esa-sceva/satcom-chunk-collection huggingface dataset:
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 4 Model Analysis
 \tag{4}\]  
On the other hand, we notice that \(QR_{i\bar{j}}\) satisfies the following condition:  
\[0\leqslant QR_{i\bar{j}}<P_{i-1} \tag{5}\]  
Therefore, according to Formulas (4) and (5), we obtain the upper limit and lower limit of the residence time of packets in regular burst mode:  
\[\sum_{k=i}^{m}P_{k}\leqslant RT_{i}<\sum_{k=i-1}^{m}P_{k} \tag{6}\]  
Formula (6) illustrates that the packet latency in regular burst mode is related to the batch \(B_{i}\) to which it belongs. When \(i\) is closer to \(m\), the packet latency is more minor, and conversely, the latency is larger. Comparing Formulas (3) and (6) and combined with the experimental results in Section 3.2, we can quickly conclude that on the data plane based on spin-polling mechanism and batch processing mechanism, the time of processing packets by CPU is an essential factor affecting packet latency. In contrast, the queuing time of packets in Rx/Tx queues is a direct factor. Reducing queuing time is a breakthrough in optimizing packet forwarding latency.
# Research on the Reciprocating Friction and Installation Force of Seals in Deep-Sea Samplers
## 4 Discussion
### Limitations of This Study  
By establishing a nonlinear finite element model that accounts for the temperature-pressure coupling effect (Section 3.1) and utilizing a self-developed deep-sea environment friction test platform (Figure 5), this study reveals the relationship between the installation force and the reciprocating friction of O-rings in deep-sea environments, as well as the influence of material and structural dimensions. However, some methodological constraints warrant acknowledgment. (1) This study demonstrates constraints related to material hardness, as indicated by its exclusive focus on NBR and FKM specimens standardized at Shore A 80 hardness. This work restricts a comprehensive assessment of elastomer performance variations across different hardness gradients (e.g., 70-90 Shore A), which may limit the predictive capabilities regarding the operational envelope for deep-sea sealing applications. (2) The torque limitations of the experimental drive system, constrained by budgetary parameters for instrumentation, imposed an operational pressure ceiling of 15 MPa for characterizing seal movement resistance. This threshold prevents direct measurement of interfacial friction dynamics at ultrahigh pressure.
## 4 Reactive Power Incentive Method  
DR methods can be roughly classified into two categories, such as TOU type and incentive type. DR using the TOU method depends on the electricity price [27; 44], which encourages the customer to change the usage of electric power. On the other hand, incentive-based DR is independent of electricity price, in which a customer can earn a profit from DisCo by the cooperative operation process. The customer side does not need to change the usage of power. Generally, DisCo has to invest in maintaining the power system, and the investment costs include equipment, maintenance and operation, which becomes a burden to the DisCo. However, these costs can be reduced through the cooperative operation of the customer side. A cooperative operation system is possible with HEMSsmart grid technology [45; 46; 47; 48]. The reactive power incentive concept provides an amount of profit to the customer to contribute to the sustainable power system of DisCo. If all customers follow the DisCo's request for the specified reactive power output \(Q_{PV}^{*}\), the DisCo can remove the unnecessary burdens of equipment costs. Therefore, the DisCo can provide incentives from saved money to customers participating in the cooperative operation program.
## Appendix A Analysis of Uncertainty of MAT Method
### MAT Evaluation of Permeability Loops  
During the evaluation, the permeability matrices of all samples were divided by the permeability matrices of reference (not-irradiated) sample(s). In this way, we obtained the correlation between the change of the magnetic parameters and the independent variable. Those parameters (MAT descriptors) were chosen from the big data pool, which resulted in the best correlation between the magnetic and independent parameters. Evidently, this process can also generate an error.  
A sensitivity map helps a lot for choosing the optimal MAT descriptor. The relative sensitivity map of the \(\mu\)-degradation functions, \(\mu\) (\(D\)\(B\)\(T\)) \(\equiv\)\(\mu\) (\(h_{a}\), \(h_{b}\)), is shown in Figure 11. The most sensitive MAT descriptors were taken from the top area of the \"hills\" (indicated by a red colour in Figure 11). The sensitivity map also indicates the reliability of the chosen descriptor. Big plateaus are favourable, where parameters only slightly depend on the actual choice of \(h_{a}\) and \(h_{b}\) field coordinates values. To minimize the error, we also performed a time check of all the neighbouring descriptors, and then we accepted the optimal MAT descriptor as satisfactory only if there were very small differences between the descriptors. This procedure also ensures that if the measurement is repeated (i.e., a long time after the first measurement), and if during the evaluation not the exact same magnetizing field (\(h_{a}\)) and minor loop amplitude (\(h_{b}\)) parameters are chosen, the error of the measurement remains small. This uncertainty of the MAT descriptor choice is the most significant error of the MAT measurement/evaluation, and it is considered as the real error of the measurement.  
