esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Datasets  
To verify the effectiveness and reliability of the algorithm, this paper uses received and transmitted signals from Danjiangkou Reservoir as the dataset. The dataset was collected at Danjiangkou Reservoir through multiple campaigns from 5 December 2023 to the end of July 2024. All data were acquired under clear weather conditions with minimal vessel activity. The deployment depth of the experimental transducer was approximately 10 m, and the experimental hydrophone was deployed at a depth of about 4 m. The basic parameters of the collected dataset are shown in Table 2:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer (Type)** & **Input Shape** & **Output Shape** & **Param** \\ \hline Linear & [1, 256] & [1, 128] & 32,896 \\ \hline Gelu & [1, 128] & [1, 128] & 0 \\ \hline Dropout & [1, 128] & [1, 128] & 0 \\ \hline Linear & [1, 128] & [1, 8] & 1032 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter table for the entire classification layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modulations** & 2 FSK, 4 FSK, 2 PSK, 4 PSK, OFDM, DSSS, 4 DSSS, 9 DSSS \\ \hline
**Signal format** & time domain signal \\ \hline
**Signal dimension** & 1 \(\times\) 4800 \\ \hline
**Distance (km)** & 1; 5; 10; 15; 18 \\ \hline
**Total number of samples** & 4000 \\ \hline
**training sets: test sets** & 7:3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset parameters.  
Among them, each signal modulation type has 100 samples collected at each distance. The SNR estimation result for a transmission and reception distance of 5 km is 6 dB. Due to the absence of 2 FSK data at 10 km, data from 15 km were used to replace the missing 10 km data to ensure an equal number of samples for each signal type during the training phase. Meanwhile, the 2 FSK data at 15 km were excluded during the testing phase.  
In this experiment, cross-entropy is used as the loss function, and its expression is:  
\[Loss=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}y_{ij}\log(p_{ij}) \tag{16}\]  
where \(N\) is the number of samples in the batch, \(C\) is the number of categories, \(y_{ij}\) is the label of the \(i\)-th sample, and \(p_{ij}\) is the predicted probability that the model assigns to the \(i\)-th sample belonging to the \(j\)-th category.  
The batch size is set to 16, and the optimizer uses AdamW with a learning rate of 0.0005. All of the above training and prediction processes are implemented using PyTorch2.3.0.
## 4 Parameter Sensitivity Analysis
### Summary of Key Parameter Impacts on Frost Heave and Thaw Settlement  
To systematically summarize the effects of critical parameters on frost heave and thaw settlement, Table 3 consolidates the sensitivity analysis results. The table categorizes the variation ranges of each parameter, quantifies their impacts on displacement behavior, identifies dominant trends, and translates these findings into actionable engineering implications. This synthesis highlights how thermal properties, geometric design, and soil hydrology collectively govern pipeline-soil interactions in seasonally frozen ground. By linking theoretical trends to practical design strategies, the table serves as a concise reference for optimizing pipeline resilience against freeze-thaw cycles.  
Figure 19: Time-dependent displacement of surface point 4 under different permeability coefficients.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 4 Discussion
### MEMS-Based Monitoring and Early Warning System  
The proposed dual-indicator MEMS monitoring system addresses the critical challenge of real-time instability assessment in unstable rock masses by leveraging NF (NF) and RMS-VAR as core parameters. Traditional seismic sensing methods, which rely on post-trigger event analysis (e.g., collapse-induced seismic waves), suffer from delayed responses and high false-negative rates in dynamic environments. Borehole-based geotechnical investigations, while effective for structural characterization, remain cost-prohibitive for localized unstable rock masses. In contrast, the MEMS-based dual-indicator approach enables the continuous vibration monitoring of both unstable rock masses and the adjacent bedrock, exploiting their differential vibration responses to derive stability metrics with high temporal resolution.  
The correlation between vibration curves from bedrock and unstable rock mass sensors is highly distance-dependent. As demonstrated in prior studies, sensor proximity (<10 m) is critical for maintaining strong signal coherence (R\({}^{2}\) > 0.85), ensuring the reliable extraction of NF and RMS-VAR. Our findings align with those of [PERSON] et al. [25], who emphasized that signal decoupling beyond threshold distances (>15 m) introduces phase distortions, rendering response characteristics non-representative of true rock behavior. To mitigate this, we advocate for MEMS sensor deployment within optimal proximity ranges, supplemented by correlation indices to validate data fidelity--a strategy validated in slope stability monitoring.  
