esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### Oil-Gas Source Comparison  
Oil and gas sources were determined mainly based on comparisons of the geochemical features of the oil, gas, and source rocks. The goal was to understand the relationships between oil accumulation, gas accumulation, and source rock accumulation in this petro-lifeous basin to further delineate reliable oil and gas source areas, determine exploration targets, and effectively guide the exploration and development of oil and gas.  
The results of comparing the characteristics of the crude oil in the HZ-A structure double Paleogene field to the characteristics of the HZ26 Paleogene Wenchang Formation source rocks showed that the crude oil in the HZ-A structure double Paleogene field was the product of the same set of hydrocarbon-generating rocks, with the parental material being a land-aquatic hybrid organic matter in a weakly oxidizing environment. The crude oil characteristics of the HZ-A structure Paleogene Enping Formation were similar to those of the shallow-lake source rocks in the Paleogene Wenchang Formation in the HZ26 sub-sag, and the crude oil characteristics of the HZ-A structure Paleogene Wenchang Formation and the Paleo buried hills were similar to those of the deep-lake source rocks in the Paleogene Wenchang Formation in the HZ26 sub-sag. As a result, the crude oil in the HZ-A structure \"double paleo\" field mainly came from the source rocks of the Paleogene Wenchang Formation in the HZ26 sub-sag (Figure 8A).  
The results of comparing the characteristics of the natural gas of the HZ-A structure double Paleogene field to those of the source rocks of the Paleogene Wenchang Formation in the HZ26 sub-sag showed that the ethane carbon isotope values of the natural gas in the HZ-A structure double Paleogene field ranged from \(-30\%\) to \(-25\%\) and that the kerogen carbon isotope values of the Wenchang Formation of the Paleogene Wenchang Formation in the HZ26 sub-sag ranged from \(-29\%\) to \(-27\%\) (Figure 8B). Therefore, the natural gas in the HZ-A structure double Paleogene field mainly came from the hydrocarbon-generating rocks in the Paleogene Wenchang Formation in the HZ26 sub-sag.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 3 Proposed Timing Recovery Scheme
### Delay-Locked Loop Design  
The corrected Co-Band PRS signal is fed to the DLL and we perform phase compensation for the PRS symbol as follows:  
\[\mathrm{Y}[q]=\left(\mathrm{FFT}(y[q+\hat{\delta}+\mathrm{Int}\{\hat{\theta}\}]) -\frac{\hat{d}_{kJ}^{(p,\mu)}}{\gamma p_{L}}\right)\cdot\mathrm{e}^{-j2\pi q \frac{\mathrm{Pr}_{4}(\hat{\theta})+\mathrm{Pr}_{2}(\hat{\theta})}{N}} \tag{21}\]  
where \(\mathrm{Int}\{\hat{\theta}\}\) and \(\mathrm{Fra}\{\hat{\theta}\}\) denote the integer and fractional parts of the timing estimation, and \(\mathrm{Fra}\{\hat{\rho}\}\) denotes the output of the DLL. The composition of the DLL mainly includes correlator, discriminator, and loop filter. The local generated early and late reference pilots of the Co-Band PRS are generated by  
\[\mathrm{Y}_{e}[q+iQ]=\Phi_{i,q}e^{-j2\pi k\omega/N},\quad\mathrm{Y}_{l}[q+iQ]= \Phi_{i,q}e^{j2\pi k\omega/N},\quad q=0,\ldots,Q \tag{22}\]  
where \(0<\omega\leq 1/2\) is the normalized OFDM time shift; in this paper, \(\omega\) is set to \(1/2\). The early and late correlations in the frequency domain can be expressed, respectively, as  
\[R_{e}[i]=\sum_{q=0}^{Q-1}\mathrm{Y}[q+iQ]\mathrm{Y}_{e}^{*}[q+iQ],\quad R_{l}[ i]=\sum_{q=0}^{Q-1}\mathrm{Y}[q+iQ]\mathrm{Y}_{l}^{*}[q+iQ]. \tag{23}\]  
In order to improve performance, we process the above results with incoherent integrals to obtain  
\[R_{e}=\frac{1}{I}\sum_{i=0}^{I-1}R_{e}[i],\quad R_{l}=\frac{1}{I}\sum_{i=0}^{I -1}R_{l}[i]. \tag{24}\]  
After obtaining the noncoherent integrals of the early and late branches, the correlation values need to be discriminated in order to obtain accurate phase values. Common code loop discriminators include the noncoherent early-late amplitude method, the noncoherent early-late power method, and the seemingly coherent dot product power method [30; 31]. In this paper, the noncoherent early-late power method is used, and the normalized S-curve function is described as  
\[\hat{\sigma}=\frac{|R_{l}|^{2}-|R_{e}|^{2}}{|\mathrm{Y}|^{2}Q^{2}K_{d}} \tag{25}\]  
where \(|\mathrm{Y}|^{2}\) represents the square of the signal amplitude, and \(K_{d}\) is the gain coefficient that guarantees normalization. The DLL S-curve is shown in Figure 4. And the proposed iterative DLL algorithm is shown in Algorithm 1.  
