esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Three Gorges Reservoir area is one of the most ecologically sensitive areas in China, and the forest landscape pattern in this region shows dramatic change due to the influence of the Three Gorges reservoir project. In this study, the locally parameterized Biome-BGC model, generated with long-term meteorological monitoring data, was used to simulate net primary productivity (NPP) and carbon density of the vegetation layer, the litter layer, and the soil layer for various forest types from 1992 to 2012 in this area. The total and unitary forest NPP presented obvious annual fluctuation under the combined influences of land use change and extreme weather events. Apart from the year 2006, from 1992 to 2012, the NPP values of each forest type showed an increasing trend, although the growth rates decreased. In 2006, due to abnormally high air temperatures and less precipitation, total and unit area forest NPP values decreased by 46.3% and 53.9%, respectively, compared to 2002. From 1992 to 2012, the carbon stocks of the forest vegetation layer, the litter layer, the soil layer, and the entire area gradually increased with decreasing growth rates. Additionally, forest carbon stocks were high in the east and the south and low in the west and the north. Generally, the forest productivity is greatly affected by the physiological and ecological characteristics of the plants themselves as well as the environmental factors, whereas total forest productivity is largely influenced by human activities. The increase in forest area and the optimization of the forest landscape pattern could improve the forest productivity and carbon sequestration.  
forest NPP; forest carbon sequestration; Biome-BGC model; Three Gorges Reservoir area figure
# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Applications
#### 4.2.3 Antarctic Tropospheric Circulation  
Antarctic is one of most windy regions in the world and yet the observations there are relatively sparse. To illustrate the mean atmospheric circulation over the Antarctic, we processed 5 days of the LEO-LEO stereo winds from 7 to 11 July 2021. The zonal mean distributions of the 5 day zonal (U) and meridional (V) winds as well as Cloud occurrence Frequency by Altitude (CFbA) are shown in Figure 15. The general circulation in the southern hemisphere polar region is evident in the zonal mean V wind and CFbA. The equatorward and poleward flows are seen in the lower (<3 km) and upper (>5 km) troposphere at latitudes between 40\({}^{\circ}\)S and 70\({}^{\circ}\)S. This mean meridional circulation results in a descending flow in the Antarctic interior due to mass conservation, which manifests itself as the low CFbA seen in the LEO-LEO stereo observations at latitudes of 75\({}^{\circ}\)S poleward. This circulation is also called a polar cell. In addition, CFbA shows more frequent occurrences in the PBL that often tops at ~2 km above the surface at oceanic latitudes between 40\({}^{\circ}\)S and 65\({}^{\circ}\)S. The cloud-defined PBL top appears to drop sharply at latitudes 65\({}^{\circ}\)S-85\({}^{\circ}\)S over the Antarctic, where the cold surface and the air descent keep cloud layers low.  
Figure 15: Vertical and latitudinal distributions of zonal mean (**a**) cloud occurrence frequency by altitude (CFbA) in %/km, (**b**) zonal wind (U), and (**c**) meridional wind (V) in m/s from 7 to 11 July 2021. The vertical coordinate is the stereo height above terrain, which is simultaneously derived with AMVs from cloud features in the IR band.  
The polar descending air is the fundamental driving force of downslope katabatic winds near the surface in the Antarctic. Around the coastal regions, the Antarctic winds are subject to large variations influenced by the katabatic flow and by synoptic perturbations such as cyclones in the Southern Ocean. Figure 16 shows maps of low-level (500-2000 m) winds and their variability from the five days when a cyclone swept through the Southern Ocean. Effects of the cyclone are clearly seen in the wind-speed map that has a low speed near the cyclone center and high values surrounding it. The cyclone generated an average poleward flow near the Peninsula and an equatorward flow near the Ross Sea. The cyclone created strong wind disturbances over Antarctica, as indicated by the enhanced standard deviations in wind speed.
