esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 2 Materials and Methods
### Data and Data Processing  
#### 2.2.1 Remote Sensing Data  
_GLASS LAI/FPAR Products:_ The GLASS LAI/FPAR products, with a temporal resolution of eight days and a spatial resolution of 1 km, were generated and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University [28]. They are also available from the Global Land Cover Facility [29]. The GLASS LAI product was estimated from time-series MODIS and AVHRR reflectance data using GRNNs, which were trained for each biome type by fusing MODIS and CYCLOPES LAI. The GLASS LAI product is believed to be more temporally continuous and spatially complete compared to the MODIS LAI product [11]. The GLASS FPAR product was generated from the GLASS LAI product; thus, it shows the same properties as the GLASS LAI product. Validation against the VAlidation of Land European Remote sensing Instrument (VALERI) ground-based estimates has shown that both GLASS LAI and FPAR products had high accuracy (RMSE = 0.7848 and R\({}^{2}\) = 0.8095 for the LAI product, RMSE = 0.1276 and R\({}^{2}\) = 0.8048 for the FPAR product) [10; 13].  
_MODIS land cover product:_ The MODIS Land Cover Dynamics product (MCD12Q2) provided estimates of yearly vegetation phenology on a global scale [30]. The product identified 17 land cover classes defined by the International Geosphere Biosphere Program (IGBP) with a spatial resolution of 1 km, which includes 11 natural vegetation classes, three developed and mosaicked land classes, and three non-vegetated land classes [31].  
_TRAGL fractional vegetation cover (FCover) product:_ The TRAGL FCover product, with a temporal resolution of eight days and a spatial resolution of 1 km, was derived from the GLASS LAI product based on the method proposed by [PERSON] et al. [32]. The product is believed to be more spatially and temporally complete with continuous trajectories compared with the GEOV1 product. Direct validation  
Figure 1: Flowchart of gross primary production (GPP) and net primary production (NPP) estimation and validation. FPAR: Fraction of Photosynthetically Active Radiation; LAI: Leaf Area Index; LUE: Light Use Efficiency; PAR: Photosynthetically Active Radiation; APAR: Absorbed Photosynthetically Active Radiation; DEM: Digital Elevation Model.  
with ground-based FCover estimates demonstrated that the TRAGL FCover product was generated with high accuracy (RMSE = 0.0865 and R\({}^{2}\) = 0.8848) [26].  
_Global Biomass Map:_ A global biomass map with a spatial resolution of 1 km was generated by the Carbon Dioxide Information Analysis Center (CDIAC) [33] using International Panel on Climate Change (IPCC) methods [34; 35]. The global biomass carbon stocks were firstly estimated using the globally consistent default values provided for aboveground biomass [35], then belowground biomass (root) was added using the IPCC root to shoot ratios for each vegetation type.
## 3 Results
### Promis-57  
A higher PROMIS T-score indicates more of the concept being present, which can represent either an improvement or a worsening condition, depending on the concept. For example, a higher Pain Interference T-score indicates a negative change, while a higher Physical Function T-score indicates a positive change [21]. AMI patients demonstrated an improvement in PROMIS Pain Interference scores, which decreased from 65.9 \(\pm\) 1.5 (SE) to 50.1 \(\pm\) 1.7 (SE) (mean difference \(-\)15.7 [95% CI \(-\)19.9 to \(-\)11.5]; \(p\) < 0.0001). PROMIS Ability to Participate in Social Roles/Activities scores similarly improved with a mean score increase from 37.7 \(\pm\) 1.6 (SE) to 52.5 \(\pm\) 1.8 (SE) (mean difference 14.82 [95% CI 11.1 to 18.6]; \(p\) < 0.0001). PROMIS Physical Function scores improved with an increase from 33.8 \(\pm\) 1.6 to 46.6 \(\pm\) 1.7 (mean difference 12.8 [95% CI 9.3 to 16.2]; \(p\) < 0.0001). There was a significant improvement in the PROMIS Fatigue score with a decrease from 57.0 \(\pm\) 1.8 (SE) to 44.4 \(\pm\) 2.0 (SE) (mean difference \(-\)12.6 [95% CI \(-\)16.3 to \(-\)8.8]; \(p\) < 0.0001). PROMIS Anxiety/Fear scores improved and demonstrated a decrease from 54.3 \(\pm\) 1.8 (SE) to 46.3 \(\pm\) 2.0 (SE) (mean difference \(-\)8.0 [95% CI \(-\)11.9 to \(-\)4.2]; \(p\) < 0.0001). PROMIS Sleep Disturbance scores also improved with a decrease from 57.1 \(\pm\) 1.6 (SE) to 48.2 \(\pm\) 1.7 (SE) (mean difference \(-\)8.9 [95% CI \(-\)12.8 to \(-\)4.9]; \(p\) < 0.0001). Lastly, the PROMIS Depression/Sadness domain demonstrated less significance than the other PROMIS domains with a decrease from 49.9 \(\pm\) 1.5 (SE) to 46.1 \(\pm\) 1.6 (SE) (mean difference \(-\)3.8 [95% CI \(-\)7.0 to \(-\)0.7]; \(p\) = 0.0181) (Figures 4 and 5).  
