\theta)\), a TCN \(f(X\in\mathbb{R}^{N_{0}\times T_{0}};L,K,D,\theta)\) consists of \(L\) hidden layers referred to as _temporal blocks_, which are composites of element-wise activation functions \(\phi\) and dilated convolution operations as explained below. The input datum \(X\) is an \(N_{0}\)-variate tensor of time sequence length \(T_{0}\). Each temporal block composite function \(\psi_{l}\) contains an element-wise activation function \(\phi\) applied after a convolution operation. The basic temporal block contains an affine operation that convolves some kernel \(W\) of size \(K\) and dilation factor \(D\) striding over the input nodes \(X_{l-1}\). This is said to be a _dilated convolution of factor \(D\)_. Kernel strides are convolved along the discrete-time dimension \(t\in T,T\in\mathbb{N}\). Temporal blocks may also contain stacks of inner blocks consisting of dilated convolution and activation operations (see Figure A3 below). The diagram below shows two examples of dilated convolutional operations. Both are dilated kernels of kernel size \(K=2\) and convolved with stride \(s=1\). The left shows a kernel of dilation \(D=1\) over an input time series of size \(T\in\mathbb{N}\) reducing the layer output to size \(T-1\). The right shows a kernel of dilation \(D=2\), reducing the layer output to size \(T-2\). Dilated convolution operations are autoregressive. Let \(W_{*D}^{(l)}\) be a dilated kernel of size \(K\) and dilation factor \(D\) in some TCN layer \(l\). Then, the output of a dilated convolution operation \({}_{*D}\)[12] applied on \(X\in\mathbb{R}^{N_{l-1}}\times T_{l-1}\) across input dimension index \(m=1,\ldots,N_{l-1}\) yielded at time index \(t\leq T\) is given by:  
\[\left(W_{*D}^{(l)}X\right)_{m,t}:=\sum_{l=1}^{K}\sum_{j=1}^{N^{(l-1)}}W_{i,j,m }\cdot X_{j,t-D(K-i)} \tag{10}\]  
Figure 11 below demonstrates the application of the dilated convolution operation of kernel \(W_{*D}\) on input matrix \(X\in\mathbb{R}^{N_{l-1}\times T_{l-1}}\) applied for \(K=2,D=2\), and unit stride. This produces an activation tensor \(A\in\mathbb{R}^{N_{l}\times T_{l}}\) where the time dimension \(T_{l}=T_{l-1}-D(K-1)\) before element-wise activation function \(\phi\) is applied. Indeed, time series index \(t\in\mathbb{N}\) of the output from a dilated convolution operation only incorporates the input from the receptive field of past time series indices \((t-s)\), \(s\geq 0\), \(s\leq t\), \(s\in\mathbb{N}\). This yields a receptive field size (RFS) of \(s+1\), inclusive of endpoints. Thus, only past information is propagated through the TCN network to learn time-lagged feature representations at different scale resolutions. A temporal block may contain one or more 'inner blocks' of composite activation and dilated convolution layers. Define a _temporal block_ function for layer \(l\), \(\psi_{l}\) with parameters \((N_{l-1},N_{l},S_{l})\).