esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Description of Carbon Felt Electrical Properties Affected by Compression
## 4 Results  
(1) The comprehensive investigation of CF electrical conductivity properties was carried in the present article.  
(2) The dependence of resistivity versus compressing rate was obtained as in dry conditions (in air) and when CF is immersed in different dielectric liquids.  
(3) Electronic resistivity was measured by applying a DC current and an AC current at several specific frequencies (100 Hz, 120 Hz, 1 kHz and 10 kHz), respectively.  
(4) Considering the requirement to verify the influence of dielectric liquid parameters on CF conductivity, four sets of tests were performed. Resistance was measured in dry conditions and with the CF immersed in different (glycerol, alcohol, and cyclohexane) non-conducting (dielectric) liquids. The use of dielectric liquids instead of real electrolytes was justified to prevent the influence of electrolyte ionic conductivity on the measurement results.  
(5) Approximating function with their specific coefficients for description average surface conductivity of CF was found. The proximity of this function to the real data was determined.  
(6) Stochastic dispersion of individual carbon filaments and their disposition angles (vertical and horizontal) were established and were described further by a gamma-distribution function.  
(7) Analytical description of CF conductivity based on a stochastic approach and represented by the integral of conductivity was developed.
## 5 Measurement Results
### Validation of Jitter Generation  
The jitter generation in this design is mainly based on the TIE jitter in the time domain model. Therefore, the results focus on its TIE track and histogram with an oscilloscope.  
The bathtub curve, eye diagram, TIE track, and histogram of sinusoidal jitter are shown in Figure 12, where test signal frequency is 200 MHz, the jitter frequency is 10.27 MHz, the period jitter is 23.972 ps, and the random jitter is 1.737 ps. Generated sinusoidal jitter can simulate jitter caused by power supply noise, on-chip oscillators, etc. It can also be used for jitter tolerance testing. The histogram and TIE track of rectangular jitter are shown in Figure 13, where test signal frequency is 400 MHz, the jitter frequency is 1.632 kHz, the period jitter is 1.0339 ns, and the random jitter is 2.3 ps. The histogram and TIE track of DCD jitter is shown in Figure 14, where test signal frequency is 200 MHz, the jitter frequency is 194.753 MHz, the DCD jitter is 812.021 ps, and the random jitter is 2.1 ps. DCD jitter is only for clock patterns of repeating 0101 bits, and its frequency is correlated with the data rate. The TIE track of DCD generated by Equation (9) is a triangular wave composed of two points as shown in Figure 12, and the histogram of DCD can be seen as the Dual-Dirac delta function. Generated DCD jitter can simulate DCD during signal transmission. The histogram and TIE track of Random jitter are shown in Figure 15, where test signal frequency is 200 MHz, and the root mean square (RMS) of RJ is 436 ps. The adjustable range of RJ is from 14 ps to 8.33 ns, and generated RJ can simulate noise environment during signal transmission.  
Figure 11: Installed system of the experiment.  
Figure 14: TIE track and histogram of duty cycle distortion (DCD) jitter.  
Figure 12: Eye diagram, time interval error (TIE) track and histogram of sinusoidal jitter.  
Figure 13: Histogram and TIE track of rectangular jitter.  
Jitter generation on the rising of data pattern is shown in Figure 15; the green signal is original signal and the pink signal is jittery signal on the rising of data pattern. According to the definition of TIE jitter, the difference between two solid lines in Figure 16 is the jitter, and the dotted line in Figure 15 indicates that the falling edges of two signals are consistent. The phase difference between the rising edges of the green and pink signals can be used to test or implement a synchronous trigger circuit. Jitter amplitude encompasses a range of 12 ps-(cycle*0.1) ps, a step of 2 ps.