Integrating NF and RMS-VAR with IoT cloud platforms resolves the limitations of standalone equipment performance. Localized thresholds for NF (\(\pm\)5% baseline), RMS-VAR (1.2-1.8), and inclined deformation (>2 mm/h) enable tiered warnings (e.g., yellow/orange/red alerts), while multi-factor big data models correlate environmental drivers (rainfall, temperature cycles) with historical instability patterns. This hybrid approach mirrors the framework proposed by [PERSON] et al. [26] for landslide prediction, where machine learning fused real-time sensor data with geospatial trends to achieve 92% early-warning accuracy.  
However, the scalability of MEMS-based systems hinges on efficient data handling. To achieve the real-time stability monitoring of field rock masses, the dual-indicator monitoring and early-warning system based on MEMS sensors requires the integration of critical technologies including solar power supply, wireless transmission, and cloud services. While ensuring data transmission stability, it is imperative to simultaneously optimize transmission efficiency. For massive data acquisition and processing demands, the system must implement load balancing algorithms and distributed storage architecture to overcome the multi-ten-second alert issuance delays inherent in traditional warning systems. Future work should explore advanced edge computing for on-site analytics, minimizing cloud dependency in remote terrains.
## 2 Classification of Phase Change Materials (PCMs)
### Eutectic PCMs
#### 2.3.2 Experimental Method  
The eutectic mass ratios can be determined by preparing and testing a series of PCMs mixtures with different proportions and comparing their DSC analysis results to determine the eutectic mass ratio [68]. In the case of binary mixtures, multiple samples are obtained by combining two PCMs. The different mass ratios of solid PCMs are weighed, melted, and stirred for 15 min to homogenate the mixture. Then, the phase change temperature and latent heat of prepared samples are investigated by DSC. The eutectic mixture point is identified by plotting the phase diagram based on DSC results. The multiple numbers of DSC tests and the time required for samples preparation make the experimental method more complicated than the theoretical one, especially for eutectic mixtures containing several components.  
In recent studies, the most reported eutectic mixtures are organic-organic ones, particularly fatty acids eutectic mixtures due to their advanced properties compared to inorganic materials, such as high latent heat and minimal volume change suitable thermal stability. Moreover, fatty acid mixtures present an excellent homogeneity, and no chemical degradation was noticed after repeated thermal cycles. On the other hand, the most used fatty acids have higher melting temperatures than the recommended temperature range for low latent heat storage (Table 1). For this purpose, mixing two or more components ensures temperature adjustment. Several types of eutectic mixtures (binary, ternary, etc.) exist, and their phase change temperature is adjusted by changing the weight ratio of the selected components (Table 2). A wide variety of eutectic mixtures with excellent thermal properties was used for several applications such as buildings insulation [69; 70], solar heating and cooling systems [71], and thermoregulating textiles [72].
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 5 Conclusions  
In this paper, we have studied the potential of Doppler shifts of LEO SV signals for navigation, when used as signals of opportunity. We defined a filter based on the tight coupling between a consumer-grade IMU, magnetometers and Doppler shifts. The filter was fed with representative sensors data, simulated for a high dynamic vehicle. We have characterized both position and attitude performances subject to thermal noise, satellite position and velocity errors, and satellite clock drift. This allowed for assessing and understanding the contribution of each error source to the navigation error. We have shown that, while the positioning error is large due to large orbit errors or high SV clock drifts, it becomes competitive with that of an INS based on a better quality IMU, if precise satellite orbits are available.  