This process is shown for the investigated samples. The MAT descriptor, characterized by \(h_{a}=-30\) mA and \(h_{b}\)= 1080 mA field coordinate values, was found to give the best correlation between the MAT parameters and independent variable. In Figure 11, those descriptors are also shown, which were taken from the neighbouring positions (\(h_{a}=-30\) mA, \(h_{b}=1080\) mA). Note that in this graph, the normalized MAT descriptors are given, because they are better in comparison in this case. It can be clearly seen that descriptors taken from this area are almost identical, and all of them properly express the correlation between the magnetic parameters and the transition temperature.
## 3 Results
### Linear Regression  
A polynomial of the first degree is chosen as the fitting curve, with the coefficient of determination R\({}^{2}\) evaluated for each season and year of the data for both sites. In addition, [PERSON]'s correlation coefficient (S) is used to quantify the magnitude and sign of the relationship between the ET\({}_{0}\) calculated through the HS formula and the SMC data. Figure 2a,b shows two examples of the results obtained for the Paterno and Busachi case studies, respectively (95% prediction intervals are also shown). Instead, the Table 2a,b shows the mean, median, and standard deviation (STD) of the statistical quantities of interest for the two selected case studies. Figure 2: Fitting lines data points of soil moisture values versus potential evapotranspiration values for spring 2006 in Paterno (**a**) and autumn 2018 in Busachi (**b**). Additionally, plotted in purple are the 95% prediction intervals. From the above tables, the seasonal results of the correlation coefficient statistics for the Paterno locality are consistent in magnitude and sign with those for the Busachi locality, except for the summer: in winter, ET\({}_{0}\) and SMC are related by a strong positive correlation (\(p<0.05\)), while in spring and autumn, a strong anticorrelation is found (\(p<0.05\)); in summer, a strong variability is found, going from positive to negative values (\(p<0.05\) or \(p>0.05\) depending on the year). Finally, the anticorrelation values in autumn are lower in modulus than in spring, probably due to the approaching winter. Regarding R\({}^{2}\) values, high and consistent values are obtained between the two locations, especially in spring and autumn: in these seasons, more than 60% of the variability of ET\({}_{0}\) is explained by the variability of SMC values. In winter this percentage drops to 50% but is still significant. Low R\({}^{2}\) values are obtained in summer, confirming the poor fitting that is obtained with a straight line for this season. The coefficients of the fitting lines show the consistency of magnitude and sign in the respective seasons over the twenty years of data, in the same case study and between the two case studies (except in summer). Moreover, the intervals of the values of the slopes and intercepts of the fitting lines of the two case studies have values in common. The statistical significance of the above results suggests that through the fitting lines found here, ET\({}_{0}\) values close to those of the HS formula can be obtained from the SMC data (satellite or in situ).
# Short-Term Effects of Pine Plantations on Vegetation and Soil in Northern Mongolia
## 2 Materials and Methods  
### Study Area and Site Description  
This study area is located in the territory of Tujin nars Nature Conservation Park, which administratively belongs to Selenge province (50\({}^{\circ}\)10\({}^{\prime}\) and 50\({}^{\circ}\)12\({}^{\prime}\) N, 106\({}^{\circ}\)14\({}^{\prime}\) and 106\({}^{\circ}\)31\({}^{\prime}\) E) in northern Mongolia with an elevation ranging from 650 to 750 m asl (Figure 1). The park stretches approximately 33 km from east to west and covers an area of 73,000 ha, of which 45,800 ha are natural pine forests and 21,000 ha are Scots pine plantations [4]. Tujin nars lies within the northern temperate and boreal forest that is distributed along the southern edge of the Siberian Taiga, at the forest steppe transitional zone called sub-taiga forests [13]. According to the updated world map of the Koppern-Geiger climate classification [14], the region lies within the transition climatic zone between a cool continental climate (Dwc) and a cold semi-arid climate (Bsk), with small pockets exhibiting a temperate continental climate (Dwb). The main tree species in this forest include Siberian larch (_Larix sibrica_ Ltd.), Scots pine (_Pinus sylvestris_ L.), Siberian pine (_Pinus sylivica_ [PERSON]), Asian white birch (_Betula platpyphylla_ Sukaczev.), and European aspen (_Populus tremula_ L) [15].  