# Study of the Critical Speed of Ultra-High-Speed Diamond Drilling Technology in Marble
## 2 Rock Breaking Simulation Model
### Rock Constitutive Model and Failure Criterion  
In finite element method (FEM) analysis, the precision of the simulation outcomes is directly influenced by the choice of the material model. Rock, being a nonlinear and anisotropic material, presents a complex interaction when in contact with diamonds. The primary constitutive models employed for rock materials typically encompass the Mohr-Coulomb and Drucker-Prager criteria [19; 23; 24; 25]. The Drucker-Prager (D-P) criteria consider both the intermediate principal stress and hydrostatic pressure, addressing the limitations of the Mohr-Coulomb (M-C) criterion. Hence, in this paper, the D-P criterion has been chosen. The expression of the D-P criteria is as follows:  
\[\alpha I_{1}+\sqrt{J_{2}}=K \tag{3}\]  
\(I_{1}\) represents the first invariant of stress, \(J_{2}\) stands for the second invariant of stress, and \(K\) is the empirical constant associated with the internal friction angle of the rock and the cohesive strength of the rock. The expression is as follows:  
\[I_{1}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{4}\]  
\[J_{2}=\frac{1}{6}\Big{[}(\sigma_{1}-\sigma_{2})^{2}+(\sigma_{2}-\sigma_{3})^{ 2}+(\sigma_{3}-\sigma_{1})^{2}\Big{]} \tag{5}\]  
When the D-P criterion aligns with the M-C criterion, the expressions for \(\alpha\) and K are as follows:  
\[\alpha=\frac{2 sin\varphi}{\sqrt{3}(3-sin\varphi)} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Density** & **Young’s** & **Poisson’s** & **Angle of** & **Flow Stress** & **Dilation** & **Yield Stress for DP** & **Fracture** \\  & **(kg/m\({}^{3}\))** & **Modulus (GPa)** & **Ratio** & **Friction (\({}^{\circ}\))** & **Ratio** & **Angle (\({}^{\circ}\))** & **Hardening (MPa)** & **Strain** \\ \hline Diamond & 3510 & 890 & 0.07 & & & & & \\ Marble & 2650 & 40 & 0.25 & 34.87 & 1 & 20.3 & 30 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of the material.  
\[K=\frac{6 ccos\varphi}{\sqrt{3}(3-sin\varphi)} \tag{7}\]  
\(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) represent the principal stresses in rock and \(\varphi\) is the rock internal friction angle. The equivalent plastic strain criterion is employed to ascertain the failure of rock elements in the simulation of rock fragmentation through ultra-high rotary cutting. The expression is as follows:  
\[\varepsilon^{p}\leq\varepsilon_{f}^{-pl} \tag{8}\]  
\(\varepsilon^{p}\) represents the equivalent plastic strain of the rock, while \(\varepsilon_{f}^{-pl}\) denotes the equivalent plastic strain of the rock at the point of complete failure. When a rock element reaches an equivalent plastic strain of \(\varepsilon_{f}^{-pl}\), it is subsequently removed from the rock model body, indicating complete failure at that stage.
## 4 Stress Distribution Law in Island Working Face
### Numerical Simulation Results Analysis
#### 4.2.3 The Stress Distribution Influenced by Working Face Length  
For different working face lengths, the stress distribution characteristics of the island working face are obtained by simulation, as shown in Figure 13(a). The plastic zone range of roadway surrounding the rock and coal pillar decreases gradually with the increase of working face length, and the peak stress of the isolated island working face decreases gradually. Figure 13(b) shows the variation law of the coal pillar stress and the evolution characteristics of the plastic zone under different working face lengths, which indicates that when the length of the working face is 100 m, 150 m, 200 m, and 250 m, the total proportion of the number of grids in the plastic zone proportion of the coal pillar proportion is 67.29%, 58.75%, 56.25%, and 55.21%, respectively, and the corresponding peak stress is 40.2 Mpa, 36.9 Mpa, 33.5 Mpa, and 33.7 Mpa, respectively, which is roughly decreasing. The variation law of the peak stress of the entity coal under different working face lengths is shown in Figure 13(c). The peak stress of the entity coal is 40.3 Mpa, 33.8 Mpa, 32.2 Mpa, and 31.2 Mpa, respectively, which decreases gradually with the increase of working face length. The analysis results show that with the increase of the length of the working face, the bearing stress range of the entity coal itself is more widely distributed, while the stress of the coal pillar is relatively reduced. This phenomenon shows that increasing the length of the working face helps to reduce the abutment stress of the entire island working face. Therefore, in the design and planning of island working faces, we should try to avoid the working face length being too small to reduce the risk of an induced rock burst. At the same time, it is necessary to reasonably increase the length of the working face, but it should be noted that the length of the working face should not be too long, so as not to adversely affect the mining efficiency.
