esa-sceva/satcom-chunk-collection huggingface dataset:
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 4 Experimental and Analysis
### Comparison with State-of-the-Art Methods
Figure 9 shows the visual comparison of the \"Zebra\" image in the contrast methods in Set14. It tends to be seen from Figure 9 that SRCNN, VDSR and DRCN cannot recover the edge and texture information effectively, and the generated image is blurry. Although LapSRN, IDN, DPSR, PAN, LAPAR-A, IMDN, DASR and DeFiAN can roughly recover edge and texture information, it also generates artifact detail. Our method can readily re-establish the stripes details of the zebra. This is mainly because our GFSR effectively integrates the extracted features of each layer, and utilizes the channel attention mechanism to explore the relationship between channel features. Figure 8: Visual comparison of super-resolution results of ‘Butterfly” (Set5) obtained by different SR algorithms with scaling factor \(\times\)4. Figure 9: Visual comparison of super-resolution results of ‘Zebra’ (Set14) obtained by different SR algorithms with scaling factor \(\times\)4. Figure 10 presents the visual comparison of the \"86000\" image in the comparison method in BSD100. From Figure 10, it can be seen that although the reconstructed image of DeFiAN is superior to that of SRCNN, VDSR, LapSRN, DRCN, IDN and DPSR as far as edge details, there are a host number of artifacts. Contrasted with the DeFiAN, the methods such as IMDN, PAN, LAPAR-A, SMSR and DASR have fewer artifacts, and the reconstructed images obtained by them successfully smother the presence of artifacts, but edge recovery is inadequate compared with our proposed GFSR. Figure 11 illustrates the visual comparison of the \"Img_067\" image in the comparison method in Urban100. As can be seen from Figure 11, a large portion of the methods, such as SRCNN, VDSR, LapSRN, DRCN and IDN, the reconstructed images appear severely blurred and even lose the fundamental structure. While DPSR, IMDN, PAN, LAPAR-A, SMSR, DASR and DeFiAN can recover the primary shapes, but cannot recover more image subtleties. Interestingly, our GFSR can get more clear outcomes and recover more high-frequency details. This is mainly because we use the prior guidance of gradient features in the model, which has a strong ability to reconstruct the edge features of buildings.
## 2 Materials and Methods
### Generation and Expansion of DCs from Peripheral Blood Monocytes of Healthy Donors  
We used leukocyte-platelet buffy coats (\(n=6\)) obtained from volunteers recruited during routine medical consultations in the Regional Blood Bank in Gdansk, Poland, and only healthy individuals were included in this study. Peripheral blood mononuclear cells were separated by Histopaque(r)-1077 gradient centrifugation at 1200 g, 30 min at room temperature (RT). After isolation and erythrocytes' lysis, cells were washed and prepared for further isolation steps. To separate monocytes, PBMCs were cultured for 24 h on an adhesive Petri dish in RPMI 1640 supplemented with FBS (10%), L-glutamine (2 mM), penicillin (100 U/mL) and streptomycin (100 ug/mL), at 37 \({}^{\circ}\)C, 5% CO2, 95% humidity. After incubation, a medium containing non-adherent cells was gently removed, and the plate with adherent cells was put on ice for 30 min. Afterwards, the monocyte layer was harvested using a scraper.  
A total of 1 \(\times\) 10\({}^{6}\) adherent cells (comprising mostly monocytes, as confirmed by flow cytometry)/1 mL were placed on 24-well plates in a medium supplemented with GM-CSF (50 ng/mL) and IL-4 (100 ng/mL) for 7 days. On day 3, half of the medium was replaced with a fresh medium containing these cytokines. On day 6, cells were subjected to maturation for 24 h in the presence of LPS (50 ug/mL) or cancer cell lysates. Lysates were obtained from the culture of HCT116 and HT29 cell lines (allogenic in relation to DCs). Untreated DCs (immature, iDCs) were regarded as control.  
Cells' differentiation and maturation were monitored and documented using Olympus CKX53 inverted microscope coupled with digital camera Olympus SC50 (Olympus, Japan). The analysis and measurements of DCs length were performed using Olympus cellSens software (Olympus, Japan).
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Land Cover Classes  
Land cover types were divided into six classes (sandy land, built-up land, cultivated land, forest land, other land, and water) in this study (Table 2) [27]. Next, the samples were selected from historical images in Google Earth Pro using a visual assessment. A total of 1000 sample points were collected in 1990, 2000, 2007, and 2019, respectively. The collected samples were divided into 70% training data and 30% validation data.