The mean annual temperature was 0.6 \(\pm\) 0.002 \({}^{\circ}\)C and the mean annual precipitation was 280.3 \(\pm\) 7.3 mm with a precipitation peak between June and August (Figure 2) according to the nearest meteorological station located at Sukhbaatar (50\({}^{\circ}\)14\({}^{\prime}\)35.063\({}^{\prime\prime}\) N; 106\({}^{\circ}\)10\({}^{\prime}\)23.008\({}^{\prime\prime}\) E, 621 asl). The soils in the study site are Podzols (arenic) (also called Derno-Forest soils in Mongolia), which were derived from sandy sediments and loess. The sandy loess stratum, which provides the parent materials, is extremely thick and is widely distributed in the study region. The soil is characterized by relatively good water retention but is poorly vegetated due to the climate [16].  
Figure 1: (**A**) displays the map of Mongolia; (**B**) illustrates the map of Tujin Nars Nature Conservation Park, Selenge Province, Mongolia, characterized by various environmental zones; (**C**) illustrates that the study region is situated inside the Tujin Nars, characterized by several soil types; (**D**) elevation of the study area.
## 2 Materials and Methods
### Testing Method
#### 2.3.2 Electrochemical Tests  
Two electrochemical methods, linear potentiodynamic polarization and electrochemical impedance spectroscopy (EIS), have been used to evaluate the corrosion susceptibility.  
An electrochemical system, PARSTAT 2273 potentiostat/galvanostat (Princeton Applied Research, AMETEK, Oak Ridge, TN, USA), was used for all electrochemical measurements. Electrochemical studies were conducted using a traditional three-electrode electrochemical cell, which consists of an auxiliary electrode (graphite rod), a saturated calomel reference electrode (SCE), and a working electrode (the NiCrAlY-coated 310H SS sample). The characteristics of the oxide layer were unaffected by the electrochemical tests, which were conducted at room temperature (22 \(\pm\) 2 \({}^{\circ}\)C) in a chemically inert solution with a pH of 7.7-7.8 (0.05 M boric acid with 0.001 M borac solution). Nyquist and Bode diagrams were drawn using the impedance spectra, acquired at open-circuit potential (OCP) with an ac amplitude of 10 mV in the frequency range from 100 kHz to 100 mHz, following OCP stabilization [36]. To perform a quantitative analysis, the experimental EIS data weresimulated using analogous electrical circuits and Zview 2.90c software (Scribner Associates Inc., Southern Pines, NC, USA).  
To observe the corrosion behavior of the samples, the potentiodynamic polarization plots were recorded in a potential range of \(-\)0.250 V vs. OCP up to +1.0 V. The scan rate was 0.5 mV/s. Based on the polarization plots, using the Tafel slope method and the polarization resistance (Rp) method, the main electrochemical parameters that were specific to the corrosion process were determined, as follows: corrosion potential (Ecorr), corrosion rate (vcorr), corrosion current density (icorr), and polarization resistance (Rp). Based on these parameters, the oxide protection efficiency (Pi) and porosity coefficient (P) were calculated as well using Equations (2) and (3) [37,38].  
\[P_{i}(\%)=\left[1-\left(\frac{i_{corr}}{i_{corr}^{0}}\right)\right]\times 100 \tag{2}\]  
where  
\(\mathrm{i_{corr}}\) is the corrosion current density of the deposited layers;  
\(\mathrm{i_{corr}^{0}}\) is the corrosion current density of the substrate.  
\[P(\%)=\left(\frac{R_{ps}}{R_{p}}\right)\times 10^{-(\frac{\Delta E_{ corr}}{\beta_{a}})} \tag{3}\]  
where  
P is the total porosity of the deposited layer;  
Rps is the polarization resistance of the uncoated substrate;  
Rp is the polarization resistance of the coated sample;  
\(\beta_{a}\) is the anodic Tafel slope;  
\(\Delta\mathrm{E_{corr}}\) is the difference between the corrosion potentials of the coating and the substrate.