## 1 Introduction
Reference [13] comprehensively considered the uncertainty of DG, and established an optimization model by analyzing the flexibility of ADN. Reference [14] proposed an optimal allocation method of storage-assisted peak shaving with consideration of economy and flexibility, and established a two-layer optimization allocation model in response to the problem of insufficient flexibility of the system due to the strong stochastic fluctuation of wind power. Reference [15] studied the coordinated operation optimization of ESS and DG, and proposed a control method based on Model Predictive Control (MPC). Reference [16] proposed a two-stage joint method of SOP and ESS based on inter-area optimization to improve the economy of ADN. Reference [17] considers the synergistic planning of ESS and SOP for demand response and maintaining voltage stability, with the objective of improving the efficiency of the distribution network. Reference [18] considers ADN with source-load imbalance characteristics and proposes a synergistic planning model for SOP and ESS, with the objective of minimizing the annual integrated cost. All of the above references are studied from the perspective of individual or coordinated operation optimization of SOP and ESS, without extending the synergy of SOP and ESS to the study of optimization strategies for flexibility enhancement of AND. Therefore, there is a need to study the coordinated planning of SOP and ESS considering the flexibility enhancement of ADN. In summary, the objective of this paper is to consider the economics and flexibility of ADN, with a view to developing a two-tier coordinated planning model for SOP and ESS. Firstly, the ADN power-side flexibility assessment metrics are established on the basis of the temporal and spatial regulation characteristics of SOP and ESS. The DG scenario generation method based on C-DCGAN is used, and a planning model with SOP and ESS is constructed to satisfy simultaneously the economy and flexibility of the system. Among them, the upper layer considers economy, with integrated cost, as the objective; the lower layer considers flexibility, with the highest level of flexibility as the objective. Finally, GA-PSO and SOCP are used to tackle the model.
# Elevation and Mass Changes of the Southern Patagonia Icefield Derived from TanDEM-X and SRTM Data
## 3 Results  
### Elevation Changes: SPI 2000-2015/16  
Figure 2 shows the elevation change rates (dh/dt) between SRTM (2000) and the mosaicked TDX acquisitions from austral summer 2015/16. The spatial pattern of changes is quite heterogeneous. The accumulation areas of most glaciers show no to slightly positive changes in elevation uniformly over the whole icefield from about 49.77\({}^{\circ}\) S to the southern end of the SPI. However, some glacier catchments such as Upsala, Viedma, Chico, O'Higgins, Occidental and Jorge Montt show thinning rates of up to 2 m\(\cdot\)a\({}^{-1}\) also in the accumulation areas. There is no marked difference between the east and west side of the icefield in this northern part of SPI. Only Pio XI Glacier shows overall balanced to slightly positive elevation changes. This catchment is also particular since the glacier is known to have advanced [36] and the positive elevation changes at the tongue (up to 8 m\(\cdot\)a\({}^{-1}\) within the period 2000-2015/16) document this dynamic behavior. The hypsometry of Pio XI Glacier shows the largest proportion of its catchment area to be above 1000 m (Figure 2c). South of 49.7\({}^{\circ}\) S, almost balanced conditions exist for many glaciers including Perito Moreno and Asia glacier. These glaciers have  
considerable areas above 1000 m surface elevation. Perito Moreno Glacier shows a marked hyposometric minimum around 1000 m (Figure 2d). The adjacent Ameghino Glacier contrasts this pattern with considerable elevation decrease with up to \(-\)10 m\(\cdot\)a\({}^{-1}\). As Perito Moreno Glacier, it exhibits a bi-modal distribution of its surface elevation, but with more area below 1000 m and a second maximum around 1900 m. Glaciers at the southern end of SPI, i.e., Amalia, Tyndall and Grey glaciers, show overall negative elevation changes. While the elevation changes at the tongues of e.g., Grey, Amalia and Chico glaciers reach up to \(-\)6 to \(-\)8 m\(\cdot\)a\({}^{-1}\) at maximum, Tyndall and Viedma glaciers show smaller maximum lowering rates with values up to \(-\)4 m\(\cdot\)a\({}^{-1}\). Still, the mean elevation changes for the entire glaciers are among the most negative ones (Figure 2b and Supplementary Materials Table S2). All those glaciers have a large proportion of their catchment areas below 1000 m. Highest lowering rates are observed on glaciers HPS12, Upsala and Jorge Montt with mean elevation changes ranging from \(-\)4.3 to \(-\)3.2 m\(\cdot\)a\({}^{-1}\) and peak losses reaching \(-\)51, \(-\)19 and \(-\)27 m\(\cdot\)a\({}^{-1}\) for the mentioned glaciers, respectively. These three glaciers cover 95.4% of the area that experienced more than \(-\)10 m of elevation change per year (157.11 km\({}^{2}\) in total). Whereas Jorge Mont Glacier does not show the most extreme minimum value, it is the glacier with the largest extent of decrease below \(-\)10 m\(\cdot\)a\({}^{-1}\) (94.87 km\({}^{2}\)). Those glaciers show a hyposometric distribution that hardly reaches 2000 m, with the major part below the snow line altitude.
