esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 FPGA Implementation of PID Controller and BB-BC
### PID Controller in FPGA  
In the past, several approaches have proposed the use of FPGA technology to implement PID controllers [20; 21] and/or optimize the PID controller parameters in real-time industrial applications, such as DC motor speed control system [22]. In [23], a fuzzy logic controller has been implemented on an FPGA platform and validated on a real-time application, namely a truck backer-upper control system. In [24], the authors presented a runtime reconfigurable PID controller that can dynamically switch between the different controller variants, e.g., PI, PD, PI-PD, etc., as the control requirements change.  
Figure 5 shows the block diagram of a typical PID controller FPGA architecture based on the differential Equation (6) and the parameters of Table 1.  
The PID controller architecture consists of five registers blocks, for storing the current and previous error values, i.e., \(e(n)\), \(e(n-1)\), \(e(n-2)\), \(u(n-1)\), and \(u(n-2)\). Notice that the PID controller has a five-stage pipeline structure in order to increase clock frequency. Five arithmetic multipliers are used to multiply the proportional part, derivative part integral part with the corresponding parameters (\(k_{3}\), \(k_{4}\), and \(k_{5}\), respectively) and the output signal with (\(k_{1}\) and \(k_{2}\)) as described in Table 1, while the adder finally sums all factors. Embedded DSP slices of the FPGA device are used for the implementation of arithmetic multipliers and adders in order to improve the design area and speed.
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 6 Experimental Results and Comparison
### Experimental Results  
Our system is designed to propose an automatic and efficient electrical load management process. Through investigating the value of cross-entropy loss in normal power consumption over time, to balance the accuracy and sensitivity of detection, the _threshold\({}_{c}\)_ is set to a variable parameter with time. In the non-variation period it is set to 1, and in other periods, it is set to 3.
## 3 Results and Discussion
### Loading Effects
This can be attributed to the greater confining pressure under this condition. Crack propagation in the pipe under surface loading is presented in Figure 5.  
The \(y\) axis depicts the wall thickness. The intrados and extrados of the pipe wall are located at 0 cm and 7.5 cm on the \(y\) axis, respectively. As shown in Figure 5, cracking initiates from the intrados to extrados at the crown and invert. However, at the springline, cracking initiates from the extrados to the intrados. Under longitudinal loading, cracking that initiates in the intrados at the crown propagates outward to a distance of approximately 0.38 cm under a surface load of 200 kPa and approximately 1.9 cm under a surface load of 500 kPa. At the springline, cracking initiates in the extrados and reaches a depth of approximately 0.75 cm under 300 kPa of surface loading. The crack depth then grows to approximately 1.33 cm under 500 kPa of surface loading. At the invert, cracking initiates in the intrados under 300 kPa of surface loading and propagates outward to a depth of approximately 0.76 cm under 500 kPa of surface loading. Under transverse loading, the cracks at the crown initiate in the intrados under a surface load of 425 kPa and propagate outward to approximately 0.76 cm under 500 kPa of surface loading. At the springline, the cracks that initiate in the extrados propagate inward to approximately 0.19 cm under 500 kPa of surface loading. At the invert, cracks initiate in  
Figure 5: Crack depth through the pipe wall under longitudinal, transverse, and uniform loading conditions. the intrados only under 500 kPa of surface loading. Under uniform loading, cracking that initiates in the intrados at the crown propagates outward to approximately 0.57 cm under a surface load of 300 kPa and approximately 3.24 cm under 500 kPa of surface loading. At the springline, cracks initiate in the extrados under the same surface load of 300 kPa but propagate outward to approximately 2.10 cm under a uniform load of 500 kPa more rapidly compared to the longitudinal loading condition. At the invert, cracks initiate in the intrados and reach a depth of approximately 0.38 cm under a uniform surface load of 300 kPa. The crack depth increases to approximately 1.71 cm under 500 kPa of surface loading. The rate of crack propagation can be estimated from the slopes of the curves in Figure 5. These curves indicate that cracks propagate more rapidly under uniform loading than under longitudinal loading. For transverse loading, the amount of data is insufficient to evaluate the rate of crack propagation for comparison.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis
