esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper proposes a novel acquisition process that leverages a FNN Model to establish the relationship between satellite orbital parameters and pointing errors in both azimuth and elevation. To evaluate the performance of the FNN Model, the predicted results were compared with on-orbit telemetry data. By integrating the predicted pointing errors with a spiral scanning function, the acquisition process was accelerated by more than 300 times, while the success rate increased by a factor of 8. The FNN Model proved effective in scenarios involving multiple influencing factors. Moreover, the potential of reinforcement learning and the feasibility of achieving full automation warrant further investigation.  
Conceptualization, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, Z.D. and H.S.; formal analysis, X.L. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 52205576), Optoelectronic Measurement and Intelligent Perception Zhongguancun Open Lab (No. LabSOMP-2023-04), and State Key Laboratory for Manufacturing Systems Engineering (Grant No. klms2021005).  
All the data used in this article can be obtained from the author upon reasonable request.  
The authors declare no conflicts of interest.  
Figure 15: Pointing error in Fourier coordinates: (**a**) pointing error of azimuth with a period from 1 s to 7200 s; (**b**) pointing error of elevation with a period from 1 s to 7200 s; (**c**) pointing error of azimuth with a period from 1 s to 600 s; (**d**) pointing error of elevation with a period from 1 s to 600 s.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.6 Importance of Coordination  
An initial approach is to solve the VNM (Equation (4)) and VLM (Equation (5)) functions separately and isolated from each other [18]. The absence of coordination suggests that the VNM is solved during the first phase, providing its outcome as an input for the solution of the VLM function, which is calculated during the second phase. Contrariwise, the computational steps of VNM can be executed in coordination with the VLM calculations with the goal to acquire an optimized output.  
From the experimental results of the studied literature, we can recognize that the adoption of any form of coordination between the node and link mapping stages, enriches the solution space and increases the overall effectiveness of the embedding process, in contrast to the uncoordinated use cases [22]. An alternative form of coordination can be implemented by simultaneously solving the VNM and VLM subproblems during a single stage, as performed in [27].
## 2 Methodology
### Dynamic Snow Mask  
To help investigate the space-time coverage of terrestrial snow, we use the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover [50] for the years 2001-2020 to serve as a reasonable proxy for binary (yes or no) snow coverage, as well as a determinant for a dynamic snow mask. Since the vast majority of the terrestrial snow cover is located in the northern hemisphere--about 40 million (km\({}^{2}\)) [51] compared to less than 1 million (km\({}^{2}\)) in the southern hemisphere [52]--only terrestrial snow over the northern hemisphere is explored here in order to minimize computational expense.  
The IMS snow mask is leveraged here to empirically describe the snow coverage extent. We compute the daily snow-covered probability through statistics of snow occurrence  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constellation ID** & **Sensor Marks** & **Sensor Mixture** \\ \hline C1 & \(\mathbf{\pi\star\star\star}\) & PMW \& two C-band SARs \& narrow LiDAR \\ C2 & \(\mathbf{\pi\star\star}\) & PMW \& Ku-band SAR \& narrow LiDAR \\ C3 & \(\mathbf{\pi\star\star\star}\) & PMW \& Ku-band SAR \& wide LiDAR \\ C4 & \(\mathbf{\pi\star\star\star\star\star}\) & PMW \& two C-band SARs \& Ku-band SAR \& narrow LiDAR \& wide LiDAR \\ \hline \hline \end{tabular} \(\mathbf{\hat{\pi}}\) = PMW sensor; \(\mathbf{\star}\) = C-band SAR; \(\mathbf{\star}\) = Ku-band SAR; \(\mathbf{\star}\) = narrow LiDAR; \(\mathbf{\star}\) = wide LiDAR.  
\end{table}
Table 2: Sensor makeup of hypothetical snow constellation configurations.  
within each 0.04-degree grid from historical data to estimate the maximum likelihood of snow cover. For the pixels with a probability larger than 0.5, it is marked as snow. Otherwise, it is considered as snow-free. The binary snow map is computed as:  
\[\begin{cases}\text{snow-covered pixel}&\text{if}\ \frac{\sum_{i=200}^{200}IMS(\vec{x}, doy,i)}{N}\geq 0.5\\ \text{snow-free pixel}&\text{if}\ \frac{\sum_{i=200}^{200}IMS(\vec{x}, doy,i)}{N}<0.5\end{cases}, \tag{1}\]  
where \(\vec{x}\) refers to the pixel location in space, _day_ refers to the day of year, and \(N=20\) represents the total number of years used during the analysis.  