# A Spam Filtering Method Based on Multi-Modal Fusion
###### Abstract  
In recent years, the single-modal spam filtering systems have had a high detection rate for image spamming or text spamming. To avoid detection based on the single-modal spam filtering systems, spammers inject junk information into the multi-modality part of an email and combine them to reduce the recognition rate of the single-modal spam filtering systems, thereby implementing the purpose of evading detection. In view of this situation, a new model called multi-modal architecture based on model fusion (MMA-MF) is proposed, which use a multi-modal fusion method to ensure it could effectively filter spam whether it is hidden in the text or in the image. The model fuses a Convolutional Neural Network (CNN) model and a Long Short-Term Memory (LSTM) model to filter spam. Using the LSTM model and the CNN model to process the text and image parts of an email separately to obtain two classification probability values, then the two classification probability values are incorporated into a fusion model to identify whether the email is spam or not. For the hyperparameters of the MMA-MF model, we use a grid search optimization method to get the most suitable hyperparameters for it, and employ a k-fold cross-validation method to evaluate the performance of this model. Our experimental results show that this model is superior to the traditional spam filtering systems and can achieve accuracies in the range of 92.64-98.48%.  
Keywords:spam filtering system; multi-modal; MMA-MF; fusion model; LSTM; CNN +
Footnote †: journal: Applied Sciences
## 3 IoT Applications
### Internet of Vehicles (IoV)  
With the development of industry, the number of vehicles is increasing rapidly. The increment of vehicles raises security concerns that trigger secure communication. IoV will lead industry 4.0. It is certain that the IoV will be bright and profitable in the future, offering improved road safety, reduced environmental effects, better space utilization, and cost control. The IoV, often known as smart transportation or connected cars, is a framework comprising vehicles, smartphones and wearables, roadside equipment, and a network. People, cars, and numerous IoT devices that are part of the transport system communicate over IoV [56]. Transportation, production, energy, software, and other industries are all affected. The IoV ecosystem includes hardware, software, services, and multiple network technologies ranging from Bluetooth and cellular to Wi-Fi and 5G, as well as several types of communication (V2V, V2X, and so on). Vehicle-to-vehicle and vehicle-to-infrastructure communication systems are combined to create the vehicular ad hoc network or VANET, and the term IoV has developed from the notation VANET. The combination of functionalities such as sensing units, control platforms, and various computer resources makes each vehicle in IoV an intelligent object. Each vehicle connects to any entity via a V2X communication architecture. The aim of IoV, also known as V2X, is safe driving by reducing accidents, alleviating traffic congestion, providing low traffic route information, and providing other information services. Every vehicle in the IoV network interacts with all other things that could have an influence on it. V2X mainly includes vehicle-to-vehicle (V2V), vehicle-to-sensors (V2S), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) communication. Nevertheless, roads can be seized by modifying or changing data or making wrong decisions due to the receipt of prank data [57]. To avoid these kinds of situations, it is required to develop a robust authentication framework that can resist security vulnerabilities and can conduct verification within milliseconds.
## 2 Finite Element Analysis
### Load Analysis  
To find the input current condition, a torque analysis was performed based on changes in the current as a function of the current phase angle, as shown in Figure 3. The current condition that satisfies the rated torque of 60 N\(\cdot\)m at the rated speed of 2387 rpm requires that the RMS value of the current is 146 A and the current angle is 25\({}^{\circ}\). Load analysis was performed under this current condition. The average torque was found to be 60.05 N\(\cdot\)m and the torque ripple was 6.951% of the torque. The output power of the motor can be calculated as the product of torque and angular velocity. If the mechanical loss is ignored, total losses of the motor are the sum of the core loss and the copper loss. The efficiency can be calculated from the output power and total losses of the motor. The efficiency of the initial model was calculated to be 91.43%.