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 4 Experimental Study of Four-Star-Type Crank Linkage
### Analysis of Experimental Data
#### 4.2.3 Analysis of the Vibration Response of the Bench  
Vibration measurement points no. 5, 6, and 7 measure the base plate vertical vibration, the upper plate vertical vibration, and the vibration in the direction of the first-level slider movement of the upper plate, respectively. The specific locations of the measurement points are shown in Figure 31b. The frequency-domain diagram of vibration acceleration at each measurement points from 0 to 1000 Hz at 240 r/min is shown in Figure 33. In the 0-300 Hz frequency band, the horizontal acceleration of the upper plate > the vertical acceleration of the upper plate > the vertical acceleration of the base plate. After 300 Hz, the vertical acceleration of the upper plate is higher than the horizontal acceleration overall. However, from the whole frequency band (0-1000 Hz), the horizontal acceleration level is 1.05 dB higher than the vertical one, which indicates that the whole spectral energy is relatively concentrated in the low-frequency band. Under the same conditions, the vibration control effect of suppressing the low-frequency vibration of the bench will be relatively obvious in the wide frequency band.  
Comparing the two cases of no wear and big wear, the existence of clearance will increase the vibration of the bench. The increments of vibration acceleration levels at measurement points 5, 6, and 7 are 0.50 dB, 0.13 dB, and 0.42 dB, respectively. The acceleration increase of the base plate is obvious, mainly because the flywheel is installed near the base plate. In addition, the bearing mounting hole of the base plate has a certain clearance, while the other end of the crankshaft is directly connected to the motor output shaft, and the restraint is more solid. The presence of the clearance mainly affects the horizontal movement of the slider; thus, there is a 0.42 dB increment in the horizontal vibration acceleration level of the upper plate.  
Figure 31: Phase diagram of slider acceleration for different clearance sizes.  
Figure 32: Phase diagram of slider acceleration at different rotational speeds.  
The speed regulation interval between 80 r/min and 240 r/min is divided into five equal parts and marked on the regulator to obtain the vibration acceleration levels in the 0-1000 Hz frequency band for measurement points 5, 6, and 7 under six operating conditions as shown in Figure 34. With the increase of motor speed, the vibration acceleration level generally shows a linear increasing trend. The difference between vertical and horizontal acceleration of the upper plate has a process of first decreasing and then increasing. The presence of the clearance accelerates the appearance of the minimum difference. The horizontal and vertical accelerations of the upper plate are significantly higher than those of the base plate. The presence of the clearance makes the acceleration level of the base plate more linear with the working condition in the low-speed section.
# Hardware Prototyping and Validation of a W-\(\Delta\)DOR Digital Signal Processor
## 3 W-\(\Delta\)DOR Hardware Implementation
### W-\(\Delta\)DOR DSP Core Architecture Description  
The W-\(\Delta\)DOR DSP core is implemented in the FPGA available in the transponder digital section and generates the signal defined by Equation (2). Detailed system specifications are provided in [1]. The system clock frequency is \(8F1=76.8\) MHz where \(F1\) is 9.6 MHz for compatibility with the work in [1,11]. The main technical specifications are:  
* Selectable chip rates: \(\frac{F1}{2}\), \(\frac{F1}{3}\), \(\frac{F1}{4}\) and \(\frac{F1}{8}\) for tuning the spectrum width of the DOR observables;
* Selectable sub-carrier tones: \(\frac{F1}{2}\) and \(2F1\), for the spectrum allocation of the observables;
* Complete in-flight parameters reprogrammability; and
* Backward compatibility with narrowband \(\Delta\)DOR.  
The LEON2-FT microprocessor is in charge to enable/disable the core. Moreover, the LEON2-FT can read and write the W-\(\Delta\)DOR core parameters by using a register file internal to the FPGA. These registers contain different parameters such as chip rates and sub-carriers frequency words. A control register is also included to enable and reset the W-\(\Delta\)DOR core and to set up the narrowband compatibility mode. The on-chip communication occurs through a dedicated bus controlled by a custom IP core called LEON2-FT Interface Bus Controller. The W-\(\Delta\)DOR core has two outputs: Root Raised-Cosine (_RRC_) and Non-Return-to-Zero (_NRZ_). The RRC output is interfaced with a 10-bit D/A converter connected to both the X and Ka transmission modules, while the NRZ signal feeds the Ka transmission module. The architecture of the core is detailed in Figure 4; it consists of two sub-parts, named _RRC Section_ and _NRZ Section_.  