This snow cover extent approximates the climatological space-time occurrence of snow as prior knowledge for simulation. It is eventually convolved with the sensor viewing area (see Section 2.1) to estimate the viewed snow cover extent, which is a necessary precursor to study remotely-sensed snow mass. This current study focuses on the viewed snow extent as a means of further exploring the snow mass in a follow-up study.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 1 Introduction
Satellite service is generally expensive ([[https://www.globalcomsatphone.com/hughesnet/satellite/costs.html](https://www.globalcomsatphone.com/hughesnet/satellite/costs.html)]([https://www.globalcomsatphone.com/hughesnet/satellite/costs.html](https://www.globalcomsatphone.com/hughesnet/satellite/costs.html))) because it costs a considerable amount of money to design, construct, launch and monitor a satellite. Private networks, though, must not only obey all the regulations governing public utilities, but also maintain all their equipment. Most transmission frequencies, for example, are regulated and require licenses, so licensing may be an additional cost. Television White Space (TVWS) is regulated worldwide [11], and licenses must be obtained to operate such equipment, even for non-profit services. In response to the need to bring connectivity in low-resource settings Wi-Fi long distance (WiLD) technology was developed from the remnants of the National Science Foundation Net [6]. Our framework considers two widely used approaches, WiLD and TVWS in limited-resource settings. The major contribution of the present work is to give a roadmap for decision makers to make the most efficient choice when faced with various options for implementing a telecommunications technology in low-resources settings. The rest of the paper is organized as following: Section 2 presents related work; a decision framework is presented in Section 3; in Section 4 a proposed framework is verified using multiple case studies, each with an existing network; in Section 5 three case studies in the design stage are evaluated and a solution based on the proposed framework recommended; Section 6 concludes the paper.
## 3 Simulation Results and Discussion
The minimum SNR for SDR is about 10 dB, ensuring a bit error rate below \(10^{-3}\) for a total emitted power of 15 mW. In general, regardless of the combining technique employed, CDR shows a better SNR and path loss than the SDR structure. It is also evident that in terms of both parameters, the CDR receiver provides the best performance. Although the self-orienting receiver exhibits a SNR almost similar to CDR, SOR presents a path loss worse than CDR, regardless of the combining technique applied. Furthermore, SOR needs to implement a SNR estimator, include a search algorithm that automatically aims the receiver towards the highest SNR, and incorporate an electromechanical orienting system. As was previously mentioned, the sensor nodes in a sensor network are limited in power and computational capacity. Therefore, using the SOR as the input sensor for the nodes in an indoor infrared-based sensor network requires additional power consumption and computational capacity over using a CDR in conjunction with EGC. In short, the use of a conventional angle-diversity receiver in conjunction with the EGC technique yields the best trade-off between SNR, computational capacity, and transmitted power. Finally, the use of multi-beam transmitters in conjunction with angle-diversity receivers and power efficient modulation schemes should be analyzed in future research in order to minimize the transmitted power requirements due to the communication between the sensor nodes in an infrared-based sensor network. This work was funded by the Spanish Government (MINECO) and by the European Regional Development Fund (ERDF) programme under project TEC2013-47682-C2-2-P.  
[PERSON] coordinated the research. [PERSON] and [PERSON] developed the models for non-imaging angle-diversity receivers, designed the simulation experiments and analysed the simulation results. The equations for the noise and signal to noise ratio were derived from the revision conducted by [PERSON], [PERSON], [PERSON] and [PERSON] programmed the simulation tool. The manuscript was mainly drafted by [PERSON] and [PERSON]. The authors declare no conflict of interest.
## 6 Conclusions  
While the classic CS-based algorithm is subject to performance degradation due to the ignorance of time-varying characteristics and block sparsity of the UWA channel, in this study, we investigate the exploitation of block sparsity for DCS estimation in the block sparse time-varying UWA channel. The key idea is that by modeling the time-varying UWA channel with blocks concatenation, the BOMP algorithm is applied to exploit the block sparsity of UWA channel. Furthermore, the support additions are computed to track the time-varying multipath. Note that the performance of block sparse channel estimation is affected by the block size, and we adopt a larger block size when the sparsity of channel is smaller. Otherwise, a smaller block size is more appropriate.  