Figure 3: Mean LEFS scores at 0 to 48 months post-operation for patients undergoing a modified AMI amputation including standard error bars.
## 2 Failure Criteria for Components at High Temperatures
### Failure Criteria of Materials  
Under high-temperature conditions, the mechanical properties of steel can change significantly, directly affecting the load-bearing capacity of components. In the case of transmission towers, the components are generally slender members, and the risk of  
Figure 1: Reduction factor at high temperatures.  
buckling failure must be taken into account. Under high-temperature conditions, the risk of buckling increases for compression members, while tension members primarily need to consider strength issues. According to the \"Code for Design of Steel Structures\" (GB50017-2017) [38], the tensile yield strength and the compressive buckling stress of components at high temperatures are calculated using Equations (3) and (4), respectively.  
\[f_{CT}=f\eta_{T} \tag{3}\]  
\[f_{T}=f\eta_{T}\varphi \tag{4}\]  
In the formula, \(f_{cT}\) is the tensile yield strength of the steel; \(f_{T}\) is the compressive buckling stress of the steel; \(f\) is the design strength of the steel; \(\varphi\) is the stability coefficient for axially loaded compression members; \(\eta_{T}\) is the reduction factor for the yield strength of steel at high temperatures.  
Steel is modeled using an ideal elastic-plastic constitutive model and its stress-strain relationship at high temperatures is shown in Figure 2. The stress-strain relationship and failure criteria are represented by Equation (5). At high temperatures, when the strain of a compression member reaches the compressive buckling strain, the member fails and loses its load-bearing capacity. For tension members, failure is considered to occur when the ultimate strain (0.02) is reached. This material failure criterion provides a basis for assessing the failure of components in subsequent finite element analyses.  
\[\sigma_{T}=\left\{\begin{array}{ll}E_{sT}&\epsilon_{T}-\epsilon_{tT}\leq \epsilon_{T}\leq\epsilon_{cT}\\ f_{cT}&\epsilon_{cT}<\epsilon_{T}<\epsilon_{u}\\ 0&\epsilon_{T}\leq-\epsilon_{tT}\;or\;\epsilon_{T}\geq\epsilon_{u}\\ \end{array}\right. \tag{5}\]  
\[\epsilon_{cT}=\frac{f_{cT}}{E_{sT}} \tag{6}\]  
\[\epsilon_{tT}=\frac{f_{tT}}{E_{sT}} \tag{7}\]  
\[E_{sT}=E_{s}\chi_{T} \tag{8}\]  
In the formula, \(\sigma_{T}\) is the stress of the steel at high temperatures; \(E_{sT}\) is the elastic modulus of the steel at high temperatures; \(\epsilon_{T}\) is the strain of the steel at high temperatures; \(\epsilon_{tT}\) is the tensile yield strain of the steel at high temperatures; \(\epsilon_{cT}\) is the compressive buckling strain of the steel at high temperatures; \(f_{cT}\) is the tensile yield strength of the steel at high temperatures; \(f_{tT}\) is the compressive buckling stress of the steel at high temperatures;  
Figure 2: The stress–strain curve of steel at high temperatures.  
\(\varepsilon_{u}\) is the ultimate strain for tension, taken as 0.02; \(E_{s}\) is the elastic modulus of the steel at room temperature; \(\chi_{T}\) is the reduction factor for the elastic modulus of the steel at high temperatures.