Contrary to the positioning, which is highly affected by large error sources, we have shown that the attitude estimation is less sensitive to large orbit errors and high SV clock drifts. For typical values of the error sources (the intermediate case), the defined SOP-based filter, which uses the SPG4 model fed with the broadcast NORAD TLE files, is able to track the vehicle's attitude with a roll angle RMSE of \(\sim\)1.6\({}^{\circ}\), a pitch angle RMSE of \(\sim\)2.2\({}^{\circ}\), and a yaw angle RMSE of \(\sim\)4.8\({}^{\circ}\). These results illustrate the potential of the LEO SV signals of opportunity for the navigation of high dynamic vehicles in a GNSS-denied environment. Recently, the European Union (EU) announced that it will launch its own system of satellite-based high-speed internet [31]. Since large satellite orbit errors and clock drifts are the main factors limiting the performance of the Doppler shift-based positioning, if such a system provides accurate satellite positions and clock drifts, then it could in addition offer a navigation service that will be very useful in GNSS-denied environments.  
Conceptualization, N.J.; methodology, N.J.; software, N.J. and Q.J.; validation, N.J. and Q.J.; formal analysis, N.J.; investigation, N.J. and Q.J.; data curation, Q.J. and N.J.; writing--original draft preparation, N.J.; writing--review and editing, N.J.; visualization, N.J. and Q.J.; supervision, N.J.; project administration, N.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Review of Functionally Graded Thermal Sprayed Coatings
## 4 Major Applications of Functionally Graded Coatings
### Thermal Barrier Coatings
* APS deposited 8 YSZ/Gd\({}_{2}\)Zr\({}_{2}\)O\({}_{7}\) coatings [251];
* APS deposited La\({}_{2}\)Ce\({}_{2}\)O\({}_{7}\)/8 YSZ [301];
* APS deposited LaMgAl\({}_{11}\)O\({}_{19}\)/8 YSZ bilayers [302]. The studies aimed at prolonged thermal cycling lifetime compared to the conventional two-layer TBC. The goal was reached owing to the increase of phase stability and of resistance against sintering. A small modification was proposed by applying yttria- and ceria-stabilized zirconia (CYSZ) as bottom layer instead of yttria-stabilized zirconia (YSZ) [303; 304]. The obtained FGC showed low thermal conductivity and satisfactory thermal shock resistance. Finally, an interesting idea of depositing the YSZ layers having various microstructures was presented [305]. The bottom layer was dense to improve bonding and the external one was porous to decrease thermal conductivity.
## 6 Practicality Comparison  
Compared with the other existing results, the practicality of the results in the present study should be further explained. As mentioned in the related references [11; 12; 13; 14; 18; 19] regarding power and yaw stability issues, as long as the chattering can be effectively controlled, under the control strategy proposed in this design, the yaw process can be fully stabilized and the power acquisition of the system will not be affected at all. The problems of the wind force magnitude and direction proposed in the references [15; 16; 17] can be uniformly summarized as disturbance problems in this design. However, PD-based iterative learning control has significant advantages when dealing with disturbances [25], so the control algorithm used in this design was actually an adaptive control algorithm with a robust performance, which could appropriately deal with wind disturbance. At the same time, the auxiliary motor driving method proposed in this design increased the driving torque from 10% to 30% or even higher (in the present study, it was only set at 20% and directly affected the LSJ system), which meant a better control performance and disturbance resistance. It can not only be applied to the control process of a single wind turbine, for resisting various types of flutter [1; 2; 3; 4], but can also be used in the control processes of wake and eddy-current [5; 6] disturbances in wind farm clusters.  
In the current wind energy production process, most of the existing research results focus on the resonance problem of the wind turbine system caused by the blades, because an increase in the swept area of the impeller inevitably prolongs the length of the blades, which increases the mass and decreases the stiffness of the blades, reduces their inherent frequency, and enters the frequency band of impeller speed stimulation, leading to the resonance of the impeller unit. In fact, there are similar problems with tower structures. As a supporting structure system, the tower system constantly receives energy from the impeller system and also bears the load transmitted by the impeller system. If its natural frequency also enters the excitation frequency band of the impeller speed, coupling resonance occurs with the impeller system during the operation of the unit. This coupling of low-speed and low-frequency vibrations has a catastrophic impact on the operation of the unit. The design in the present study focused on the low-speed buffeting during the yaw process, and treated the load excitation in the related references [1; 2; 3; 4; 5; 6; 11; 12; 13; 14; 15; 16; 17; 18; 19] as a dynamic disturbance, thus achieving stability control. Based on the proposed iterative algorithm using the open-loop PD, further research on the coupling resonance of the impeller tower has a positive significance for reducing the incidence of wind turbine damage accidents.
