esa-sceva/satcom-chunk-collection huggingface dataset:
# An Indoor DFEC Ranging Method for Homologous Base Station Based on GPS L1 and BeiDou B1 Signals
## 3 Implementations and Evaluation  
In this section, we will use the existing test environment to verify the feasibility of the ranging principle. By using a dual-frequency pseudo-satellite system to broadcast the frequency of B1(1575.42 Mhz) and L1 (1561.098 Mhz) pseudo-satellite signals indoors, use mature commercial receiver ulox to carry out indoor signals carrier phase data collection, and use the obtained carrier phase value to carry out ranging experiments. The whole experimental process is mainly considered from three aspects: first, whether each switch on and off has an impact on the estimation of whole cycles; second, whether the internal error of the receiver affects the estimation of the whole cycles after each cold start; if the above two points have no impact, then we need to further verify how much the environmental interference has affected the whole cycles estimation. After completing these three steps of verification, we can determine whether this method can actually be used for ranging indoors, so the entire experimental process involves two parts. The first part is to connect the pseudo satellite base station to the ulox receiver by wire, and using the u-center receiver to receive and output the observation data. Then use MATLAB program for analysis. Here, in order to complete the verification quickly, the existing analysis algorithm is used, and it is judged by analyzing the whole cycles estimate of the distance difference between the channels. If the value is consistent each time, it can be considered that the ranging method is feasible. The same analysis algorithm is also used in subsequent wireless environment data analysis. The second, by using the existing test environment, multiple typical feature points are selected in this environment, and then the feasibility and fitness of the method at different feature points are analyzed. The specific settings are described in the following sections.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 4 Methods  
### AD-FG Framework  
The overall framework of AD-FG is illustrated in Figure 1. During the local learning phase, each client first converts its local network traffic data into graph-structured representations using a graph construction module. Within the federated collaborative learning module, a dual-channel GNN equipped with decoupled structural and attribute encoders is locally trained to enable parallel learning of structural and attribute knowledge. A cross-gated feature fusion mechanism is employed to integrate node-level information into a global graph-level representation, which is then used for classifying the network traffic graphs. Notably, only the parameters of the structural encoder are uploaded to the server, while node attribute information remains strictly local and is never shared. In the cross-domain knowledge learning phase, the server aggregates the uploaded structural encoder parameters and leverages knowledge from public domains to learn generalized structural features, thereby forming an enhanced global structural model. This updated model is redistributed to the clients in the next round of communication.
## 2 Main Subject
### Sensor System for Active Driving Assistance  
#### 2.1.1 UWB  
UWB is an ultra-wide wireless communication technology that enables distance measurement and data transmission and reception over a bandwidth of 500 Mhz or more. It is possible to measure distance through the transmission time or transmission time difference information rather than the existing RSSI method of measurement, and it is easy to use in various environments due to high obstacle permeability because it uses a measurement method through radio waves. However, when estimating positions through a single UWB sensor, the accuracy is not high because of the problem of strong outliers, due to the influence of multiple paths caused by diffraction and reflection of radio waves and noise through the transmission and reception of radio waves. Therefore, to solve this problem, various filters such as EKF are used to increase accuracy by removing outliers and noise [31; 32]. However, the single sensor-based method has difficulty correcting data distortion through obstacles during driving, and it is difficult to use as a driving assistance system because delays occur when suppressing noise strongly to increase accuracy. Therefore, in this study, a UWB sensor-based position and direction estimation system suitable for a wheelchair environment is proposed. In order to solve the problem of a single UWB sensor, a position and direction estimation system for driving assistance is designed, entailing position estimation using multiple sensors rather than position estimation through a single sensor.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments  
To validate the effectiveness of the VEPO model, we conducted a series of quantitative and qualitative experiments. Specifically, we first introduce the experiment settings, including the hyperparameter settings, experimental environments, datasets, baseline models, and evaluation metrics. Subsequently, we present the quantitative comparison results of our proposed method and other baseline models. Following this, we describe the ablation experiments conducted to substantiate the effectiveness of different components of the model. Finally, we depict the prediction results of VEPO-S2S through qualitative analysis.