## 3 Materials and Methods  
### Chemicals and Standards  
Methanol (MeOH, 99.9%), acetonitrile (ACN, 99.9%), formic acid (99%), acetic acid (99.5%) and dichloromethane (99.9%) were purchased from Carlo Erba (Barcelona, Spain). Sodium hydroxide (NaOH, 98.0%) was obtained from AnalaR BDH Chemicals (Leicestershire, UK). Phosphoric acid (85.0%) and disodium phosphate (99%) were purchased from Panreac (Barcelona, Spain). Diphenylamine (DIF, 98.0%) used as internal standard (IS) was purchased from Sigma-Aldrich (Saint Louis, MO, USA). Hydrochloric acid (HCl, 37%) was purchased from Sigma-Aldrich (Vienna, Austria). Ultra-pure water was obtained from Milli-Q water purification systems from Merck Millipore (Burlington, MA, USA). Six pharmaceutical tablets commercially available in the Portuguese market were used to prepare the standard solutions: ADT produced by Generis (Amadora, Portugal) containing 10 mg of amitriptyline hydrochloride (AMT); Tolvon produced by Merck Sharp & Dohme (Paco de Arcos, Portugal) containing 30 mg of mismerin hydrochloride (MIA); Surmontil produced by Laboratorios Vitoria (Amadora, Portugal) containing 35 mg of trimipramine (TRI); Tofranil produced by Amdipharm (Dublin, Ireland) containing 10 mg of imipramine hydrochloride (IMP); Mirtazapina Alter produced by Alter (Alter do Chao, Portugal) containing 15 mg of mittazapine (MIR); Protiadene produced by Teofarma (Pavia, Italy) containing 75 mg of dothiepin hydrochloride (DOT). Stock solution were prepared according to previously described protocols [56; 57], where each tablet was individually meshed and dissolved in 10 mL of MeOH giving different concentrations according to the pill initial dosage. Then, the solutions were sonicated (42 \(\pm\) 2.5 kHz, 100 W, Branson 3510, Switzerland) for 15 min, centrifuged at 3000 rpm for 10 min. Diphenylamine stock solutions were prepared by dissolving in MeOH to give a final concentration of 1000 mg L\({}^{-1}\), stored at \(-\)20 \({}^{\circ}\)C in glass flasks and renewed every month.
## 3 Analysis of Torque
### The Effect of the Gap Shape on the Magnetic Circuit
To clarify the magnetic induction density corresponding to different angles, the surface at an MRF thickness of \(h/2\) was taken as the neutral surface. Figure 6: Schematic diagram of the magnetic boundary conditions. (**a**) Magnetic boundary conditions; (**b**) distribution of magnetic field lines in the semi-circular MRF gap. Figure 7: Magnetic flux density at the MRF gap (\(r_{\text{a}}=1\) mm, \(h=1\) mm, \(NI=300\) A). (**a**) Magnetic flux density at the semi-circular gap; (**b**) magnetic flux density at the neutral surface. The magnetic induction density at the neutral surface was evaluated and the results are shown in Figure 7b. When comparing the magnetic flux densities in Figure 7b, one can observe that the maximum value in the semi-circular gap is 0.542 Tesla, whereas the maximum value in the planar gap is 0.5 Tesla. Compared with the planar gap, the maximum magnetic flux density in the semi-circular gap increased by 8.4%. However, over all angles, the average \(B\) for the semi-circular gap is smaller than the same value for the planar gap. When the current rises, the MRF in the semi-circular gap is more likely to become magnetically saturated in local areas. If the degree of unevenness in the magnetic field distribution is too high in the entire gap, it will be detrimental to torque control. The equivalent length \(l_{\mathrm{g}}\) of the unilateral semi-circular gap increases compared with the planar gap as follows:  
Figure 3:0  
where \(n_{\mathrm{m}}\) is the total number of arcs. Combined with the equivalent length \(l_{\mathrm{g}}\), at \(r_{\mathrm{a}}=1\) mm, the equivalent working length of a single semi-circular gap is \((\pi-2)\) mm longer than that of a planar gap with the same radial distance (about a 57.1% increase). Although the average \(B\) of the semi-circular gaps is smaller, the equivalent working length increases more. Altogether, the semi-circular gap can provide more torque. The effect of the arc radius \(r_{\mathrm{a}}\) on the magnetic field is explored below, and the magnetic flux density at the neutral surface is shown in Figure 3:1  
Along with the increase in the arc radius, the inhomogeneity of the magnetic flux distribution increases as the magnetic flux density \(B\) decreases at the transition between the two arcs. In addition, the magnetic flux density within the working gap of a single arc is symmetrically distributed along the geometric axis. In accordance with the data presented in Figure 8, it can be found that the flux distribution within the working gap is more uniform when \(r_{\mathrm{a}}=0.5\) mm. As the chain formation process of magnetic particles is influenced by the magnetic flux density's magnitude, the MRF in the gap will not be sufficient when the inhomogeneity of the magnetic field distribution increases [19]. The shear yield stress at the circular gap is low, which leads to a decrease in the transmission device's ability to transfer torque. Therefore, \(r_{\mathrm{a}}=0.5\) mm was chosen as the optimal size.