## 3 Common Latitude Power Series Expansions with Geodetic Latitude as Variable
### Power Series Expansion of Reduced Latitude with Geodetic Latitude as Variable  
According to the geodesy theory, the reduced latitude \(u(B)\) is related to the geodetic latitude \(B\) by  
\[\tan u=\sqrt{1-e^{2}}\tan B. \tag{13}\]  
\begin{table}
\begin{tabular}{c} \hline \hline \multicolumn{1}{c}{**Expand to \(e^{10}\) Based on the First Expand to \(n^{5}\) Based on the Third**} \\
**Eccentricity \(e\)** \\ \hline \(\left\{\begin{array}{l}\par  
The power series expansion of the reduced latitude with geodetic latitude as the variable is  
\[u(B)=B+m_{2}\sin 2B+m_{4}\sin 4B+m_{6}\sin 6B+m_{8}\sin 8B+m_{10}\sin 10B \tag{14}\]  
The coefficients in Equation (14) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 4.
# A Study on the Super Resolution Combining Spatial Attention and Channel Attention
## 2 Related Work  
### ResNet  
ResNet is a convolution neural network model using skip connection to effectively learn a neural network deeper than the existing 19 layer VGG19 neural network [12]. Figure 1 is a ResNet structure consisting of 152 convolution layers.  
In general, as the number of layers increases in a neural network, a higher level of features can be extracted. However, when the neural network increases beyond a certain layer, the gradient vanishing problem occurs, and learning is not performed properly [13]. ResNet uses residual block using skip connection to solve this problem.  
\[F(x)=H(ReLU(H(x)), \tag{1}\]  
\[R(x)=H(ReLU(H(x))+x, \tag{2}\]  
(a) is the existing neural network and (b) is the residual block structure proposed by Resnet in Figure 2. Equation (1) is the existing neural network formula, and Equation (2) is the residual block formula of ResNet. In Equations (1) and (2), \(x\) is the input feature map, \(ReLU\) is the Rectified Linear Unit, and \(H\) is the convolution. Existing neural networks train the neural network to output from input \(x\) feature maps [14]. However, in ResNet, unlike existing neural networks, the neural network is trained so that the sum of the  
Figure 1: ResNet Architecture.  
output and the input comes from the input \(x\) feature map. ResNet is a structure that learns \(R(x)-x=H(ReLU(H(x))\) to learn the weight of \(H(ReLU(H(x))\), and there is a detour path from input to output. This path is called skip connection, and it is called identity mapping because the conversion input state is maintained through this path. In ResNet, it is called residual block because it learns the difference between input and output with this skip connection. If an input similar to the output comes in, the convolution operation of the residual block learns a value close to the zero vector, so even a small change in the input can be sensitively detected. In addition, if the target value has already been extracted in the previous step as a deep layer, it can be used as an output by skipping several convolutions with a skip connection structure through the convolution operation. Therefore, one neural network has the effect of using multiple neural networks with different numbers of layers. ResNet can learn neural networks with many layers and increase accuracy by using residual blocks with such a skip connection.
## 5 Guidance of Traffic Parameters for Signal Control Optimization
### Signal Control Scheme Evaluation System  
Extracted multi-dimensional traffic parameters not only provide inputs for signal control optimization but also constitute a comprehensive effectiveness evaluation system. This evaluation method, based on actual operational data, proves more objective and accurate compared with traditional simulation evaluation.  