## 5 Discussion
### Evolution Law of Microcrack Number  
The compressional failure of rocks involves initiating and developing microcracks within the rock. Figure 14 illustrates how the dip angle and length of fissures relate to the evolution of the microcrack number. Figure 14a illustrates that for \(\alpha\) = 0\(\sim\)45\({}^{\circ}\), the number of cracks shows a \"step\" growth trend in both the crack development and rapid growth phase. This mode shows more obvious tensile failure in the failure modes. In contrast, when \(\alpha\) = 60-90\({}^{\circ}\), the number of cracks grows steadily in the crack development phase and steeply in the rapid growth phase, similar to the evolution of the number of cracks in the fissure-free specimen. The shear failure in the failure modes is also significant in this case. Figure 14b shows that when the fissure length is small (2a = 4 mm), the crack growth pattern is consistent with that of the fissure-free specimen. However, for more considerable fissure lengths (2a = 16 mm), the mechanical properties of the specimen decrease, and the number of cracks decreases as well. It can be inferred that the evolution law of crack number corresponds to the mechanical properties and failure modes. When the dip angle of fissures is slight, the deterioration effect on the mechanical properties of the surrounding rock mass by symmetric fissures around the hole is more significant. The fluctuation of crack evolution is vital, which is exacerbated by an increase in fissure length.  
Figure 14: Relationship between symmetric fissures and the evolution of microcracks. (**a**) Microcrack evolution law for specimens with different fissure dip angles (2a = 8 mm); (**b**) microcrack evolution law for specimens with different fissure lengths (\(\alpha\) = 45\({}^{\circ}\)).  
Figure 13: Symmetrical fissure-hole sandstone crack propagation process and force chain distribution (2a = 8 mm, \(\alpha\) = 45\({}^{\circ}\)). The subfigures (a)–(h) represent the crack extension pattern and microscopic force chain distribution corresponding to points a-h in the stress-strain curve.
###### Abstract  
This study evaluated the performance of remote sensing (RS) algorithms for the estimation of actual maize evapotranspiration (ET\({}_{a}\)) using different spaceborne, airborne, and proximal multispectral data in a semi-arid climate region to identify the optimal platform that provides the best ET\({}_{a}\) estimates to improve irrigation water management and help make irrigated agriculture sustainable. The RS platforms used in the study included Landsat-8 (30 m pixel spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), multispectral radiometer or MSR (1 m), and a small uncrewed aerial system or sUAS (0.03 m). Two-source surface energy balance (TSEB) models, implementing the series and parallel surface resistance approaches, were used in this study to estimate hourly maize ET\({}_{a}\). The data used in this study were obtained from two maize research sites in Greeley and Fort Collins, CO, USA, in 2020 and 2021. Each research site had different irrigation systems. The Greeley site had a subsurface drip system, while the Fort Collins site had surface irrigation (furrow). Maize ET\({}_{a}\) predictions were compared to observed maize ET\({}_{a}\) data from an eddy covariance system installed at each research site. Results indicated that the MSR5 proximal platform (1 m) provided optimal RS data for the TSEB algorithms. The MSR5 \"point-based\" radioric-looking surface reflectance data and surface radiometric temperature combination resulted in the smallest error when predicting hourly (mm/h) maize ET\({}_{a}\). The mean bias and root mean square errors (MBE and RMSE, respectively), when predicting maize hourly ET\({}_{a}\) using the MSR5 sensor data, were equal to \(-0.02\) (\(-3\%\)) \(\pm\) 0.07 (\(11\%\)) mm/h MBE \(\pm\) RMSE and \(-0.02\) (\(-3\%\)) \(\pm\) 0.09 (\(14\%\)) mm/h for the TSEB parallel and series approaches, respectively. The poorest performance, when predicting hourly TSEB maize ET\({}_{a}\), was from Landsat-8 (30 m) multispectral data combined with its original thermal data, since the errors were \(-0.03\) (\(-5\%\)) \(\pm\) 0.16 (\(29\%\)) mm/h and \(-0.07\) (\(-13\%\)) \(\pm\) 0.15 (\(29\%\)) mm/h for the TSEB parallel and series approaches, respectively. These results indicate the need to develop methods to improve the quality of the RS data from sub-optimal platforms/sensors/scales/calibration to further advance sustainable irrigation water management.  
remote sensing; evapotranspiration; crop coefficient; surface energy balance; irrigation 2024 164850. [[https://doi.org/10.3390/su16114850](https://doi.org/10.3390/su16114850)]([https://doi.org/10.3390/su16114850](https://doi.org/10.3390/su16114850))