esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This study introduces a novel DPE framework that incorporates a low-complexity correlation approach combined with the ADBO algorithm to enhance positioning accuracy in complex environments while significantly reducing computational cost. Traditional 2 SP methods face accuracy degradation in multipath and weak signal conditions. By leveraging global signal correlation and optimizing the search process through swarm intelligence, the proposed method effectively addresses these challenges. A key innovation is the integration of low-complexity correlation functions into the DPE framework, eliminating the need for full signal-level correlation. The ADBO algorithm, designed using FLA, dynamically adjusts the population structure and convergence factors, enhancing flexibility in the optimization process, particularly through a hybrid Cauchy-Gaussian mutation strategy.  
Experimental results, including benchmark tests using CEC2020 functions, demonstrate that ADBO achieves superior convergence and optimization performance, ranking first among all comparative algorithms. The DPE-SI method consistently outperformed MRG, ARS, and 2 SP in both fine and poor satellite geometries, improving positioning accuracy and computational efficiency. In real-world experiments, ADBO-based DPE achieved the highest positioning accuracy and reduced computational time by 82.1% and 85.8% in static and dynamic scenarios, respectively, compared with MRG. Despite promising results, several limitations remain. The iterative nature of ADBO results in higher runtime compared with 2 SP, which could be a constraint for real-time applications. Additionally,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **2 SP** & **MRG** & **ADBO** \\ \hline Sum position error (m) & 14.63 & 20.12 & 10.75 \\ Position error in East (m) & 6.51 & 8.76 & 4.83 \\ Position error in North (m) & 6.16 & 8.38 & 4.43 \\ Position error in Up (m) & 11.06 & 15.24 & 8.01 \\ Execution time (s) & – & 13.74 & 1.95 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performance comparison in the dynamic receiver scenario.  
Figure 13: Performance comparison of 2 SP, ADBO, and MRG in the dynamic receiver scenario. (**a**) Positioning road map. (**b**) Comparison of positioning error.  
the robustness of the proposed method under extremely low signal-to-noise ratio conditions or in highly multipath-prone environments was not assessed and warrants further investigation.  
Future work will focus on optimizing real-time performance and exploring hardware-level acceleration. Furthermore, the DPE-SI framework will be extended to support multi-frequency, multi-constellation GNSS signals, enhancing its robustness and global applicability. Potential applications include urban canyon navigation, UAV high-dynamic positioning, and low-cost GNSS devices. In summary, this study demonstrates that integrating swarm intelligence with low-complexity correlation-based DPE enables high-precision positioning under challenging conditions while significantly reducing computational overhead.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method
### Frequency-Domain Analysis
#### 2.2.2 Broad-Band Method  
In the broad-band random process, due to the large difference between peak frequency and crossing frequency in unit time. If the traditional narrow-band method is still used to calculate the fatigue damage will make the result larger. Therefore, [PERSON] [13] introduced the correction factor \(\lambda\) to compensate the calculation results of narrow-band process fatigue damage by using the approximate narrow-band method. When the correction factor \(\lambda\) is substituted into Equation (6), the fatigue damage formula for the broad-band stationary process is obtained:  
\[D_{W}=\lambda\frac{f_{0}T}{C}\Big{(}\sigma\sqrt{2}\Big{)}^{m}\Gamma\Big{(}1+ \frac{m}{2}\Big{)} \tag{8}\]  
where correction factor \(\lambda\) is a function of exponent \(m\) of the effective bandwidth \(\varepsilon\) and S-N curve:  
\[\lambda=0.962-0.033m+[0.074+0.033m](1-\varepsilon)^{(1.587m-2.323)} \tag{9}\]  
The irregular factor \(\gamma\) varies between 0 and 1. When the stress signal approaches the narrow band process (sine wave \(\gamma=1\)), it approaches 1. When the stress signal is close to the white noise process, it is close to 0.  