### Real Image Deblurring
When compared with other comparison algorithms, IDR\({}^{2}\)ENet recovered sharper results in the poster and font parts. Compared with Figure 8A, the blurred image of Figure 8B has milder blur degradation. The results of DeblurGAN-v2, DMPHN, and MIMO-UNet+ all show varying degrees of mottled artifacts in the ground portion of the lower right corner of the deblurred image when viewed overall. From the enlarged blocks, IDR\({}^{2}\)ENet still obtains reconstructed results with clearer details. In general, deblurred images of IDR\({}^{2}\)ENet reconstruct the details more clearly and do not generate incorrect artifacts. Furthermore, in order to further verify the effectiveness of IDR\({}^{2}\)ENet on real image deblurring tasks, we tested it on the RWBI dataset. Two images were selected and their visual effects before and after processing are shown in Figure 9 and Figure 10, respectively. In Figure 9, the real blurred image after IDR\({}^{2}\)ENet processing achieves a good deblurring performance, e.g., the edges of the building at the center of real blurred image and the logo on top of it are very clear and do not have any vignettes. However, there still exist some areas where the deblurring performance is not satisfactory, such as tree branches on the right side of the image. In Figure 10, the real blurred image after IDR\({}^{2}\)ENet deblurring is clearly identifiable in the enlarged text part of the letters. Overall, the test results on the RealBlur and RWBI datasets show that IDR\({}^{2}\)ENet is consistently effective and reliable in real image deblurring tasks. Moreover, the author captured some real blurred images using a mobile phone and processed them using IDR\({}^{2}\)ENet, and the comparative results are shown in Figure 11.  
From Figure 11, we can see that the deblurred images after IDR\({}^{2}\)ENet processing no longer have obvious blurred parts in the overall perception, and the text that is most affected by the blur degradation is basically recovered. As a complementary experiment, we also select a low-contrast image from the RealBlur-J dataset to test IDR\({}^{2}\)ENet's performance in low-contrast situations, with the results shown in Figure 12. The results show that our IDR\({}^{2}\)ENet also performs well on low-contrast blurred images. Figure 11: **Deblurring performances of IDR\({}^{2}\)ENet on images captured by a mobile phone. ** From left to right: real blur images, and deblurred images of IDR\({}^{2}\)ENet.
# Ground Simulation Tests in Two-Dimensional Dynamic Acceleration Environment
## 3 Simulation of Impact Overload Environment
### Impact Process of Air Cannon
In the tests, only the initial pressures of the back-pressure and high-pressure chamber are variables, so the initial air pressure of the high-pressure and back-pressure chambers can be adjusted to obtain different launch accelerations. As the displacement of the piston rod increases, the thrust becomes increasingly small, and the piston rod can reverse directions. To avoid reverse collision between the arm and the recoiling air cannon, the piston rod and the arm are not placed at the same level. The collision end of the piston rod is machined into a pulley. The pulley rises before and falls after the collision. When simulating an impact overload environment of 10 \(g\), the simulated air pressures of the high-pressure and back-pressure chambers versus the displacement of the piston rod can be obtained. Figure 3 plots the relationship between the pressure and the displacement of the piston rod. At the displacement of 0.5 m, the air cannon is fired, which is controlled by the air pressures of the high-pressure and back-pressure chambers. The acceleration of the pulley and the tangential acceleration of the arm are plotted in Figure 4. As can be observed, with an impact overload of 10 \(g\), 2 ms  
Figure 3: Relationship between the pressure and the displacement of the piston rod at the impact overload of 10 \(g\). after the air cannon launching, the accelerations peak 10.74 \(g\) and then drop rapidly. The accelerations remain between 6 and 7 \(g\) for about 30 ms, after which they decrease. The entire propulsion time lasts 119.5 ms. At the end of the launching, the tangential acceleration of the arm is zero \(g\).