# An Improved Dynamic Joint Resource Allocation Algorithm Based on SFR
## 3 Proposed Algorithm
### Complexity Analysis  
Firstly, we analyze the computational complexity of resource allocation for a single cell. Since there are \(N-k_{i}+1\) different combinations for (\(N_{i}^{major}\),\(N_{i}^{\min{or}}\)), DJRA needs traverse \(N-k_{i}+1\) to obtain the power set in Step 1. Similarly, Step 2 takes at most \(N-k_{i}\) operations to assign the remaining powers to edge users. Therefore, the single cell resource allocation process will take \(L^{{}^{\prime}}=L_{1}+L_{2}\) operations in one cell per iteration. Step 3 only requires one process, so the complexity for Step 3 is of the order of \(L_{3}=O(1)\). So far, we completed the complexity analysis in a single cell.  
We define \(\tau\) as the number of iteration and the computational complexity of interior sector partition is of the order of \(O(N)\). Based on the above analysis, the multi-cell computational complexity of DJRA is  
\[L=\tau CL^{{}^{\prime}}+O(N)=O(\tau CN)+O(N) \tag{21}\]  
In the traditional static resource allocation algorithms, the major/minor subcarrier and power allocations per cell are set prior to network deployment. Therefore, the computational cost for these algorithms is of the order of \(O(1)\).
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 2 Overview of Radio Wave Propagation for 6G Systems
### Indoor Channel Characteristics
At three different bands of 30, 140, and 300 GHz, the close-in free space reference distance (CI) model, floating-intercept (FI) model, alpha-beta-gamma (ABG) model, and close-in frequency dependent (CIF) model were investigated, and the results showed that the ABG and CIF models were more stable than the CI and FI models [31].
introduced PSYOLO, reducing parameters by half while improving both accuracy and speed [29]. Additional works, including RLRDYOLO [30] and CFYOLO [31], also enhanced YOLOv8/YOLOv11 for small-object UAV detection. Collectively, these studies demonstrate the promise of YOLOv11 for orchard applications. Nonetheless, its default configuration continues to face challenges in detecting small targets, handling severe crown overlap, and distinguishing subtle class boundaries in complex environments, highlighting the need for further improvement. Comparable visual challenges have also been explored in other domains, such as medical image analysis. For example, the vSegNet model [32], originally developed for medical image segmentation, integrates multi-scale feature fusion with structure-aware attention to enhance boundary precision under class imbalance and limited data. Although intended for a different application, its architectural principles offer valuable insights for small-object detection and inter-class differentiation in dense UAV imagery. Building on these considerations, this study proposes YOLOv11-OAM, an enhanced one-stage detector tailored for orchard tree species recognition. The model extends the YOLOv11 framework with three innovations. First, Omni-Dimensional Dynamic Convolution (ODConv) [33, 34] improves spatial adaptability under heterogeneous canopy structures. Second, Adaptive Spatial Feature Fusion (ASFF) [35, 36] enables effective multi-scale semantic integration for densely distributed small objects. Third, Multi-Point Distance IoU (MPDIoU) [37] provides a geometry-aware loss for refined localization, introducing a multi-anchor discrepancy measure that substantially improves fine-grained localization where traditional IoU metrics often fail due to blurred boundaries and scale variability. In addition, a class-balanced data augmentation strategy is employed to mitigate category imbalance and enhance generalization. Taken together, these innovations establish a detection framework specifically designed for UAV orchard imagery, aimed at addressing spatial complexity, high inter-class similarity, and small-object detection challenges. To promote reproducibility and further research, the full dataset, annotation files, and implementation code have been publicly released on Zenodo: [[https://doi.org/10.5281/zenodo.15654967](https://doi.org/10.5281/zenodo.15654967)]([https://doi.org/10.5281/zenodo.15654967](https://doi.org/10.5281/zenodo.15654967)) (accessed on 13 June 2025).