\[\varepsilon=\sqrt{1-\gamma^{2}} \tag{10}\]  
The n-order spectral moment of the stress power spectral density \(S(f)\) can be expressed as follows:  
\[m_{n}=\int_{0}^{\infty}f^{n}\cdot S(f)df \tag{11}\]  
\[\gamma=\frac{m_{2}}{\sqrt{m_{0}m_{4}}} \tag{12}\]
## 2 System Design and Working Principle  
This paper discusses the direct-expansion solar PVT heat pump system, which synchronizes the output of electrical and thermal energy by efficiently utilizing solar radiant energy, as shown in the experimental principle in Figure 1. The system consists of two subsystems: a photovoltaic power generation subsystem and the heat pump subsystem. The photovoltaic power generation subsystem comprises photovoltaic panels, batteries, and inverters. It primarily converts solar energy directly into electricity, outputs 48 V DC power stored in batteries, and converts it into 220 V, 50 Hz AC power through inverters for supply to the national grid or local use. The heat pump subsystem includes PVT collectors, compressors, condensers, and expansion valves (as shown in Figure 2), using R410a as the circulating refrigerant. The refrigerant evaporates directly in the PVT panels, absorbing heat and removing part of the heat accumulated in the photovoltaic cells. After compression, the refrigerant enters the condenser in the water tank and exchanges heat with the water in the tank. This process not only reduces the temperature of the PVT panels, thereby increasing the photovoltaic conversion efficiency, but also produces hot water by utilizing the heat.
###### Abstract  
Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an \(\ell_{1}-\ell_{1}\) optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.  
robust channel estimation; underwater acoustic communications; compressed sensing; impulsive noise +
Footnote †: journal: _remote sensing_  
02024/16, 1380. [[https://doi.org/10.3390/rs16081380](https://doi.org/10.3390/rs16081380)]([https://doi.org/10.3390/rs16081380](https://doi.org/10.3390/rs16081380))
# Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
## 2 Problem Formulation  
The nonlinear aircraft dynamics, containing input uncertainty, matched uncertainty, and unmatched disturbance, is given by  
\[\left\{\begin{array}{l}\dot{x}_{p}(t)=A_{p}x_{p}(t)+B_{p}\Big{(}\Lambda u(t) +\theta_{p}^{T}\Theta_{p}\Big{(}x_{p}(t)\Big{)}\Big{)}+d_{p}(t)\\ y_{p}(t)=C_{p}x_{p}(t)\end{array}\right. \tag{1}\]  
where \(x_{p}\in\mathbb{R}^{n_{p}}\) is the plant state, \(u\in\mathbb{R}^{m}\) is the control input, and \(y_{p}\in\mathbb{R}^{l}\) is the measurement output of the system. \(A_{p}\in\mathbb{R}^{n_{p}\times n_{p}}\), \(B_{p}\in\mathbb{R}^{n_{p}\times m}\), and \(C_{p}\in\mathbb{R}^{l\times n_{p}}\) are constant known matrices. In practical flight applications [26], the state matrix \(A_{p}\) is generally obtained by wind-tunnel measurements. \(C_{p}\) can be determined through the relationship between output and state. However, the input matrix \(B_{p}\) may be inaccurate for the nominal system due to damage and failures, or due to changes in aircraft dynamics. The actuator anomaly can be modeled as an unknown element \(\Lambda\in\mathbb{R}^{m\times m}\), representing the uncertain control effectiveness. \(\Lambda\) is nonsingular, which has upper bounds known a priori: \(\sigma=\sup\lVert\Lambda\rVert\). \(\theta_{p}^{T}\Theta_{p}(x)\in\mathbb{R}^{m\times n_{p}}\) represents the matched uncertainty that enters the system through the columns of \(B_{p}\). \(\theta_{p}\in\mathbb{R}^{s\times m}\) is the unknown constant weight matrix, and \(\Theta_{p}(\cdot):\mathbb{R}^{n_{p}}\rightarrow\mathbb{R}^{s}\) is a vector of the basic functions that can be described as \(\Theta_{p}\Big{(}x_{p}\Big{)}=\Big{[}\phi_{p1}\big{(}x_{p}\big{)},\phi_{p2}\big{(} x_{p}\big{)},\ldots,\phi_{pN}\big{(}x_{p}\big{)}\Big{]}^{T}\). \(d_{p}\in\mathbb{R}^{n_{p}}\) represents the unmatched bounded disturbance uncertainty in the flight period; i.e., \(\sup_{\
u_{t}}\big{\|}d_{p}\big{\|}\leq d_{0}\).  