## 5 Discussion  
In the early stage of this study, the conventional triaxial test and the triaxial test under freeze-thaw cycles were carried out on the deep expansive clay samples, and the mechanical properties of the soil were analyzed.  
With the increase in the number of freeze-thaw cycles, the internal cracks of the soil will continue to expand with the expansion of water ice, which has a huge impact on the structure of the soil. How to properly reflect the damage law of soil under the combined action of loading and freeze-thaw cycles, the key is to select the appropriate damage variable. Many scholars had selected micro-benchmarks for damage analysis under loading [30; 31]; however, there were some defects. The reason is that the initial soil damage has a great degree of randomness and random evolution. If the constitutive relationship of traditional methods is used, the randomness of soil in the damage process cannot be well reflected.  
The reason is that the changes of microstructure and mechanical properties belong to micro and macro aspects, respectively. In order to study this problem fundamentally, it is necessary to combine macro research methods with micro research methods. In order to combine macro and micro research methods, macro and micro experiments must be carried out simultaneously. On this basis, this paper reasonably processes the test data by using probability and statistics methods, introduces appropriate damage variables, and establishes a damage constitutive theory combining the macro and micro.  
Figure 7: The comparison of the damage theoretical curve and test curve of deep expansive clay under different confining pressures. (**a**) 100 kPa, (**b**) 200 kPa, and (**c**) 300 kPa.  
Based on the research experience of previous scholars, this paper further studies the various mechanical properties of deep expansive clay and establishes the corresponding damage constitutive equation. However, due to time and other factors, the article still has shortcomings and needs further improvement:  
1. This paper mainly studies some properties of deep expansive clay, but it does not take into account the great differences in particle size, mineral composition, and structure of expansive clay at different depths. We can further explore the influence of these factors on the mechanical properties of soil.
2. Whether the artificial soil can represent the deep environment and whether it is different from the deep in situ soil samples needs further discussion.
3. Due to the limited amount of undisturbed soil and the test period and test equipment and other factors, the test sample used in the article is slightly insufficient; it can be supplemented by some relevant tests to make the conclusions of the article more reliable.
## 2 Results and Discussion
### Effect of Pressing and Glycosidase on Volatile Composition in Goji Berry Wine
#### 2.3.1 Effect on Dried Goji Berry Fermented Wine  
The pressing treatment resulted in significant differences in the concentration of 27 volatile compounds in the dried goji berry fermented wines (Table 4). For example, the wine fermented from the dried goji berry free-run juice contained higher concentrations of \(\beta\)-cyclodextrin, 3-phenyl-4-hydroxyacetophenone, isopentyl acetate, ethyl octanoate, isobutyl acetate, isobutyl lexanoate, isoamyl octanoate, 1-[2-(isobutyrylov)-1-methylethyl]-2,2-dimethylpropyl 2-methylpropanoate, styrene, and cedrol. Meanwhile, the pressing treatment significantly enhanced the concentrations of decanoic acid, 2-heptanol, 2-ethyl-1-hexanol, 2-undecanone, ethyl caprate, ethyl laurate, hexyl acetate, methyl caprate, diethyl succinate, isoamyl decanoate, and 2,\(\delta\)-dimethyl-2,\(\delta\)-undecadien-10-ol in the dried goji berry fermented wine. Decanoic acid (fatty and rancid odor) [37], ethyl caprate (fruity note) [37], and isopentyl acetate (fruity note) [38] exhibited concentrations higher than their thresholds in the dried goji berry fermented wine with the pressing treatment. The glycosidase treatment did not show a significant effect on the alteration of the aromatic profile in the dried goji berry fermented wine. For example, the increased contents of 1-octen-3-ol, 1-nonanol, and decanal in the dried goji berry fermented wine after the glycosidase treatment were not above their thresholds. The interactive effect from pressing and glycosidase treatments was also limited.