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 3 System Build-Up and Test Results
### Actual Measurement of Driving  
The test for actual measurement is carried out for three different movements: moving straight, stopping, and turning.  
When a vehicle stops on the side of the road, the recorded value of the GPS value drifts significantly. The drift is corrected by the three-axis acceleration measurement function. When the vehicle is stationed, the three-axis acceleration detection amount is zero (ax = 0), and the angular velocity w (\(\theta\) = constant) of the gyroscope is a constant value. d, a distance between two points is calculated based on the recorded value of GPS with the default value dmin of 2.0 m. When d is less than dmin, it is not recorded. Figure 29a shows the judgment without the three-axis acceleration, the angular velocity measurement value of the gyroscope, and dmin. The drift of GPS data become large when the vehicle stops and  
Figure 28: Result received by the backend server database.  
Figure 27: Microcontroller POST request code.  
stays. When adding the three-axis acceleration, the angular velocity measurement value of the gyroscope, dmin, and the drift significantly decreases Figure 29b.  
In a driving test, an average moving speed is about 40 km/h. Figure 30 shows that movement tracking is performed at sampling intervals of 1 and 5 s. In Figure 30a, when the sampling interval is long, blind spots occur at the corner, resulting in a large deviation in the point-to-point connection of the path movement tracking. Thus, a short sampling interval allows movement tracking to be more efficient, as shown in Figure 30b.  
However, the sampling interval is related to the speed of the vehicle. Figure 31a,b show the monitoring of the angular velocity change. Under the test speed of 40 km/h, \(\theta\) = \(\pm\)300 and \(\theta\) = \(\pm\)150 are used to track the movement tracking at the turn. The red dots represent the coordinates recorded by the GPS. The actual measurement found that the movement is effectively recorded at the turn. The smaller the \(\theta\), the denser the tracking points.  
Figure 32 shows the test of the motion tracking in a straight line on a highway with a path length of 350 m. The GPS data are recorded every 0.5 s, as shown in Figure 32a and when the attitude sensor detects the acceleration value (ax = \(\pm\)0.1 g). The comparison shows that the use of acceleration value changes requires less data storage to track the movement, as shown in Figure 32b.  
Figure 30: GPS sampling rate test (vehicle speed of 40 km/h): (a) 5 s sampling interval; (b) 1 s sampling interval.  
Figure 29: GPS value test chart of a stopped vehicle: (a) current situation of GPS value drift; (b) current status of GPS value drift improved.
## 4 Experiments
### Experimental Results
#### 4.5.2 Model Structure Comparison
The adaptive adjacency could learn the hidden spatial dependency existing in the road network and the diffusion convolution could capture localized spatial correlations. In the temporal dimension, Graph WaveNet employs stacked dilated casual convolution (DCC) to obtain temporal dependencies. The stacked dilated casual convolution's receptive field grows exponentially as the number of layers increases and can handle long sequence very well. Therefore, as shown in Table 3 and Figure 9a, Graph WaveNet performs better than ST-MetaNet. Our proposed model GSTGCN integrates the spatiotemporal correlations of traffic data and the influence of external factors together. In the temporal dimension, we employ three spatial-temporal components considering multiple temporal periodicities, and we use stacked dilated casual convolution (DCC) with residual connection to obtain temporal dynamics in each component. In the spatial dimension, we model local and global correlations through a global correlated module, which contains K-order Chebyshev graph convolution and a global correlated mechanism. When constructing the adjacency matrix, we consider not only the distance between the geographic locations of the sensors, but also the surrounding points of interests (POIs) data to explore the functional similarity of the area where the sensors are located. In addition, we take external factors into account using fully connected layers. Compared to Graph WaveNet, GSTGCN considers multiple temporal periodicities, global spatial correlations, and the impact of external factors on traffic data. Hence, the experimental results demonstrate that GSTGCN achieves the best prediction accuracy on all metrics.
# Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction
## 5 Conclusions  
In this study, we proposed a novel application of the CNN-GRU model for GNSS deformation monitoring. Our objective is to leverage the capabilities of deep learning in capturing complex spatiotemporal patterns inherent in GNSS time series to enhance modeling and prediction in hydraulic structures. Through comprehensive analysis and experimentation, we demonstrated the effectiveness of the CNN-GRU model in modeling and prediction for GNSS time series. The CNN model efficiently extracted features, capturing localized deformations and spatial dependencies. Meanwhile, the GRU model effectively models the temporal dynamics and long-term dependencies, enabling the accurate prediction of future deformations.  