Hence, one of the main parameters in the peak-current control with ramp compensation is the slope of the ramp. Several techniques to design the ramp compensation exist, which include fixed slopes [43; 44], adaptive slopes [45; 46; 47], and polynomial shaped ramps [48; 49]. In this paper, we propose to tune the slope of the ramp in such a way that it guarantees the stability for varying values of input and output voltages, using Floquet multipliers. Once the ramp slope has been set, we complete the stability analysis of the controlled system using Lyapunov exponents and bifurcation diagrams. As far as we know, such complete analysis of the sZETA converter has not been performed to date; with this aim, the paper is organized as follows. In Section 2, we introduce the model of the sZETA converter together with the proposed control. In Section 3, we describe the stability analysis tools that we used, namely calculation of Floquet multipliers and Lyapunov exponents. Next, we devote Section 4 to tuning the value of the ramp slope that guarantees stable operation in several combinations of input and output voltagesand presenting a numerical study of the stability regions of the desired solutions, using the mathematical tools developed in Section 3. In this section, we also describe the way in which such solutions lose stability and we demonstrate the robustness of the proposed control to a wide range of disturbances. Finally, in Section 5, we conclude the paper with some discussion and remarks.
## 4 Conclusions  
In this study, two types of mesoporous silicas with sulfonic acid groups were synthesized and functionalized: MCM-41-SO\({}_{3}\)H, with hexagonal honeycomb structure, and MSU-2-SO\({}_{3}\)H, with a three-dimensional interconnected pore structure. These silicas were characterized and their performance as strong cation-exchange sorbents was evaluated  
Figure 13: Atropine and scopolamine recoveries (% \(\pm\) SD) obtained in SPE assays using 50 mg of MSU-2-SO\({}_{3}\)H or MCM-41-SO\({}_{3}\)H as sorbents when the target analytes were loaded in 10 mL of acidified (pH 1) ethanol or water (0.1 to 30 \(\mu\)g/L of each analyte) and eluted with 6 mL of 5% methanolic ammonia solution. For the remaining conditions, see Figure 2. Error bars represent the standard deviation of replicate samples (\(n=3\)). In each graphic, a different letter indicates a statistically significant difference (\(p\leq 0.05\)).  
in the solid phase extraction of atropine and scopolamine, using ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry. The results indicated that the MCM-41-SO3H material presented the highest recovery efficiency, around 100% for both analytes when acidified water was used as the loading solvent. This was attributed mainly to the accessibility of the sulfonic groups, favored by the regularity and uniformity of their pores. These findings highlight MCM-41-SO3H as a promising sorbent for solid-phase extraction of tropane alkaloids in aqueous solutions, with potential applications in the control of natural toxins in food. Furthermore, this work underlines the importance of the pore structure of the mesoporous silicas in the optimization of extraction processes, opening new perspectives for the development of more efficient and sustainable miniaturized sample preparation protocols, in line with the principles of Green Analytical Chemistry.  
Conceptualization, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; methodology, F.L.V.-B., S.M.-Z. and D.P.-Q.; software, F.L.V.-B. and D.P.-Q.; validation, F.L.V.-B.; formal analysis, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; investigation, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; resources, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; data curation, F.L.V.-B. and D.P.-Q.; writing--original draft preparation, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; writing--review and editing, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; visualization, F.L.V.-B., S.M.-Z., D.P.-Q. and I.S.; supervision, S.M.-Z. and I.S.; project administration, I.S.; funding acquisition, I.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MCIN/AEI/10.13039/501100011033/FEDER, UE, grant number PID2022-137278 OB-I00, EVALKALIM II.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Modification of PLA Scaffold Surface for Medical Applications