## 5 Conclusions  
In this study, a two-stage strain gauge location optimization method is proposed based on a minimum correlation criterion. In this method, an improved correlation coefficient method is defined to evaluate the correlation degree between multiple monitoring points. Moreover, the operational load prediction accuracy of the proposed method, LASSO regression, and principal component regression is compared to validate the advantages of the proposed method. Some other major conclusions are summarized as follows:  
1. The strain gauge location optimization method based on the proposed improved correlation method gives different combination sequences of monitoring points and the optimal strain monitoring location sequence can be readily determined.
2. The proposed correlation method is reliable and robust in optimizing the strain gauge locations and providing accurate load prediction results.
3. LASSO regression can select strain monitoring points by constructing a penalty function, but its performance depends on the monitored loads and can only deal with a single load variable condition each time.
4. Principal component regression can yield two principal components to make its contribution rate reach 95%, with their load prediction errors being less than 5%, but it belongs to the feature extraction method, requiring the information of all monitoring points.
5. For practical application of the proposed method, the optimal strain gauge locations are determined via finite element analysis of aircraft structures. Furthermore, the optimized strain gauge locations should be validated by a ground test before implementation in practical applications. In addition, backup strain gauge locations should be considered for practical application.  
Conceptualization, H.P. and M.L.; methodology, H.P.; validation, Y.N., S.C. and B.W.; formal analysis, S.C.; investigation, M.L.; resources, B.W.; data curation, S.C.; writing--original draft preparation, H.P.; writing--review and editing, S.C.; visualization, Y.N.; supervision, B.W.; project administration, M.L.; funding acquisition, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 12102346.  
Not applicable.  
The data will be made available upon request.  
The authors declare no conflicts of interest.
## 4 Experimental Results and Analysis
### Comparison with State-of-the-Art (SOTA) Methods
\begin{tabular}{c} **VDSR** \\ **[49]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **[16]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **[17]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **[18]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **[22]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **[25]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **[41]** \\ **(dB)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Ours** \\ **(dB)** \\ \end{tabular}  } \\ \hline
0.02 & 80.5968 & 85.7465 & 87.4305 & 88.0954 & 90.4366 & 91.1316 & 91.0545 & 91.7665 & 91.6  
Figure 7: _Cont._  
Figure 8: Comparison of denoising results. Original forward model (**a**) and noisy forward model (**b**) and denoised results (**c**–**e**) processed by IDN, USRNET, and our method, respectively. Figure 7: Comparison of denoising results. Original (**a**) and noisy (**b**) synthetic data and denoised results processed by shearlet algorithm (**c**), USRNET (**e**), and our method (**g**), respectively. The corresponding eliminated noises are plotted in (**d**,**f**,**h**), respectively. Figure 9: Comparison of results processed by commercial software and our method for original real seismic data with a low SNR.
## 4 Discussion and Application
### Sensitivity Analysis
As shown in Figure 10, there are forty-three missed detections, of which twenty-five are in the SNR range of (\(-5\), \(0\)], eight are in the range of (\(0\), \(5\)], five are in the range of (\(5\), \(10\)], four are in the range of (\(10\), \(15\)], and one in the range of (\(15\), \(20\)], respectively. The missed detections contribute to low overall error rates of \(2.5\%\), \(0.8\%\), \(0.5\%\), \(0.4\%\), and \(0.1\%\), respectively. The error rates are mainly concentrated in the low SNR ranges. With an increase in the SNR, the number of missed detections and error rates decrease. The proposed approach can better detect the micro-fracture waveforms with SNRs greater than \(5\) dB, even when the micro-fracture waveforms are interrupted by untrained Gaussian noise. In summary, the PDNetwork has a low sensitivity to noise. Figure 9: \(Cont\).