Our goal was to design a control input \(u(t)\) that would make \(y_{p}(t)\) track a reference command \(r(t)\) with bounded errors in the presence of uncertainties \(\Lambda\), \(\theta_{p}\), and \(d_{p}\). Furthermore, we assumed that the pair \(\Big{(}\ A_{p}\ \ \ B_{p}\ \Big{)}\) was controllable, \(x_{p}(t)\) was available for feedback, and \(u(t)\) was the class of admissible controls consisting of measurable functions.
## 6 Analysis
### Communication and Memory Overhead  
The network lifetime is a critical goal in designing protocols for wireless sensor networks. In our proposal, we minimized the broadcast data requirement in establishing direct key between neighboring nodes. Our _1-hop_ broadcast message length is _sizeof(ID)_\(+\)_sizeof(nonce)_\(+\)_3_\(\times\)_sizeof(hash)_. Comparing with other models in [30], the broadcast messages in key discovery phase contain hundreds of key, to achieve high connectivity. With CPPS in [13], the length of broadcast messages is _sizeof(ID)_\(+\)_5_\(\times\)_sizeof (polynomial ID)_, which is higher than ours.  
The memory size for storing key materials derived from polynomials is _M_\(=\)_3_\(\times\)_(t_\(+\)_1)_\(log_{2}q\)_\(+\)_sizeof(ID)_\(+\)_sizeof(nonce)_(bits)_. This value, along with the number of nodes sharing a polynomial, affects to the resilience against node compromise attacks. This issue will be discussed in more detail in the following section.  
Figure 8: Network connectivity vs. Deployment point distance (_a_).
## 3 Materials and Methods
### Quality Assessment and Evaluation of Selected Studies  
To ensure the quality of the selected articles, we employed the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) framework. This method allowed us to systematically evaluate each study across four key domains: risk of bias, inconsistency, indirectness, and imprecision. Each domain reflects a potential shortcoming, where a higher score (3) indicates fewer concerns (e.g., low risk of bias or high consistency), and a lower score (1) reflects greater concerns (e.g., high risk of bias or low consistency). Two authors independently assessed each paper, and the scores for each domain were averaged to minimize subjectivity. The overall quality score for each study was calculated as the average of the four domainscores, where a higher overall score corresponds to higher overall quality. The numerical scores were converted into descriptive quality levels, categorized as low, low-to-moderate, moderate, moderate-to-high, and high, to provide an easily interpretable assessment.  
The evaluation outcomes for the selected papers, summarized in two tables (Tables A1 and A2 in Appendix A), reveal key insights into the methodological rigor and relevance of the included studies. For the methodological studies, most papers achieved an overall quality rating of \"high\", indicating low levels of bias, inconsistency, and imprecision, with only a few studies showing moderate indirectness. This observation reflects the general robustness of the proposed methods in addressing the research objectives. In contrast, the literature review papers displayed a broader range of scores. While two reviews were rated as \"high quality\", one was classified as \"moderate to high\" due to higher indirectness and moderate imprecision. This outcome highlights potential limitations in contextual relevance or comprehensiveness in some reviews. Overall, the assessment demonstrates the reliability of the selected studies while providing clarity on areas for further improvement.