## 3 Results and Discussion
### Effect of the Used Gas on the Surface Free Energy and Its Polar and Dispersion Components
In the case of MII, the highest polar component and SFE values (32.5 mJ/m\({}^{2}\) and 60.6 mJ/m\({}^{2}\), respectively) were obtained when the membrane was modified for 60 s. It was also found that the difference in the polar component values obtained for the exposure time in the range of 15-60 s did not exceed 1 mJ/m\({}^{2}\) (Supplementary Materials). The most favorable membrane properties were obtained as a result of plasma treatment carried out in plasma generated in an argon and carbon dioxide gas mixture (50%vol. Ar + 50%vol. CO\({}_{2}\)). In the case of MI, the best modification effects were observed with the shortest exposure time. After 15 s, the surface free energy was 64.3 mJ/m\({}^{2}\), and the polar component was 32.5 mJ/m\({}^{2}\). Increasing the exposure time led to lower values of the SFE and polar component. For MII, the increase in the SFE value and the polar component was observed with an increasing exposure time. The membrane after 60 s of modification had a surface free energy of 67.6 mJ/m\({}^{2}\), and the polar component increased more than threefold compared with the material before modification, reaching 39.5 mJ/m\({}^{2}\). The surface free energy and polar component after modification achieved higher values for the membranes obtained by the dry method than by the wet method. The best properties of the membranes, characterized by the highest increase of the value of the polar component \(\Delta\)p (which is responsible for the hydrophilic properties (Figure 4)), were obtained after the modification of MII. The highest values of \(\Delta\)p were obtained when the process was carried out for 60 s in an argon-carbon dioxide mixture. The values of the SFE and the polar component were 67.6 mJ/m\({}^{2}\) and 39.5 mJ/m\({}^{2}\), respectively, which was most likely the result of surface layer oxidation and the introduction of new functional polar groups. These values correspond to those obtained by Jorda-Vilaplana in a plasma jet in the air (SFE = 60 mJ/m\({}^{2}\)) [16]. Figure 4: The effect of the used gas and exposition time on the polar component (\(\Delta\)p). Discharge power = 20 W.
# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 1 Introduction
It can sense the topology changes, load changes, and network parameters, such as the delay and bandwidth in the satellite network. It can make the best decision according to the network service requirements. This paper proposes a load-balancing routing algorithm based on deep reinforcement learning to address the challenges and opportunities of the LEO satellite network. It aims to solve the issues of traditional algorithms that cannot adapt to the high dynamics and load balancing of the network. The work focuses on the following:  
(1) The satellite routing process is modeled as a Markov decision process (MDP) [6], and its state space, decision space, and reward function are defined. (2) A Deep Q-Network (DQN)- [7] based load-balancing routing algorithm for LEO satellites (DQN-LLRA) is proposed. The algorithm relies only on the state information of the surrounding nodes of the current satellite node, and the optimal decision model is obtained through iterative training. (3) Through numerical simulation, our algorithm reduces the maximum queue utilization and the average queue utilization of the routing path, avoids the occurrence of congestion, and realizes the path routing under load balance while ensuring that the delay cost increases only slightly and in an acceptable fashion. The paper is organized as follows. Section 2 provides an overview of the current research on LEO satellite routing, including its load-balancing challenges and related intelligent algorithms. Section 3 presents the system model and algorithm design of the proposed DQN-based load-balancing routing algorithm for LEO satellites. Section 4 describes the experimental simulations and resulting evaluations carried out to validate the proposed algorithm. Finally, in Section 5, the paper concludes with a summary of the contributions and directions for future research.
## 4 Results and Discussion
### Results at Higher Engine Speeds  
Cylinder deactivation is mainly used to improve the fuel economy for low load conditions of low and middle engine speeds. The above discussions are focused on low engine speed. In order to verify the consistency of results at different engine speeds, the results at higher engine speeds (2400 rpm and 3200 rpm) are shown in Figure 19. The IMEP360 values of 0.88 MPa and 0.98 MPa are selected, respectively, as the operation points of the active cycle for 2400 rpm and 3200 rpm.  
Figure 17: The EGR rate of the active cycle: (**a**) 1200 rpm; (**b**) 1600 rpm.  
Figure 18: The optimal area of VCD operations: (**a**) 1200 rpm; (**b**) 1600 rpm.  
As we can see, delaying the IVC is beneficial for reducing the total energy losses during the transition process and increasing the minimum in-cylinder pressure of the inactive cycle. For the mass fraction of oxygen, it also increases with the delay of IVC when the IVO is same. In addition, the mass fraction of oxygen decreases gradually as the firing density increases with the same IVO and IVC.  
Figure 19: The results of 2400 rpm and 3200 rpm with the proposed VCD valve strategy: (**a**) Total energy losses at 2400 rpm; (**b**) Total energy losses at 3200 rpm; (**c**) The minimum in-cylinder pressure of the inactive cycle at 2400 rpm; (**d**) The minimum in-cylinder pressure of the inactive cycle at 3200 rpm; (**e**) Mass fraction of oxygen at 2400 rpm; (**f**) Mass fraction of oxygen at 3600 rpm.  