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation  
In this section, we propose a novel rate adaptation algorithm derived from ALINEA [39], which is a classical traffic control algorithm. We redesign the algorithm and redefine the  
Figure 3: The aggregation of MCC messages.  
meaning of the algorithm parameters in SRMCC. As shown in Equation (3), \(R(t+1)_{cal}\) and \(R(t)\) mean the calculation rate at time interval \(t+1\) and the sending rate at time interval \(t\), respectively:  
\[R(t+1)_{cal}=\ R(t)\times(1+w\times(O_{th}-O(t)) \tag{3}\]  
where, \(O(t)\) is the current queue occupancy ratio, \(w\) is the weight factor, and \(O_{th}\) is the queue occupancy ratio threshold. The values of \(w\) and \(O_{th}\) are adaptable. At different stages, they are assigned different values to control the fluctuation of variations in the calculation rate. Specifically, the definition of \(O_{th}\) is provided in Section 4.1. Moreover, the value of \(O_{th}\) and the calculation method of \(w\) in different congestion stages are proposed in Section 4.2. Then, the minimum rate \(R(t+1)_{min}\) and the maximum rate \(R(t+1)_{max}\) for next time interval are determined in Equation (4).  
\[R(t+1)=min\Big{(}R(t+1)_{cal},R_{fair-shared}\Big{)} \tag{4}\]  
Next, we define the congestion state value, and then give the rate calculation method corresponding to each congestion state value. Then, we give the rate selection strategy. The overall algorithm pseudo code is shown in Algorithm 1.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results
### Effect of Power-Sharing Parameters, \(w_{i},\alpha_{i}\), on PER, Throughput, and Delay
Figure 12: PER and throughput performance comparison between D-NOMA-HARQ setting \(w_{1}=0.6\), and S-NOMA-HARQ setting \(w_{1}=0.51\), 0.6 (\(w_{2}=1-w_{1}\)) with CC-HARQ when \(m=1\), \(\alpha_{1}=0.5\), \(\alpha_{2}=0.4\), \(n=100\), \(k=50\).
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 3 Materials and Methods
Kubernetes: (i) Cloud-native applications--Kubernetes is often used as a foundation for building cloud-native applications. It provides the infrastructure and capabilities necessary to develop and run containerized applications in cloud environments, thus promoting portability, scalability, and resilience. (ii) Big Data and Analytics--Kubernetes is used in big data and analytics environments to organize and manage distributed data processing frameworks such as Apache Spark and Hadoop. It enables the deployment of containerized data processing tasks and simplifies the management of complex data feeds. (iii) Machine learning and AI--Kubernetes is used in machine learning and AI workflows to manage the deployment and scaling of training and inference workloads. It enables organizations to efficiently run containerized machine learning models, perform distributed training, and manage resource-intensive AI workloads. Proxmox: (i) development and test environments--Proxmox can be used in development and test scenarios to create isolated virtual environments for software development, debugging, and testing. It allows developers to easily replicate different settings and configurations, enabling efficient software development and testing workflows. (ii) Web hosting and service providers--Proxmox is used by web hosting companies and service providers to offer hosting and cloud-based virtual private server (VPS) services to their customers. It provides the infrastructure and management capabilities necessary to host multiple virtual environments and provide scalable and reliable services to clients. (iii) Media streaming and content delivery--Proxmox can be used in media streaming and content delivery applications. It enables the creation of virtual environments for media servers, content caching, and load balancing, facilitating the efficient distribution of multimedia content over networks.
## 3 Results  
### Spectra Characteristics of Paddy Fields  
Based on rice phenology, the response of the Sentinel-1 and 2 spectra to three key phenological periods (Figure 3) can be described as follows:  
1. The tillage and planting (T) period, where rice fields are flooded. According to the VH polarization data in Figure 3, the rice fields had a local minimum VH polarization value about \(<\)\(-\)24.5 (i.e., close to the value of a water body). This value depends on water levels or soil moisture during soil tillage. Higher water levels lead to more negative VH values [22]. On the other hand, the NDVI value during this period may not be  
Figure 4: Accuracy assessment by using a street view image from Google Earth in Sungai Besar, Selangor state (3°42′43.92''N, 101°03°10.93''E) in July 2019.  
at the required minimum due to cloud cover. Thus, Sentinel-1 data (VH polarization value) are more sensitive to detect the transplanting period than Sentinel-2 data.