Numerical simulations and a sea experiment are provided to demonstrate the effectiveness of the proposed algorithm, and the results show that for the block sparse time-varying UWA channels, the proposed BS-CS method outperforms the benchmark algorithms due to the exploitation of the block and time-varying sparsity. In future research, we will further test our proposed BS-CS algorithm using the watermark benchmark [32], so that a standard sound channel can be used as a reference for our estimations. Moreover, we will design the channel equalizer to handle the block sparse time-varying features in the UWA communication.  
Conceptualization, W.J. and L.X.; methodology, W.J.; software, L.X.; validation, L.C., Y.L. and W.J.; formal analysis, W.J.; investigation, L.C.; resources, L.X. and Y.L.; data curation, L.C.; writing--original draft preparation, W.J. and L.X.; writing--review and editing, W.J. and Y.L.; visualization, Y.L.; supervision, W.J.; project administration, W.J., L.X. and Y.L.; funding acquisition, L.X. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangdong marine economic development project (No. GDNRC2021-31), through the National Natural Science Foundation of China (No. 11874302).  
Not applicable.  
Code for data analysis is available on request from the authors.  
The authors declare no conflict of interest.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 3 Discussion
### Significance of NMR to CBM Development  
The NMR \(T_{2}\) spectra of coal samples in different states not only reflect the scale and distribution of pores and fissures in the coal sample, but also reflect the migration of water during centrifugation. The difference between the saturation and centrifugal \(T_{2}\) spectra can be used to evaluate the distribution and connectivity of pores and fissures in the coal reservoir. Depending on the characteristics of the \(T_{2}\) spectra of the saturated coal samples, the porosity and fluid saturation can be calculated, and the admissibility of CBM can be evaluated.  
In CBM production, methane desorbs, diffuses, and flows in connected a pore and fissure system, and the transport mechanism of methane in various types of pores is different (M  
information of pores and fissures is obtained by NMR. They control the gas storage capacity of the coal reservoir and explore the potential of CBM. The gas transport mechanism and scale in the nanopores and micro-fissures will be the focus of future research.
# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
## 5 Conclusions  
This study utilized remote sensing data from various growth stages of wheat in the Guanzhong Plain, alongside meteorological and soil data from TerraClimate, to predict wheat carbon flux at different growth stages using the multimodal deep learning framework, the MCFEW model. The results of the wheat carbon flux estimates demonstrate that the model effectively simulates and forecasts wheat carbon flux in the region, achieving an average adjusted R\({}^{2}\) value of 0.88, which indicates strong explanatory power and predictive accuracy. The RMSE of 5.31 gC\(\cdot\)m\({}^{-2}\cdot\)8d\({}^{-1}\) and nRMSE of 0.05 further suggest that the model performs with low error, showing minimal fluctuations across the growth stages. Among the features, LAI, NDVI, and EVI were identified as the most influential for carbon flux estimation, underscoring the significant role of remote sensing indices. Among meteorological variables, Vap emerged as the most important feature, followed by Pet, PDSI, and Pr. These climatic factors are closely associated with drought conditions and crop water content, while temperature and soil conditions had a lesser impact on the carbon flux estimates. The spatial distribution maps of the estimation results revealed areas with generally good wheat growth, highlighting regions where local land quality and climate conditions are conducive to wheat cultivation. This study offers an effective methodology for monitoring and estimating wheat carbon flux, contributing valuable data and insights for future research and applications, while demonstrating the potential of the model for carbon flux prediction.  
Conceptualization, writing--original draft, methodology, validation, X.C.; Methodology, supervision, writing--review and editing, Y.D.; Funding acquisition, conceptualization, D.H. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (42401398) and Beijing Forestry University Fundamental Research Funds for the Central Universities (BLX202362).  
The dataset is available on request from the authors. The authors declare no conflicts of interest.