# Preparation of Few-Layer Graphene Dispersions from Hydrothermally Expanded Graphite
## 3 Results and Discussions
Probably, the small size and folded edge of graphene layers (Figure 2d-f), contributes significantly to the observed D peak, although we cannot completely rule out the presence of in-plane defects. The structure of G peak observed in the dispersed graphene at \(\sim\)1572 cm\({}^{-1}\) appears to be slightly affected by the presence of D' peak, however, the D' peak usually is present when there are surface defects, such as charging or other impurities adsorbed onto the surface [31]. The latter here is attributed to the remaining surfactant as observed in Supplementary Figure S3 at the highest concentration of CTAB, which strongly confirms the need to use our method at low concentrations of surfactant. Interestingly enough, the D and G (G and D') peaks of our graphene sample are well separated from each other and are distinctly different from those of graphene oxide, in which case the D and G peaks are broad and overlap. This fact suggests that our dispersed graphene did not undergo severe in-plane disruption as in the case of graphene oxide [30]. The peak is slightly broader between 2625 and 2700 cm\({}^{-1}\) and it is commonly used to estimate the number of layers in obtained graphene [32]. However, the intensity of the 2D peak depends on the excitation laser frequency, and therefore cannot be solely relied upon [31]. In this context, we use the full width at half maximum (FWHM) to evaluate the 2D peak, which is found to be a qualitative guide to distinguish the number of layers from single- to five-layer graphene [33]. By fitting the 2D peak with two Lorentzian functions, the intensity of the 2D\({}_{\text{1A}}\) peak (detected at \(\sim\)2683 cm\({}^{-1}\)) increases in dispersed graphene, with respect to the intensity of the 2D\({}_{\text{1A}}\) peak (at \(\sim\)2676 cm\({}^{-1}\)) in pristine graphite. Most importantly, in Supplementary Figures S4 and S5, we report the FWHM of 2D\({}_{\text{1A}}=~{}68.3~{}\pm~{}1.6\) cm\({}^{-1}\), 2D\({}_{\text{2A}}=~{}36.9~{}\pm~{}1.0\) cm\({}^{-1}\), and 2D \(=~{}68.7~{}\pm~{}1.1\) cm\({}^{-1}\), respectively. These values are in good agreement  
Figure 2: Scanning electron microscopy (SEM) morphology of pretreated natural graphite in (**a**) 1.0 mg mL\({}^{-1}\), (**b**) 0.5 mg mL\({}^{-1}\), and (**c**) 0.1 mg mL\({}^{-1}\) of CTAB aqueous solution. (**d–f**) Semitransparent layers obtained from supernatant after 2000 rpm centrifugation for 90 min, subject to 3 h of sonication in the three different CTAB concentrations. with previous reports for tri-, four-, and five-layer graphene [33]. Furthermore, the 2D peak became more symmetrical in dispersed graphene, after HT and sonication, indicating the presence of FLG in obtained graphene dispersions.