The proposed W-\(\Delta\)DOR core has been designed for \(N=3\) with reference to Equation (2), with one NRZ- and two RRC-shaped signal components. Hence, the RRC and NRZ sections have, respectively, two and one data paths. A detailed description of the W-\(\Delta\)DOR core components is provided in
## 1 Introduction
In this paper, we propose a simulation framework for assessing the interdependency of integrated gas and power systems in terms of security of supply. The framework combines a steady state AC-flow model with a transient hydraulic gas model and captures the physics of both systems. The data exchange between both models is established through a developed software application named SAInt (Scenario Analysis Interface for Energy Systems), which contains a graphical user interface for creating the network models and scenarios and for evaluating the simulation results. The proposed framework implemented in SAInt, is intended to be used by system operators, researchers, operational planners interested in analyzing the operation and interdependency of gas and electricity systems in terms of security of energy supply; i.e., to analyze the cascading impacts of contingencies on the operation of integrated gas and power systems and to assess system flexibilities by providing information on system abilities to react to changes. To achieve these goals, the paper follows the following pattern. In Section 2, we give an overview of available models in the scientific literature addressing the analysis of combined gas and power systems and highlight the gaps in the literature we intend to fill. Section 3 discusses the different modeling aspects to be considered in a combined gas and power system model for assessing security of supply, while Section 4 elaborates the developed simulation framework and its implementation into a software application. In Section 5, we apply the proposed model to perform a contingency analysis on a real life sized test network. Finally, the conclusions are given in Section 6.
## 6 Hardware Implementation Evaluation  
In addition to causing performance overhead at the processor, the integrated SPU also inevitably increases the original SoC with hardware complexity, resource overhead, and power consumption. The CPU-SPU collaborative SoC was synthesized, implemented, and evaluated on a Xilinx Virtex-5 FPGA development board. In addition, Synopsys back-end design tools of Design Compiler (DC) and IC Compiler (ICC) were utilized to synthesize the SoC into technology-mapped gate-level netlists, and complete automatic placement and routing based on SMIC 130-nm CMOS standard technology library.  
Table 6 presents the CPU-SPU collaborative SoC hardware implementation on FPGA and ASIC. According to the resource utilization of FPGA, the hardware-implemented  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Security** \\ **Mechanism** \\ \end{tabular} } & \multicolumn{5}{c}{**Security Capability**} & \multicolumn{5}{c}{**Comprehensive Practicality**} \\ \cline{2-7}  & \multirow{2}{*}{**Level**} & **Instruction** & **Fault** & **Coarse/Fine** & **ISA** & **Compiler** & **Performance** \\  & & **Tampering** & **Self-Repairing** & **Grain** & **Extension** & **Modification** & **Overhead** \\ \hline HAM [31] & I & Yes & No & Coarse & No & No & Medi (5.59\%) \\ CFI-LEA [15] & I & Yes & No & Coarse & Yes & Yes & Low (3.19\%) \\ CCFI [32] & I & Yes & No & Coarse & Yes & Yes & High (52.0\%) \\ CEDA [33] & I & Yes & No & Coarse & Yes & Yes & High (10.5\%) \\ AE-SSS [34] & II & Yes & No & Fine & No & No & Medi (7.70\%) \\ HCIC [35] & II & Yes & No & Fine & No & No & Low (0.95\%) \\ CLR-REV [36] & II & Yes & No & Fine & Yes & Yes & Low (1.87\%) \\ CPU-ASP [37] & II & Yes & No & Fine & No & No & Low (2.52\%) \\ \hline Our SPU & III & Yes & Yes & Fine & No & No & Low (1.92\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of different security mechanisms in terms of security capability and comprehensive practicality.  
SPU occupies about 9.83% on the total slices of SoC and consumes a certain amount of on-chip storage resources for integrity checking and checkpoint-rollback operations. On the whole, our integrated SPU is a relatively smaller hardware module compared to the overall CPU-SPU collaborative SoC.  
We implemented the LHash engine as a hardware-friendly algorithm, where the Feistel-PG internal permutation structure only requires 817 gate elements (GE), its hardware consumption is less than another lightweight hash implementation of PHOTON [38], where the internal permutation requires 1120 GE. From the ASIC implementation of SoC, the extended SPU occupies 36.8% of the chip area, which is larger than the proportion of 9.83% on FPGA after automatic routing because of more RAM IP library placements, and its dynamic power maintains a low power consumption. Therefore, the proposed SPU reaches a good balance between security capability and hardware overhead.