Figure 4: Normalized S-curve function for the case of \(\omega=1/2\).  
```
1:\(\text{Y}[q],\text{Y}_{e}[q],\text{Y}_{l}[q]\) Output: \(\text{Fra}\{\hat{\rho}\}\)
2:initialize: Set \(\text{Fra}\{\hat{\rho}\}=0,iterator=0,\hat{\rho}=0\)
3:while\(iterator<5\)do
4: Update \(\text{Y}[q]\leftarrow\text{Y}[q]e^{-j2\pi q\text{Fra}\{\hat{\rho}\}/N}\)
5: Obtain \(R_{e}\) and \(R_{l}\) by invoking Equations (23) and (24), and obtain \(\text{Fra}\{\hat{\sigma}\}\) by invoking Equation (25)
6: Update \(\text{Fra}\{\hat{\rho}\}\leftarrow\text{Fra}\{\hat{\rho}\}-\text{Fra}\{\hat{ \sigma}\}\), and update \(iterator\Leftarrow iterator+1\)
7:if\(\text{Fra}\{\hat{\sigma}\}<0.001\)then
8: break
9:else
10: continue
```  
**Algorithm 1** Iterative DLL algorithm
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
## 2 Materials and Methods
### Imagery Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Vegetation Index**} & \multicolumn{2}{c}{**Equation**} & \multicolumn{1}{c}{**Authors**} \\ \hline Normalized Difference Vegetation Index & NDVI = (NIR \(-\) Red)/(NIR \(+\) Red) & [PERSON] et al. [37] \\ \hline Normalized Difference Red-Edge Index & NDRE = (NIR \(-\) Red-edge)/(NIR \(+\) Red-edge) & [PERSON] et al. [38] \\ \hline Green Normalized Difference Vegetation Index & GNDVI = (NIR \(-\) Green)/(NIR \(+\) Green) & [PERSON] et al. [39] \\ \hline Wide Dynamic Range Vegetation Index & WDRVI = (a \(\times\) NIR \(-\) Red)/(a \(\times\) NIR \(+\) Red) & [PERSON] [40] \\ \hline Red: reflectance of red region (630 nm-685 nm). red-edge: reflectance in the transition region (690 nm-730 nm). NIR: reflectance in the near-infrared region (760 nm-1500 nm). Green: reflectance of green region (542 nm-578 nm). a: weighting coefficient (0.1).  