## 2 Status and Literature Review
### Review of Design-for-Safety
\begin{table}
\begin{tabular}{c c c} \hline
**Country** & **Name** & **Criteria** \\ \hline \multirow{2}{*}{Korea} & \multirow{2}{*}{Design-for-safety (DIS)} & Design-for-safety Checking Manual (2017) [9] \\  & & Case study on Design-for-Safety Review Report in 2018 (2019) [10] \\ \hline \multirow{2}{*}{U.S.A.} & Prevention through Design (PJD) & Leadership in Energy and Environmental Design (LEED) Credit, [11] \\  & Design for Construction Safety (DKS) & Sustainable Construction Safety and Health (SCSH) Rating System (2006) [12] \\ \hline \multirow{3}{*}{U.K.} & \multirow{3}{*}{Construction Design Management (CDM)} & Workplace health, safety and welfare (1992) [13], \\  & & Managing health and safety in construction (2015) [14], \\  & & Health and safety in construction (2006) [15], \\  & & Construction Phase Plan (CDM2015) [16] \\ \hline \multirow{3}{*}{Singapore} & Design-for-safety, & Guidelines on Design-for-Safety in Buildings and Structures (2008) [17], \\  & GUIDE Process, & Workplace Safety and Health Guidelines: Design-for-Safety (2016) [18], \\  & Construction Safety Audit Scoring System & A Guide to the Construction Safety Scoring Audit System (2013) [19], \\ \cline{1-1}  & (ConSAS) & Construction Safety Audit Scoring System Audit Checklist (2007) [20] \\ \hline \multirow{2}{*}{Hong Kong} & \multirow{2}{*}{Design-for-Safety} & Guidance notes of Design-for-Safety (2013) [21], \\  & & Worked examples of Design-for-Safety (2013) [22] \\ \hline \multirow{2}{*}{Australia} & \multirow{2}{*}{Safe Design} & Guidance on the principles of safe design for work (2006) [23], \\  & & Safe design of structures code of practice (2014) [24] \\ \hline \end{tabular}
\end{table} 
Table 1: Design-for-Safety review cases.
# A Capillary-Based Micro Gas Flow Measurement Method Utilizing Laminar Flow Regime
## 1 Introduction
Consequently, 21 st-century research has prioritized minimizing nonlinear error impacts. In 2010, [PERSON] et al. designed a laminar flowmeter with three pressure tapping points, selectively utilizing differential pressure from capillary tube ends at low flow rates and from downstream sections at higher rates, effectively reducing nonlinear error contributions. [PERSON] et al. enhanced linearity by implementing a dual-channel cross-symmetric laminar element configuration, mitigating inlet/outlet localized losses and nonlinear pressure drops in the capillary entrance region. Concurrently, [PERSON] et al. developed a novel micro slit laminar flowmeter that circumvents nonlinear effects by strategically positioning pressure taps within fully developed flow regions [16; 17; 18]. Building upon these advancements, this study proposes a micro gas flow measurement method employing a single capillary laminar flow element. The objective of this method is to simplify the structural complexity of measurement devices in the low micro flow regime (1-10 cm\({}^{3}\)/min) and reduce nonlinear errors, thereby enhancing measurement accuracy. This paper is mainly divided into five main chapters. The Section 1 is the Introduction, which introduces the origins and development history of laminar flow measurement devices, summarizes the limitations of existing laminar flowmeters, and elicits the research content of the paper. The Section 2 is Materials and Methods, which analyzes the principles underlying the limitations of laminar flowmeters, the structural parameters of the new laminar flow measurement device designed in this study, the establishment of error correction methods, and the construction of the experimental platform. The Section 3 is Results, which mainly presents the experimental results and data. The Section 4 is Discussion, which focuses on explaining the processing and analysis of the data. The Section 5 is Conclusions, which mainly summarizes and concludes the research content.
## 3 Materials and Methods
### Metal Leaching  
As previously mentioned, prior to metal leaching the samples were milled. This facilitated cleavage of the chemical and mechanical bonds between the Ag and Al electrodes and the Si layer, and made the surface area for the reaction larger. To test the effect of milling, two samples were prepared: a powder sample (powder size < 0.5 mm) and a flake sample (random flakes of about 0.5-1 cm\({}^{2}\)). Preliminary studies on silver leaching were carried out to screen the optimal conditions by determining the leaching agent (HNO3) and its concentration range, the temperature and the contact time range. Silver can be easily dissolved in HNO3 as the possible chemical reactions of Ag with HNO3 have negative values of standard-state free energy (\(\Delta\)G), indicating the spontaneous character of the reactions [33]. Since Ag is a more valuable metal compared to Al, choosing only one type of acid is considered the most profitable solution.  