Our results indicated that the CNN-GRU model outperformed traditional methods with about 45% improvement. By jointly leveraging convolutional and recurrent architectures, the proposed model achieved superior performance in capturing both spatial and temporal characteristics of deformation patterns, thereby enhancing the accuracy and reliability of deformation prediction in hydraulic applications.  
Overall, this study demonstrates the potential of deep learning approaches, particularly the CNN-GRU model, in advancing GNSS deformation monitoring and prediction capabilities in GNSS deformation monitoring for hydraulic structures. By effectively integrating spatial and temporal information, this approach offers a promising avenue for improving the efficiency, accuracy, and reliability of deformation monitoring systems, ultimately contributing to the safety and sustainability of a hydraulic infrastructure.  
Conceptualization, Y.X., J.W. and A.D.; Data curation, H.L. and Y.K.; Formal analysis, Y.X. and H.L.; Funding acquisition, A.D. and Y.X.; Methodology, H.L., Y.K. and Y.X.; Project administration, J.W.; Resources, J.Z. and Y.W.; Software, H.L. and Y.Y.; Writing--original draft, H.L.; Writing--review and editing, J.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Provincial Department of Water Resources, grant number 2022050 and 2022083, the Jiangsu Hydraulic Research Institute, grant number 20232036 and 2023z045, and the Department of Natural Resources of the Hunan Province, grant number 20230104.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
## 3 Proposed GRU-MC-AE Network Architecture
The operation of the gates and hidden states in the GRU can be expressed as follows:  
\[d_{t}=\sigma(W_{d}\cdot\hat{x}_{t}+U_{d}\cdot h_{t-1}+b_{d}), \tag{5}\]  
\[R_{t}=\sigma(W_{R}\cdot\hat{x}_{t}+U_{R}\cdot h_{t-1}+b_{R}), \tag{6}\]  
\[\hat{h}_{t}=\tanh(W_{h}\cdot\hat{x}_{t}+U_{h}\cdot(R_{t}\odot h_{t-1})+b_{h}), \tag{7}\]  
\[h_{t}=(1-d_{t})\odot h_{t-1}+d_{t}\odot\hat{h}_{t}, \tag{8}\]  
where \(\sigma\) represents the sigmoid activation function, \(tanh\) denotes the hyperbolic tangent function, \(\odot\) represents element-wise (Hadamard) multiplication, \(b_{d}\), \(b_{R}\), \(b_{h}\) are the bias vectors, \(W_{d}\), \(W_{R}\), \(W_{h}\) are the weight matrices, and \(U_{d}\), \(U_{R}\), \(U_{h}\) are the recurrent weight matrices for the update, reset, and candidate hidden state, respectively. The final layer of the decoding section is a dense layer with \(M\) nodes, utilizing a softmax activation function. By utilizing the softmax, the decoder produces a probability vector \(\hat{s}=[\hat{s}_{1},\dots,\hat{s}_{M}]^{T}\), where the \(i\)-th entry represents the likelihood that the corresponding message is transmitted. The largest entry of \(\hat{s}\) is used to determine the estimated message \(\hat{s}\). Applying softmax enables the model to produce a clear and interpretable output with values that sum is \(1\). This allows the output to be evaluated based on the correct predicted symbol, similar to a classification setup in communication systems [41]. If \(\theta_{\text{dec}}=\{W_{i},b_{i}\}_{i=1,2}\) denote the parameters and \(z\) denotes the input of the output layer, the output is expressed as follows:  
\[\hat{s}=\sigma_{\text{Softmax}}(Wz+b), \tag{9}\]  
where \(\sigma_{\text{Softmax}}\) represent the softmax activation functions. The detailed signal detection process for the proposed GRU-based decoder in the MC-AE system is systematically presented in Algorithm 1. This algorithm provides a structured overview of each stage,  
\[\mathbf{u}=\mathbf{Ws}+\mathbf{b}, \tag{3}\]0 The internal structure of a GRU model. including data generation, AE model design, training, and testing. The step-by-step organization offers a clear perspective on the entire workflow.