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**DAC**} & \multirow{2}{*}{**Orbital Image Dates in 2018 (Month/Day)**} & \multirow{2}{*}{**Orbital Image Dates in 2019**} & \multirow{2}{*}{**Phenological Stage**} \\ \cline{1-1} \cline{5-5}  & & & **(Month/Day)** & \\ \hline I1 & 30 & NA & NA & Initial \\ I2 & 60 & NA & NA & Initial \\ \hline T1 & 90 & 02/04, 02/09, 02/24 & 02/09 & Tillering \\ T2 & 120 & 03/06, 03/11, 03/16, 03/21 & 03/06, 03/26, 03/31 & Tillering \\ T3 & 150 & 04/05, 04/20, 04/25, 04/30 & 04/20, 04/25 & Tillering \\ \hline D1 & 180 & 05/20, 05/30 & 05/05, 05/30 & Development \\ D2 & 210 & 06/19, 06/29 & 06/09, 06/14, 06/24, 06/29 & Development \\ D3 & 240 & 07/04, 07/09, 07/14, 07/19, 07/24, 07/29 & 07/09, 07/14, 07/24 & Development \\ \hline R1 & 270 & 08/13, 08/18, 08/23, 08/28 & 08/08, 08/18, 08/23, 08/28 & Ripening \\ R2 & 300 & 09/07, 09/22 & 09/07, 09/12, 09/17 & Ripening \\ R3 & 330 & 10/12, 10/22 & 10/02, 10/12, 10/17 & Ripening \\ \hline M & 360 & NA & NA & Maturation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dates of the orbital images and respective phenological stages of sugarcane.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
###### Abstract  
High-speed centrifugal pumps with low specific speed have the characteristics of a small flowrate, a high head, and being compact and light weight, making them promising candidates for applications in the thermal management of aerospace and electronic devices. The energy loss in the low specific speed pump is critical and complex due to the large impeller diameter, the narrow and long flow channel, and the small outlet width. In this paper, an analysis method based on an energy loss model and computational fluid dynamics simulations (ELM/CFD) is proposed to analyze the performance of the low specific speed pump with a fully sealed structure. Experiments were carried out under variable water flowrates. The results show that the empirical correlation method failed to accurately predict the performance of high-speed centrifugal pumps, because the bearing clearance leakage and motor channel leakage are ignored. Moreover, the volume loss and hydraulic loss are calculated based on the empirical parameters of commonly used pumps that are different from the high-speed pump with the low specific speed in the complex flow channel structure. The ELM/CFD method calculates various loss power based on the simulation results and can predict the head and efficiency with deviations less than 2% and 5%, respectively. ELM/CFD can accurately analyze the optimization direction of the pump. The hydraulic loss and the volume loss of the impeller are the dominant factors that restrict the pump efficiency under the lower flowrates, while the hydraulic loss of subsequent flow channels becomes important under the larger flowrates.  
centrifugal pump; low specific speed; energy loss model; CFD simulation +
Footnote †: journal: Applied sciences
## 4 Conclusions  
An original concept of a hybrid data transmission network was developed and tested for monitoring the operating parameters of individual solar batteries. A universal hybrid data transmission network concept with software for monitoring solar power operating parameters could not be found in the literature. During the design and installation of this network, various components were used for information collection and analysis, such as RF modules, RS 485 modules communicating with twisted pair cables, etc. Data storage took place using a central server with a MySQL database. The operation of the data network was ensured using PIC series microcontrollers with original programming code written in the C language. Using the GSM connection, basic working data can be transferred to mobile devices with the Android operating system. For this purpose, an original app for mobile devices was created. During testing of the created hybrid data transmission network, an information transmission speed measurement was performed, which showed that the network speed fluctuated within the range 15.5-16 Mbps. Meanwhile, measurements of the operating parameters of the batteries of the 870 W solar power plant were performed during network testing. As measurements show, the bifacial solar cell is about 15-20% more efficient than traditional silicon-based solar cells. Meanwhile, the volt-ampere characteristic curves of both the new batteries and the 10-year-old battery coincide, with the voltage variation range ranging from 0 to 32 V, and the current ranging from 0 to 9 A. This shows that the alloying degree of the Si active material is the same in all batteries. The measurements of the ambient temperature in which the solar power plant was operating showed that the temperature varied 6 from to 30. When testing the reliability of the hybrid network, the data monitoring process took place for 7 days without a break, during which no malfunctions were observed.  
Methodology, I.S. and G.B.; Software, I.S.; Formal analysis, G.G.; Investigation, Z.K.; Data curation, V.C. 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.