* The vegetative (V) and reproductive (R) period, where rice grows rapidly with canopy closure. Consequently, increasing chlorophyll signals are accompanied by a significant decrease in soil signals [29]. NDVI values rapidly increase during this period, peaking at the end of the generative period, when the crop enters the heading or maturity stage (NDVI value was about 0.7). On the other hand, VH polarization also increases in this period and continues to rise until the end of phenology.
* The maturity (M) period. Characterized by a rapid decrease in chlorophyll content as the carotenoid content increases [29]. This distinctive drop in NDVI values indicates the maturity of the rice plant. This period occurs in the last month of the rice growth cycle.  
Sentinel-1 data (VH polarization value) were more sensitive to detecting the transplanting period; on the other hand, Sentinel-2 data (NDVI value) were more detailed in showing the stages of growth and maturity. The difference between the local minimum and the peak was about four months, representing one growing season. The VH polarization and NDVI values decreased during the fallow period.  
Figure 3 shows an example of a rice field cluster in Selangor that had a double cropping season in a year, where the first (October-January) and second (April-July) planting seasons were referred to as the main and off planting season, respectively. The time series profiles of VH polarization and NDVI can be designated with the four periods: T: tillage and planting (30 days); V: vegetative (30 days); R: reproductive (30 days); and M: maturity (30 days). Thus, one season of rice cropping was about 120 days or 4 months.
# Method to Remove Tilt-to-Length Coupling Caused by Interference of Flat-Top Beam and Gaussian Beam
## 3 Theoretical Analysis
### Non-Diffractive Form
When we set the beam waist on the detector, e.g., \(z_{0}=s\), the wavefronts of the two beams on the detector are both planar. The pathlength signal is then caused by the geometric path only and not by the wavefront mismatch; therefore, \(LPS_{LPF}\) is equal to \(LPS_{OPD}\). Furthermore, Equation (11) illustrates an interesting phenomenon in that when the waist position of the Gaussian beam coincides with the pivot point of the flat-top beam, e.g., when \(z_{0}=0\), the pathlength generated by the wavefront mismatch then has the same magnitude as \(LPS_{OPD}\) but a different sign. The resulting \(LPS_{LPF}\) then becomes negligible. However, Equation (10) indicates that the extra TTL coupling will be generated if the detector size is not large enough or its gap size is not equal to 0. Next we use the numerical simulation tool to verify and increase confidence in the accuracy of the theoretical analysis above. In the simulation, we set the position of detector \(s=500\) mm, the Gaussian waist \(\omega_{0}=0.5\) mm and its waist position \(z_{0}=0\). The radius dimension of flat-top beam \(R=9\) mm, the diameter of detector \(D=10\) mm, while the gap size of detector \(L\) is set to be 0 and 20 \(\mu\)m respectively to enable observation of the change in \(LPS_{LPF}\) with respect to the angle \(\alpha\). The results are illustrated in Figure 2, which shows that the simulation results are in good agreement with the results derived from the analytical solution. Therefore, the TTL coupling in an interferometer with one Gaussian beam and one large flat-top beam and a large single-element photo-diode (SEPD) (i.e., a QPD with gap size equal to 0) can be made to vanish by setting appropriate optical parameters.
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 2 Femtosecond Laser-Induced Self-Organized Periodic Surface Structures (LIPSS)
### Microgrooves  
Although LISR formation occurs for low excited electron densities, laser-induced surface grooves (LISG) appear for large ones. The increased temperature due to the relaxation process leads to a thermal gradient and the formation of convection rolls in the molten material because of the Marangoni effect, which eventually leads to sub-wavelength microgrooves [35]. As confirmed by both experimental studies and simulations, the LISG periodicity is an increasing function of the number of pulses [35]. LISG show higher superhydrophobicity compared with LISR and, therefore, are expected to be good candidates for oil-water separation [42].
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 6 Instrument Noises and Corrections
### Tilt-to-Length Coupling
Based on these results, we can conclude that the tilt-to-length effect does not limit the KBR-LRI residuals.