## 2 Materials and Methods
### SpectraViT Model
#### 2.2.1 Inverted Residual Module  
The core idea of residual connections [28] is to directly pass the input signal to subsequent layers through skip connections, allowing the network to better retain feature information. The inverted residual structure [29] was first introduced in the lightweight and modular MobileNetV2 model. It addresses the issues of gradient vanishing and gradient exploding during the training process of convolutional networks. The input dimension is first reduced through projection convolution, then features are extracted using depth-wise separable convolution, and, finally, the output dimension is increased again through projection convolution. This approach includes a linear bottleneck to prevent information loss. Compared to traditional residual structures, it offers a higher accuracy, stronger generalization ability, and reduces the number of parameters and computational load. Our model uses two residual modules (Figure 1) for local feature learning of spectral signals, improving accuracy compared with traditional CNN structures. Each residual module includes two 1 \(\times\) 1 convolutional layers and a 1 \(\times\) 20 depthwise separable convolutional layer. The SiLU [30] activation function is used for feature extraction from the spectral signals. The second residual module is used for downsampling, with a convolution stride set to 2 to reduce the feature dimensions.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
###### Abstract  
The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car.  
passenger car; automatic emergency braking; hardware-in-the-loop system; dynamic simulation 2022
## 4 Trnsys Simulation and Operation Strategy Optimization  
### System Model and Results Comparison  
According to the actual equipment parameters and system form of the SA-ASHP water heating system, a simulation model was established using TRNSYS software, and the simulation calculation results were compared with experimental test results to verify the accuracy of the simulation model. The TMY2 typical meteorological year generated using METEONORM software was used as a meteorological file for the simulation model. The subject had a latitude of 39\({}^{\circ}\)06\({}^{\prime}\) N, a longitude of 117\({}^{\circ}\)10\({}^{\prime}\) E, an altitude of 3.3 m, and the time zone is located in the East Eighth District.  
This simulation model shown in Figure 11 included 12 types of simulation modules: a vacuum tube solar collector (Type 71), air source heat pump (Type 505b), water tank (Type 4c), water pump (Type 3b), meteorological data reading and processing (Type109-TMY2), temperature controller (Type 2b), water load distribution (Type 14h), supplemental water temperature distribution (Type 14e), integrator (Type 24), plotter (Type 65d), printer (Type 25c) and calculator. The water load distribution module and the make-up water temperature distribution module used the measured water consumption and tap water make-up temperature during the experimental test when the distribution function was written, thereby improving the accuracy of the simulation model and reducing the error from the theoretically assumed water load and make-up temperature zone.  
The relative errors between the simulated results and the measured results of solar heat collection, air source heat pump heating and solar energy contribution rates were 2.90%, 4.66% and 3.83%, respectively. The relative error of each parameter was small, which indicated that the simulation results were in good agreement with the measured results, which verified the accuracy of the established simulation model.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 2 A Low-Complexity Digital Filter to Model Mobility
### Time Varying Fractional Delay Line
The position of this delay can then be updated for each time instant in order to implement the desired time-varying resampling. Working with the fractional delay has two main benefits to model mobility. First, any resampling rate can be implemented with low computational complexity since the time-varying delay will simply be different for different resampling rates. Second, this model accounts for dynamically changing velocity between the receiver and transmitter, because it relies on the time-varying delay, \(\tau_{p}(t)\), which can be simply calculated for any dynamic scenario. To verify the functionality of the TVFDL, the resampling operation is applied to a reference waveform using different resampling methods and the results are compared in Figure 3. In this simulation, the scaling factor \(\alpha=1.0167\), which is equivalent to a constant relative velocity of \(|v_{c}|=+25\) m/s, and compresses the received signal. The sound velocity \(c_{w}\) is expected to be constant along the water column and is equal to 1500 m/s. As shown in Figure 3, the TVFDL has a similar result to linear interpolation methods. To demonstrate the ability of TVFDL in implementing a wide variety of mobile deployment scenarios, a deterministic channel model with four paths is implemented. As described in [16], the time-varying characteristics associated with each path are modelled using different geometric functions. Specifically, the first and second paths have a constant velocity of \(v=0\) and \(v=2\) m/s, respectively. In addition, the third and fourth paths are subject to a time-varying velocity with a dynamic and constant acceleration rate, respectively. Note that, for demonstration purposes,  
Figure 2: The time-varying fractional delay line: the signal is delayed by the integer value, while the sinc function implements the fractional delay. the amplitude of these paths is constant. While the objective here is to demonstrate the flexibility of the TVFDL, physical channel models developed using the proposed technique are validated against real measurement results in [16]. To verify the functionality of this model, the channel impulse response is estimated using a cross-correlation filter and the impulse response as a function of time is shown in Figure 4. The output demonstrates the capacity of the model to represent various mobility conditions. In the next section, a receiver is described to compensate for Doppler shift.