## 2 Theoretical Calculation Method  
For the total energy calculations, we employed the DFT using the projector augmented wavelet (PAW) method [33; 34], implemented in VASP software [35], version 6.2.1. The evaluation of the exchange and correlation energy was performed using the generalized gradient approximation (GGA) with Perdew-Burke-Ernzerhof (PBE) functions [36]. A value of U\({}_{\text{Cr}}\) = 3.7 eV was selected in the calculation because this value provides the highest energy stability for the material in its magnetic state. Additionally, with this value, the material maintains its semiconducting behavior. This correction in the Hubbard potential ensures that the bandgap value is closely aligned with those reported for similar materials, such as CaTiO\({}_{3}\)[7; 23]. A cutoff energy of 520 eV was determined for the PAW-PBE potentials, which allowed high convergence in the total energies to be achieved, with discrepancies of less than 0.001 eV per atom. Brillouin zone sampling was performed using the Monkhorst-Pack method, with a k-point grid of 7 \(\times\) 7 \(\times\) 5 [37], which was sufficient to obtain highly convergent energies, with variations of less than 1 meV per atom. For the calculation of the partial occupancies of the electronic states near the Fermi level, we applied the blurring technique proposed by [PERSON] and [PERSON] [38], with a blurring parameter of 0.05 eV. Together, these parameters ensured that our calculationsachieved convergence in the total energy within 1 meV. In addition, we optimized the lattice parameters and ionic positions until the forces acting on the ions were minimized to less than 30 meV/A.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 3 Different Channel Models
### RTS-Based Model  
The Euclidean distance-based model and the two graph-based models can compute the average channel gain in seconds. However, those models cannot accurately capture information about the environment. The average channel gain computed via those models is an approximation of the real one. Given that an RTS is able to accurately capture environment information [34], this section introduces an RTS-based approach. The RTS is provided by Huawei Technologies (Gothenburg, Sweden). We use the approach to evaluate the solutions generated by the other, less complex, channel models. The RTS considers a set of evenly spaced sub-carrier frequencies within a given bandwidth. The average channel gain for the RTS-based model is computed as  
\[\widetilde{\beta}_{il}=\frac{\sum_{f\in\mathcal{F}}\sum_{m=1}^{M}|h_{ilmf}|^{2 }}{|\mathcal{F}|}, \tag{15}\]  
where \(h_{ilmf}\) is the channel response between user location \(i\) and antenna \(m\) of the array of the AP at location \(l\) when frequency \(f\) is used. Set \(\mathcal{F}\) includes all the sub-carrier frequencies within the considered bandwidth. As mentioned in Section 1, the RTS-based model is computationally intensive. In Section 4, two methods are applied to simplify the computation.
###### Abstract  
The space-air-ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic-drone matching and LEO-drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.  
Keywords:space-air-ground integrated network; high altitude platform drone; LEO satellite; matching problem; reinforcement learning +
Footnote †: journal:  
Article  
Joint Drone Access and LEO Satellite Backhaul for a Space-Air-Ground Integrated Network: A Multi-Agent Deep Reinforcement Learning-Based Approach. _Drones_**2024**, \(8\), 218. [[https://doi.org/10.3300/drones8060218](https://doi.org/10.3300/drones8060218)]([https://doi.org/10.3300/drones8060218](https://doi.org/10.3300/drones8060218))
# Frequency Response Extension Method of MET Vector Hydrophone Based on Dynamic Feedback Network
## 4 Results  
### Experimental Setup and Open-Loop Test  
Figure 8 shows the experimental environment for hydrophone sensitivity testing. The hydrophone test device is shown in the figure. The core of the device comprises a calibration tube (the entire container), which contains the transmitting transducer, the tested MET hydrophone, and the reference hydrophone. The function signal generator (Agilent 33210A, Keysight Technologies, Santa Rosa, CA, USA) generates a sweep sine signal in the specified frequency range, which is amplified by the power amplifier (SL1000 VCF, MB Dynamics Inc., Cleveland, OH, USA), and then drives the transmitting transducer. The transmitting transducer generates the specified water pressure signal in the calibration tube. The MET hydrophone is fixed on a programmable revolver for directional testing. The MET hydrophone and the reference hydrophone receive the water pressure signal. Their output signals are recorded and analyzed by the oscilloscope (TDS2024C, Tektronix, Beaverton, OR, USA) to obtain the response characteristics of the tested MET hydrophone at different frequencies. Finally, the frequency response curve for sensitivity and performance evaluation is drawn.  
During the open-loop test, we obtained the open-loop frequency response curve of the hydrophone, as shown in Figure 9. It can be seen that in the frequency range of 2 Hz to 100 Hz, the sensitivity is greater than \(-170\) dB ref  
Figure 8: Hydrophone sensitivity test environment.  
To verify the directivity of the hydrophone, we conducted directivity tests on the hydrophone. The test results are shown in Figure 10. It can be seen that the directivity of the hydrophone has vector characteristics, with higher sensitivity in the 0\({}^{\circ}\) and 180\({}^{\circ}\) directions and lower sensitivity in the 90\({}^{\circ}\) and 270\({}^{\circ}\) directions.  