Multi-level evaluation indicator system:  
1. Microscopic level: Vehicle-level indicators including number of stops, travel times, and speed fluctuations, reflecting individual travel experiences
2. Mesoscopic level: Link average speeds, flows, densities, and other indicators, evaluating link operational efficiency
3. Macroscopic level: Network average speeds, total delays, system throughput, and other indicators, measuring overall performance  
Furthermore, parameter extraction based on trajectory data possesses near-real-time characteristics, enabling dynamic monitoring and feedback, specifically including: continuous monitoring of signal control effectiveness, timely detection of abnormal states, dynamic adjustment of control schemes, and long-term trend analysis and evaluation.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 2 System Model
### The Model of UAV Communication Coverage  
In order to facilitate the information fusion between each UAV and its neighboring UAVs, each UAV needed to broadcast its own sensed information. These specific types of information sensed are introduced in Section 3.3. Due to the limited payload of each UAV, the communication coverage of each UAV was usually limited. The effective communication coverage of each UAV was an area enclosed by a circle of a certain radius centered around itself. This radius could be defined as the communication coverage radius. The communication coverage radius of the \(i\)-th UAV in the \(t\)-th time slot could be defined as \(\omega_{i}^{t}\), and the state vector of the communication coverage radius of all UAVs in the \(t\)-th time slot could be defined as \(\mathbf{\omega}^{t}\); then:  
\[\mathbf{\omega}^{t}=\left[\omega_{1}^{t},\omega_{2}^{t},\cdots,\omega_{M}^{t} \right]^{T} \tag{6}\]  
Obviously, the larger the communication radius, the greater the communication power required by the UAV. For the convenience of subsequent representation, all values of \(\omega_{i}^{t}\) were obtained from a discrete value set, which formed a vector:  
\[\mathbf{\rho}=[\rho_{1},\rho_{2},\cdots,\rho_{N}]^{T} \tag{7}\]
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 2 Material and Methods
### Predicting Wheat PY Based on the EEO Theory
#### 2.2.2 Environmental Constraint on Peak LAI  
To predict the wheat growth under future scenarios, the temporal variation of LAI follows the strategy suggested by the global dynamic global vegetation model: LPJmL4 [51]. The LAI at time step t (\(LAI_{t}\)) can be written as a function of peak LAI (\(LAI_{max}\)) and a factor of phenological development ranges from 0 to 1 (\(f_{t,\ ref}\)):  
\[LAI_{t}=f_{t,\ ref}\times\ LAI_{max} \tag{2}\]  
Traditional static \(LAI_{max}\) lookup tables ignore the response of this metrics to the climate change [52]. As suggested by [PERSON] et al. [11] and [PERSON] et al. [35], this peak greenness is synergistically limited by the absorbed carbon, or, say, available energy, and by water supplement:  
\[LAI_{max}=\min(LAI_{c},LAI_{w}) \tag{3}\]  
\(LAI_{c}\) represents the peak LAI under the condition that the wheat growth is not limited by the water availability, i.e., no soil moisture stress is employed during the GPP estimation. Part of the total GPP during the growing season (\(f_{LAI}\)) would be apportioned to the leaves:  
\[\sum GPP=f_{LAI}\times LAI_{c}\times LMA \tag{4}\]  
where leaf mass per area (LMA) represents the thickness of the leaves. [PERSON] et al. [53] suggested a universal optimizing scheme to predict LMA. By integrating Equation (1) to (4), the \(LAI_{c}\) can be solved by an iteration.  
Within a rainfed wheat system, the wheat growth is limited by the water availability. The total evapotranspiration (ET) throughout the growing season follows a part of the precipitation:  
\[\sum ET= f_{ET}\times Pre \tag{5}\]  
The proportion of ET to total precipitation (\(f_{ET}\)) can be estimated by a Budyko function [54]:  
\[ET/Pre=\frac{PET/Pre}{\left[1+\left(PET/Pre\right)^{n}\right]^{1/n}} \tag{6}\]  
where PET is the potential evapotranspiration and n is the shape metrics of the Budyko curve that can be estimated by the leaf area [55]. The ET can be estimated based on the Penman-Monteith (PM) equation. A key metric within the PM equation, the canopy conductance (\(G_{s}\)), can be estimated by a universal ET model suggested by [PERSON] et al. [33] to avoid employing type-based parameters:  
\[G_{s}=1.6\cdot\text{GPP}/(c_{a}-c_{i}) \tag{7}\]where 1.6 represents the difference in diffusion capacity of CO\({}_{2}\) and H\({}_{2}\)O molecules, and \(c_{i}\) is the intrinsic CO\({}_{2}\) concentration, which can be estimated by the Pmodel [33]. During the ET calculation, a denser canopy, i.e., a higher LAI value, means that a higher proportion of radiative energy is intercepted by the canopy [56]. By integrating Equation (5) to (6), the \(LAI_{w}\) can be solved by an iteration.