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 4 Discussion
Hence, the sensitivity of the sensor under test is \(s=1/a_{1}=0.077\)\(\Omega\) g\({}^{-1}\) and estimation of resistance change due to the displacement along the vertical axis can be calculated as \(\Delta R/\Delta L=12.54\)\(\Omega\)mm\({}^{-1}\) by considering force and displacement ranges of 114 g and 0.7 mm, respectively. This means that if temperature affects resistance up to 12 \(\Omega/^{\circ}\)C, then a variation \(\Delta\)T = 0.1 \({}^{\circ}\)C can lead to a displacement measurement error of 0.1 mm or a force measurement error of 15 g:  
\[e_{d}=\frac{0.1\ ^{\circ}\mathrm{C}\cdot 12\ \frac{\Omega}{\mathrm{C}}}{12.54\ \Omega\mathrm{mm}^{-1}}\cong 0.1\ \mathrm{mm}\ e_{f}=\frac{0.1\ ^{\circ}\mathrm{C}\cdot 12\ \frac{\Omega}{\mathrm{C}}}{0.077\ \Omega\mathrm{g}^{-1}}\cong 15\ \mathrm{g} \tag{5}\]  
Hence temperature compensation is crucial, particularly when small force steps are considered. Overall, the presence of hysteresis affects calibration results of all tests, with absolute values five times higher with respect to the preliminary on-off tests PF1 and PF2, where no hysteresis was present but still obtained RMSE lower than 13% of the tested range. This suggests that good accuracy can be achieved when employing this kind of sensor to detect impulse forces or the presence of a load. In the present paper, low-cost 3D printed strain gauges have been characterized to reduce the gap in knowledge found in the scientific literature. A way to compensate for the temperature effect when the sensor is employed for force measurement was found and discussed. Several studies have been conducted in the past few years to correlate process parameters to sensor performances to enhance these latter. Indeed, since the fabrication process is at an early stage and constantly evolving, particular efforts should be focused on the choice of extruded materials. A new challenge, from a manufacturing standpoint, is using new material extrusion techniques based on the deposition of conductive inks in conjunction with thermoplastic material extrusion to level up sensor performances and push the role of AM technologies in the field of sensor fabrication.
## 4 Data Description  
### Data Collection  
To explore the formation mechanism of conflict risk in the diverging area of the toll plaza, trajectory data were collected at the toll plaza located on G42 freeway in the northeast area of Nanjing, China. G42 is an east-west major corridor from Shanghai to Chengdu, with four main lanes for each direction. This study site focused on the eastbound diverging area of the toll plaza. Figure 3 depicts the layout of the diverging area. The diverging area is approximately 360 m in length, including 60-m main lanes and a 300-m non-marked diverging area. Over 300 m, the width of the diverging area without lane markings is increased from 15 m to 65 m along the driving direction of vehicles. There are 12 toll lanes in total, including 3 ETC lanes on the leftmost side and 9 MTC lanes on the right side. Note that MTC trucks can only use the two rightmost MTC lanes.  
As mentioned above, trajectory data contain abundant traffic information, which formed the basis for the microscopic study of conflict risk. In this study, the unmanned aerial vehicle (UAV), which could provide 4K ultra-high quality and 30 frames per second (fps) video, was utilized to record vehicle movements in the diverging area. The data were collected during peak periods on 17 March 2018, a sunny and windless day, to ensure that the UAV can hover steadily and capture clear videos. The UAV flew at an altitude of around 200 m. A total of 1.5 h videos were recorded, and 50 min videos were finally selected for analysis. The videos that did not capture any conflicts between vehicles or failed to record complete vehicle trajectories were removed.  
The video data were processed by the video analytics system named Automated Roadway Conflicts Identification System (ARCIS), developed by the University of Central Florida Smart and Safe Transportation (UCF SST) team [43; 44]. The system contains video data stabilization, features detection and tracking, coordinate transformation, and error elimination. The ARCIS has higher vehicle detecting and tracking accuracy than a traditional video analysis system. More detailed data processing could be seen in our previous study [10]. The trajectories of 1152 vehicles were obtained after data processing, including 1031 cars, 30 buses, and 42 trucks. The hourly traffic flow rate ranged from 1050 vph to 1740 vph (calculated six times of the 10-min volume).  
Figure 3: Layout of the toll plaza diverging area and toll collection lanes.