Figure 10: Hydrophone directivity test.  
Figure 9: Hydrophone closed-loop frequency response.
# Effect of Aging and Moisture Damage on Fatigue Cracking Properties in Asphalt Mixtures
## 3 Experimental Program
### Fatigue Cracking Resistance Test  
To evaluate the resistance to fatigue cracking, a cyclic fatigue test was performed using two experimental methods: load control and displacement control. In this study, a controlled crosshead cyclic loading (CX) test was conducted. This allows the simulation of the load conditions that occur during actual use by applying a mixed type of load (neither stress nor strain control) on the specimen. The controlled CX test was performed by loading at 20 \({}^{\circ}\)C at a rate of 10 Hz. The fracture time was determined as the time point of the phase angle exhibiting a rapid change using the method proposed by [PERSON] (1997) [23]. Figure 7 shows the changes in the dynamic modulus and phase angle according to the number of loading cycles. In general, as the number of loading cycles increases, the dynamic modulus gradually decreases, and the phase angle increases. As shown in Figure 7, the phase angle  
Figure 6: Dynamic modulus test.  
decreases rapidly after a load is applied for a certain period of time, which indicates that the specimen is broken.  
In the fatigue test performed previously, the dynamic modulus itself does not directly reflect the fatigue performance owing to aging and moisture damage. In other words, the fatigue performance should be evaluated considering the resistance to deformation, resistance to damage, and fracture properties. The fatigue test reflects the fatigue properties of the material; however, fatigue life or cracking in the asphalt pavement should be predicted based on the traffic load and environmental conditions. Therefore, to use the fatigue performance prediction equation presented by the Mechanistic-Empirical Pavement Design Guide in the United States, this study intends to obtain the coefficient values for each condition (for each stage of aging and before/after moisture damage) required for the fatigue performance prediction equation based on the results of the indoor experiment. The allowable fatigue cracking cycles under each condition were determined using the coefficient values obtained in this study.  
\[N_{f}\ =\ \text{C}k_{1}\bigg{(}\frac{1}{\varepsilon_{t}}\bigg{)}^{k_{2}} \bigg{(}\frac{1}{E}\bigg{)}^{k_{3}} \tag{4}\]  
where \(N_{f}\) is the number of repetitions to fatigue cracking; \(\varepsilon_{t}\) is the tensile strain at the critical location; \(E\) denotes the stiffness of the material; \(k_{1}\), \(k_{2}\), and \(k_{3}\) are the laboratory regression coefficients; and \(C\) is the laboratory-to-field adjustment factor.
## 4 Discussion
The measurements described in this paper cannot be recorded with a simple dynamometer [46], which does not calculate torque nor velocities. To mention methodological limitations, the reader should take necessary caution when interpreting our results because the relatively small sample size of this study could have resulted in bias we did not investigate eccentric contractions due to the risks involved when muscles are extended. To avoid iatrogenic injuries, a longer familiarization stage would have been better, which we will include in further research and clinical application. As for practical applications, isokinetic assessment is the most accurate method for the evaluation of muscular activity [47]. The measurement is made with a computerized system that allows for movement at a constant previously determined angular velocity. This expensive piece of equipment, called an isokinetic device, is still the gold standard for quantifying torque and power during isokinetic control. DINABANG, with its EB, could be a handy complement for field work as its price is two orders of magnitude less and it can be conveniently carried around as a kind of \"physiotherapist's stethoscope\", much better than a simple field dynamometer. Future work will address the exact modes of neurological and musculoskeletal adaptations of training using specific motor tasks and EB, as this sequence has not been completely described [45]. Dumbbells use gravity to create isotonic resistance and, thus, are limited regarding the diversity of exercises to which they can be applied, allowing for only one per machine. By contrast, EB do not rely on gravity but, rather, on how much the band is stretched and, therefore, they offer great versatility and several exercise options. Elastic bands together with DINABANG provide an interesting spectrum of training options, including its use in terms of wellness, for all individuals, whether they are old adults, middle-aged, or young adolescent. Once the numerical characterization of torque, velocity and power deployed by healthy individuals with EB is available, the novel DINABANG device will include them as standards to be used with patients and athletes in training.