## 4 Discussion
As a consequence, the system was required to be flexible enough to define semantic structures with no limitation on the number of depth levels that sections can have in compliance with the ISO 13606 and openEHR specifications. This causes certain limitations when we want to export implementable forms in systems such as OpenClinica or Diraya due to the maximum depth of sections in these systemsbeing just one level. Although systems with increased flexibility of depth can be found, they always have a maximum level of depth. This is reasonable since the screen presentation capabilities will not be able to display an unlimited number of subsections. Two approaches were tested to overcome this problem: providing guidance to modelers about the limitation of depth of subsections and providing automatic mapping that reduces the number of sublevels included in the structure. Issues designing implementable forms for data analytics: Given that the system has been designed to support the establishment of how data will be managed in data collection tools and analytics tools, it was recommended to define from the beginning those items that will be applied in the data analysis. This is especially relevant when data analysis is performed from variables that are derived from transformations and calculations from the original collected data. There were identified benefits from specifying these transformations at the moment of designing data collection since they were allowed to define automatic transformations for data analysis of the collected data. Limitations and future work: This research was focused initially on the definition of a semantic consistent transformation between multiple EHR specifications with a generic reference model such as openEHR, ISO 13606 and CDISC. Additional work will be required to define a library of resources correctly mapped with other specifications whose reference model is dependent on the information carried out such as HL7 FHIR and HL7 CDA. It is expected that it will be carried out as long as future use cases based on these specifications will emerge. They will be able to benefit from the ongoing cross-mapping work carried out by openEHR and HL7.
## 2 Physical Modeling of the Tunnel  
Laboratory experiments such as sieve analysis, field density using the core cutter, and pycnometer are carried out to find the physical characteristics of the soil used in our experiments. Figure 2 shows the particle-size distribution curve through which the soil could be classified as sand. The average field density determined using the core cutter was found to be 1.6043 gm/cm\({}^{3}\) and the dry density was 1.53 gm/cm\({}^{3}\). The pycnometer test was performed to find the average specific gravity of sand which turns out to be 2.65. The lab-test data were later used to find an appropriate soil model for numerical analysis.  
In the present study, a cubical tunnel box of 1 m dimensions was manufactured with a galvanized iron sheet of 14-gauge thickness. An opening of 30 cm diameter on the vertical walls was provided to insert the lining. A steel frame was attached to the tunnel box for levelling and measurement purposes. The schematic diagram of the laboratory model is shown in Figure 3. Tunnel lining was simulated through an aluminium sheet with 26-gauge thickness.  
The experimental study involved various phases as described below.
## 3 Results
### Ground Reaction Forces and Joint Dynamics
#### 3.1.1 Peak Collision Force

Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Intensity** & **Time** & **EP (N/s/kg)** & **d vs. Baseline** & **d vs. Baseline** & **d vs. Baseline** & **d vs. Baseline** \\  & & **95\% CI** & **95\% CI** & **95\% CI** & **EM (N/s/kg)** & **d vs. Baseline** \\ \hline \multirow{4}{*}{Baseline} & \multirow{4}{*}{30 s} & \(-\)27.47 \(\pm\) 7.16 & - & 14.27 \(\pm\) 2.86 & - & 2.7 \(\pm\) 0.44 & - \\  & 30 s & 4.94 \(\pm\) 5.9 & 0.37 [\(-\)0.07, 0.81] & 13.28 \(\pm\) 2.7 & 0.36 [\(-\)0.08, 0.80] & 2.6 \(\pm\) 0.5 *,* & 0.24 [\(-\)0.20, 0.68] \\  & 4 min & \(-\)31.37 \(\pm\) 7.11 * & 0.55 [0.11, 0.99] & 17.29 \(\pm\) 2.55 *,* & 1.18 [0.74, 1.62] & 2.92 \(\pm\) 0.45 *,* & 0.52 [0.08, 0.96] \\  & 8 min & \(-\)31.61 \(\pm\) 6.48 *,* & 0.61 [0.17, 1.05] & 17.6 \(\pm\) 2.17 *,* & 1.20 [0.76, 1.64] & 2.9 \(\pm\) 0.46 *,* & 0.46 [0.02, 0.90] \\  & 12 