Hydrothermal leaching tests were performed in cylindrical steel reactors (Technistro, Nagpur, India) internally coated with PTFE and with a volume of 100 mL (Figure 2). The sample was mixed with 30 mL of HNO3 according to the desired L/S ratio and was hydrothermally treated at 100-140 \({}^{\circ}\)C for 0.5-2 h without stirring. The resulting product was allowed to cool and then filtered with a 0.45 \(\upmu\)m syringe filter for subsequent metal analysis with an inductively coupled plasma mass spectrometer (7500 \(\mathrm{c}\mathrm{c}\mathrm{c}\mathrm{r}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}  
Low temperature metal extraction  
The optimized conditions for hydrothermal leaching were used in the next experimental series. Keeping the HNO\({}_{3}\) concentration at 2 N and the L/S ratio at 1/10, the experiments were conducted at lower temperatures (25-45 \({}^{\circ}\)C), increasing the processing time (2-24 h), while flakes were used as the processing material. The effect of stirring was also tested in lower temperature experiments since stirring could not be performed with the hydrothermal reactors.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Spectral Domain-Based Methods  
Frequency is an indicator that characterizes the intensity of grayscale changes in the image [52]. In spectral domain evaluation functions, the high-frequency and low-frequency components of the image correspond to sharp and blurred parts, respectively. The sharper the image, the more detail and edge information it contains. Therefore, image sharpness can be assessed by a high-low transformation in spectral domain [53]. This type of evaluation methods are usually based on Fourier transform (FT) [54], wavelet transform (WT) [55] methods, etc. The general framework of spectral domain-based methods is shown in Figure 4, where H and L represent the high-pass filter and the low-pass filter, respectively, and LL, HL, LH and HH are the corresponding components after filtering again.  
Figure 3: Edge detection results: (**a**) Original image; (**b**) Canny operator; (**c**) Sobel operator; (**d**) Prewitt operator.
## 5 Simulation Results
### Testing of the Proposed Control Strategy
#### 5.2.1 Case 1: Simultaneous Cooling and Power Load-Following Performance  
Case 1 was designed to test the simultaneous cooling and power load-following performance of controllers. First, compare the proposed method with conventional MPC. Assume that the MGT-CCP system is operating at point #3. At 200 s, the MGT-CCP system changes from the operating point #3 (73.5 kW, 8.5 \({}^{\circ}\)C) to the operating point #4 (69.69 kW, 8.8 \({}^{\circ}\)C). The simulation results are shown in Figure 11 and Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IAE\({}_{\text{Power}}\)** & **IAE\({}_{\text{Temperature}}\)** \\ \hline MPC & 907 & 607 \\ Proposed method & 841 & 121 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance indices for Figure 11.  
Figure 10: Control structure of comparison methods. (**a**) PID; (**b**) MPC.  
Both the proposed method and the conventional MPC can track the set points. But the proposed method is faster for load tracking. The power tracking performance under the IAE indicator increased by 7%, and the cold load tracking performance increased by 80%. The reason is that the proposed method uses coupling compensation and nonlinear compensation strategies.  
Then we test the tracking performance of the controllers for a wide range of load changes. At 1500 s, the MGT-CCP system changes from the operating point #4 (69.69 kw, 8.8 \({}^{\circ}\)C) to the operating point #5 (65.18 kw, 9.1 \({}^{\circ}\)C), and then keeps running at the operating point #4, at 3000 s, the MGT-CCP system changes from operating point #5 to operating point #1 (80 kw,8 \({}^{\circ}\)C), and finally keeps running at operating point #1. The simulation results are shown in Figure 12 and Table 3. The simulation results show that the proposed method has smaller overshoot and faster tracking speed. The power tracking performance under the IAE indicator increased by 48%, and the cold load tracking performance increased by 20%.  
Figure 11: Case 1: Load tracking performance of the MGT-CCP system (Solid in blue: proposed method; dashed in black: Conventional MPC; dot in green: reference). (**a**,**b**) Output variables; (**c**,**d**) Manipulated variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IAEPower** & **IAETemperature** \\ \hline PID & 1636 & 364 \\ Proposed method & 846 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indices for Figure 12.