###### Abstract  
The Earth rotation parameters (ERP) play a crucial role in defining the global reference frame and the Global Navigation Satellite System (GNSS) is one of the important tools used to obtain ERP, including polar motion (PM), its rates, and length of day (LOD). The latest IGS Repro3 ERP products, which provided the IGS contribution to the latest ITRF2020, were generated without consideration of the Beidou Navigation Satellite System (BDS) observations. The global BDS, namely the BDS-3 constellation, has been completely constructed from July 2020 and the observing stations are evenly distributed globally now. Two couple dual-frequency combinations, including the B1I/B3I and B1C/B2a combinations, are commonly used for BDS-3 ionosphere-free combination usage. With the goal of identifying the optimal dual-frequency combination for BDS-3 ERP estimates for the future ITRF definition with a consideration of BDS-3, this research evaluated the performance of ERP estimation using B1I/B3I and B1C/B2a combinations. Firstly, we conducted a comparison of the ambiguity resolutions. The mean percentage of successfully resolved ambiguities for the BDS-3 BIC/B2a combination is 86.5%, surpassing that of B1I/B3I. The GNSS satellite orbits and ERP were estimated simultaneously, thus the accuracy of orbits could also reflect the performance of the ERP estimates. Subsequently, we validated the orbits of 22 BDS-3 Medium Earth Orbit (MEO) satellites using Satellite Laser Ranging (SLR), and the root mean square error (RMS) of the SLR residuals for the 3-day arc orbit with B1C/B2a signals was 5.72 cm, indicating superior accuracy compared with the B1I/B3I combination. Finally, we compared the performance of ERP estimation, considering both internal and external accuracy. For the internal accuracy, B1C/B2a-based solutions demonstrated a reduction in mean formal errors of approximately 17% for PM, 22% for LOD, and 21% for PM rates compared with B1I/B3I-based solutions. In terms of external accuracy, we compared BDS-3-derived ERP estimates with the IERS 20C04 products. The BIC/B2a combination exhibited a slightly better standard deviation performance and a significant reduction in mean bias by 56%, 54%, 39%, 64%, and 23% for X, Y polar motion, X, Y polar motion rates, and LOD, respectively, compared with B1I/B3I solutions. In conclusion, the results highlight the excellent signal quality for BDS-3 BIC/B2a and its superiority in ERP estimation when compared with the B1I/B3I combination.  
BDS-3; B1C/B2a; Earth Rotation Parameters; Satellite Laser Ranging; ITRF2020
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 5 Study of Sand Emergence Phenomenon Based on In Situ Reservoir Samples  
This section was mainly carried out using the device shown in the figures (Figures 5 and 6). This device could simulate the sand production morphology of weakly consolidated reservoirs with different median particle sizes (D\({}_{50}\) = 5-1000 \(\upmu\)m), and record the sand production and water yield. The device consists of a micro-imaging acquisition module, a gas-liquid pumping module, a core thin-section model module and a sand collection module. The outlet of the gas-liquid pumping module is connected to the inlet of the core flake model module, the outlet of the core flake model module is connected to the inlet of the sand collection module, and the micro-imaging acquisition module is connected to the core flake model module. The position and angle of the micro-imaging acquisition module can be changed at will.  
Based on the reservoir samples from Shenhu Sea in the South China Sea, research on the characteristics of sand production after hydrate decomposition in muddy chalk reservoirs was carried out. During the experiment, a simulated core (reservoir) was loaded in the visualisation cell to simulate the state of the reservoir after consolidation. Then, The driving fluid (gas/liquid) is injected into the cell at a set flow rate through 8 inlet  
Figure 5: Flowchart of the experimental setup.  
Figure 6: Physical diagram of the experimental setup.  
holes evenly distributed around the circumference of the cell, and the fluid flows in the pore space of the simulated core in the cell, which represented the process of stripping, transporting, carrying, and accumulating the particles around the pore space when the fluid flowed in the pore space of the formation. As the fluid continues to be injected, gravel detachment begins to occur at the outlet location of the thin section flow, and the formation of a sand outlet pore expands towards the inflow end of the thin section. By changing different experimental conditions, it could compare the sand transport and hole widening process under different production conditions. At the same time, it could be analyzed the sand clogging situation according to the real-time differential pressure changes. The whole experimental process can be video-recorded or photographed at any time by launching the software.