Under the same restrictions where the mass fraction of oxygen is under 1% and the minimum in-cylinder pressure of the inactive cylinder is above 0.02 MPa, the optimal areas (A1 and A2) for IVO and IVC are achieved, as shown in Figure 19e,f. Finally, we can see that the results at higher engine speeds are consistent with those at lower speeds.
## 1 Introduction
However, this brings about pulse compression in the frequency domain, which makes it impossible to determine the time difference of the input chirp signals and the frequency bands. In many real applications, the measured signals are sparse or distributed mainly in a small area of the frequency domain, such as the oxygen and water absorption bands in atmospheric radiation measurements. This means that many frequency bands do not have a signal in the entire working bandwidth, a shortcoming that is not taken into consideration in classical digital pulse compression methods. Considering the limitations of the SAW filter and the sparsity of the measured signals in the frequency domain, in the present research, a linear phase sampling and accumulating (LPSA) algorithm was developed for fast pulse compression. The simulation and experiment results show that the proposed method can realize pulse compression with sufficient amplitude accuracy and reduced computation compared to classical digital pulse compression methods when dealing with sparse signals. The contents of this paper are organized as follows. In Section 2, a brief introduction of the CTS system and pulse compression is presented. Some limitations of the SAW filter are also listed. In Section 3, existing digital pulse compression methods are introduced. Subsequently, considering the limitations of the classical digital pulse compression method, a novel fast digital pulse compression method (LPSA algorithm) is proposed. In Section 4, some simulation models based on the CTS system, built using Advanced Design System (ADS), are presented. The results from the different pulse compression methods are compared and analyzed. In Section 5, an experiment to validate the LPSA algorithm is described. The related amplitude accuracy and frequency resolution are measured and the computational complexity is also discussed. A brief conclusion is drawn in the last section.
# Dynamic Dark Channel Prior Dehazing with Polarization
## 3 Experimental Setup
### Polarization Axis Calibration System  
In order to reduce the influence of polarization filtering errors on imaging accuracy, the axis of the polarization state analyzer needs to be calibrated. The schematic diagram of its principle is shown in Figure 5a. When linearly polarized light passes through a polarizer,  
Figure 4: (**a**) Chip model. (**b**) Synchronization control unit.  
Figure 3: (**a**) The three-dimensional model of the mechanical module. (**b**) The structural diagram of the instrument.  
the physical interaction process between them can be quantitatively described by [PERSON]'s law [25]:  
\[I=I_{0}\cos^{2}\theta, \tag{15}\]  
where \(I\) and \(I_{0}\) are the transmitted and incident light intensities, respectively, and \(\theta\) is the angle between the vibration direction of the incidence vector and the polarization axis orientation of the polarizer. According to Equation (15), when \(\theta\) equals to \(90^{\circ}\) and \(\theta\) equals to \(0^{\circ}\), the transmitted light intensity is minimal and maximal, respectively.  
On the basis of the above-mentioned theory, the experimental setup for measuring the polarization axis of the polarizer is shown in Figure 5b. A halogen-tungsten lamp is used as the light source in the experiment. Light passing through the polarized beam splitter (PBS) based on the prism is decomposed into two linearly polarized beams: A beam of linearly polarized light enters a cascade prism along its propagation direction, while another beam of linearly polarized light has its electric field vector oscillating perpendicular to its direction of propagation. The linearly polarized light entering the cascade prism is divided into four detection channels, each equipped with a CMOS camera with a polarizer. Next, we calibrate the angles of the four polarizers.  
Firstly, we calibrate the \(0^{\circ}\) and \(90^{\circ}\) of the PSA by manually rotating two of them. Based on [PERSON]'s law, when the angle between the polarization axis of the rotated polarizer and the polarization direction of the incident light is \(0^{\circ}\), the light intensity imaged on the CMOS camera through the polarizer is maximized, and thus one polarizer's angle can be determined as \(0^{\circ}\). Similarly, when the light intensity imaged on the CMOS camera through the polarizer is minimized, another PSA's angle can be determined as \(90^{\circ}\). Then, we rotate the scales on the other two polarizers by \(45^{\circ}\), respectively, based on the calibrated \(0^{\circ}\) and \(90^{\circ}\), and thus the PSA's \(45^{\circ}\) and \(135^{\circ}\) can be determined.