min & \(-\)31.5 \(\pm\) 7.19 *,* & 0.58 [0.14, 1.02] & 18 \(\pm\) 2.83 *,* & 1.15 [0.71, 1.59] & 2.86 \(\pm\) 0.42 *,* & 0.36 [\(-\)0.08, 0.80] \\  & 30 s & \(-\)24.89 \(\pm\) 6.88 * & 0.36 [\(-\)0.08, 0.80] & 12.64 \(\pm\) 2.57 & 0.57 [0.13, 1.01] & 2.55 \(\pm\) 0.54 * & 0.28 [\(-\)0.16, 0.72] \\  & 4 min & \(-\)33.65 \(\pm\) 7.25 *,* & 0.86 [0.42, 1.30] & 18.13 \(\pm\) 2.53 *,* & 1.31 [0.87, 1.75] & 3.03 \(\pm\) 0.43 *,* & 0.76 [0.32, 1.20] \\  & 8 min & \(-\)33.47 \(\pm\) 7.01 * & 0.84 [0.40, 1.28] & 18 \(\pm\) 3.33 *,* & 1.20 [0.76, 1.64] & 3.09 \(\pm\) 0.43 *,* & 0.91 [0.47, 1.35] \\  & 12 min & \(-\)33.68 \(\pm\) 7.47 * & 0.87 [0.43, 1.31] & 18.6 \(\pm\) 2.97 *,* & 1.34 [0.90, 1.78] & 3.05 \(\pm\) 0.4 *,* & 0.79 [0.35, 1.23] \\  & 30 s & \(-\)25.82 \(\pm\) 6.37 & 0.23 [\(-\)0.21, 0.67] & 13.7 \(\pm\) 2.13 & 0.45 [0.01, 0.89] & 2.71 \(\pm\) 0.39 & 0.02 [\(-\)0.42, 0.46] \\  & 4 min & \(-\)28.19 \(\pm\) 6.64 & 0.10 [\(-\)0.34, 0.54] & 14.65 \(\pm\) 2.01 & 0.13 [\(-\)0.31, 0.57] & 2.76 \(\pm\) 0.41 & 0.14 [\(-\)0.30, 0.58] \\  & 8 min & \(-\)28.51 \(\pm\) 7.2 & 0.15 [\(-\)0.29, 0.59] & 14.96 \(\pm\) 2.5 & 0.  
condition (\(p<0.05\)), although no significant differences were observed between the high- and medium-speed conditions (see Table 2).
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 4 Discussion
### Fine-Spatial-Resolution Remote Sensing Data Improves GPP Estimations  
The fine-spatial-resolution GPP product (GPPL) showed good agreement with the GPP observed at the flux sites (Figures 3 and 4; Table 4), which indicated that the GPPL could characterize the spatial and temporal change in GPP better than the coarse-spatial-resolution GPP products. The GPPL significantly improved the accuracy of the GPP simulations in the cropland and dryland ecosystems [20]. Our results suggested that the GPPL provided more detailed information with which to delineate the GPP distribution (Figures 5-7). Fine-resolution images can be used to characterize the spatial heterogeneity of roads, buildings, farmland in cropland ecosystems, and grass and trees in savanna ecosystems. The improved consideration of the seasonal change in GPP in savanna may  
Figure 8: (**A**) Proportion of three types of reconstructed NDVI data and (**B**) relative prediction error (RPE), (**C**) R\({}^{2}\), and (**D**) RMSE of GPP driven by different reconstructed NDVI among nine vegetation types. All the NDVI and GPP data were collected from the 193 investigated sites.  
improve our understanding of its dynamic carbon sequestration [20; 21]. Furthermore, the \(\mathrm{GPP_{L}}\) of evergreen broadleaf forest can represent the GPP decrease in the dry season better than coarse spatial GPP products (Figure 4C). Therefore, the \(\mathrm{GPP_{L}}\) characterizes the complexity of GPP [10] and supports specific applications in spatially heterogeneous regions [4].  
The \(\mathrm{GPP_{L}}\) represented the magnitude of the GPP because we reparametrized the coefficients in the fine-spatial-resolution EC-LUE model. On the one hand, [PERSON] et al. (2015) reported that at the Nebraska extension cropland experiment site (Figure 5) [22], the maximal daily GPP of soybean could reach 18 g C m\({}^{-2}\) d\({}^{-1}\), but both the \(\mathrm{GPP_{M}}\) and the \(\mathrm{GPP_{A}}\) significantly underestimated this condition. After optimizing the coefficients in the EC-LUE model, the peak of the \(\mathrm{GPP_{L}}\) was 17.5 g C m\({}^{-2}\) d\({}^{-1}\), which was close to the observed GPP [8]. Thus, using the \(\mathrm{GPP_{L}}\) to evaluate the amount of GPP in a cropland area can better characterize the growing condition of crops and precisely estimate their yield [23]. On the other hand, in desert regions, vegetation begins to grow after rainfall, but its distribution is sparse. The land-cover classification in coarse-spatial-resolution data may identify these areas as non-vegetated, so the GPP is set to zero. However, fine-spatial-resolution data can be used to detect very small changes in vegetation growth, as indicated by NDVI, in the form of pixels covered by vegetation [24]. Moreover, although the total amount of GPP in dryland may be lower, its expansion leads to substantial changes in GPP [25]. Therefore, the \(\mathrm{GPP_{L}}\) product can precisely simulate the total amount of photosynthesis over a large region.