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 2 Materials and Methods  
This study employed a comprehensive methodology to develop and evaluate a hybrid model of Conformer and LSTM for ocean wave height prediction. The Conformer architecture, characterized by its combination of self-attention mechanisms and convolutional neural networks, was detailed. Convolutional Neural Networks (CNNs) were utilized to capture local features, while Long Short-Term Memory (LSTM) networks addressed long-term dependencies in time series data. To ensure the selection of the most relevant features, Spearman correlation analysis was conducted. The overall algorithm was designed by integrating Conformer and LSTM, followed by detailed steps involved in training the hybrid model. The algorithm process was systematically outlined, including a step-by-step guide to its implementation. The dataset, sourced from NOAA buoys, was meticulously preprocessed to handle missing and abnormal data. The performance of the model was evaluated using metrics such as MAE, RMSE, MAPE, and R\({}^{2}\) within a specified software and hardware environment.
## 3 Circularly Polarized 5G Antenna Design and Optimization
### Geometry 4  
The geometry 4 of RDRA is presented in Figure 2d. This design configuration was achieved by adding an additional length to the feeding strip. At this stage, the feeding strip was composed of 4 different lengths. The impedance matching (\(\,\)\(\rm 1S_{11}\)\(\,\leq\)\(\,\)10 dB) over a bandwidth of \(\sim\)8.8% was attained by this configuration, as shown in Figure 3. The degenerate mode pair of the first higher-order, i.e., TE\({}^{\rm x}_{\rm 813}\) at 4.14 GHz and TE\({}^{\rm y}_{\rm 183}\), at 4.55 GHz was excited to generate the circularly polarized response. The circular polarization over a bandwidth of \(\sim\)5.8% was provided by the antenna. A significant improvement was observed in S\({}_{11}\) and AR ratio bandwidths but, still, the achieved CP response was not enough to cover the desired bandwidth i.e., 4.4-4.8 GHz. This design configuration was again modified to make the final proposed geometry.
# A SLR Pre-Processing Algorithm Based on Satellite Signature Effect
###### Abstract  
As one of the major error sources, satellite signature effect should be reduced or even erased from the distribution of the post-fit residuals to improve the ranging precision. A simulation of satellite signature effect removal process for normal point algorithm is conducted based on a revised model of satellite response, which fully considers the structural and distribution characteristics of retroreflectors. In order to eliminate both long-term and short-term satellite signature effect, a clipping method for SLR data processing is proposed by defining the clipping location as 5.6 mm away from the mean value of the long-term fit residuals to select effective returns for normal points. The results indicate that, compared to normal points algorithm, the RMS per NP of LAGEOS-1 observation data processed by the clipping method is reduced from 62.90 \(\pm\) 9.9 mm to 56.07 \(\pm\) 4.69 mm, and the stability of RMS is improved 53%. This study improves the satellite signature effect model and simulates the fluctuation of normal points caused by satellite signature effect for the first time. The new method based on the simulation of satellite signature effect has stronger robustness and applicability, which can further minimize the influence of satellite signature effect on the SLR production and significantly improve the data property.  
satellite laser ranging; satellite signature effect; satellite geodesy; LAGEOS-1; pre-processing algorithm 22 August 2021  
13 September 2021  
1
## 6 FPSE Method Development
### Selection of FPSE Sorbent Chemistry  
As can be seen in Table 1, FPSE offers a broad range of sorbents spanning from nonpolar, to medium polar, to polar, to ionized, to mixed mode, and to zwitterionic. As such, it is practically impossible for one to determine the most efficient sorbent by real experimentation. As such, for the first time, a new approach for selecting FPSE sorbent chemistry has been developed based on an absolute recovery percentage calculator that utilizes the logKow of an analyte to predict an estimated absolute recovery of an analyte for a given FPSE sorbent chemistry. For example, the absolute recovery on sol-gel Carbowax 20M (sol-gel CW 20M) sorbent coated on 100% cotton cellulose fabric can be expressed as:  
Absolute Recovery % = 4.2977487 + 22.823041 \(\times\) Log Kow \(-\) 3.1343544 \(\times\) (Log Kow \(-\) 2.737)2  
This equation is valid for any analyte possessing a logKow value between 0.3 and 5.07, and majority of the analytes we generally encounter fall in this range. During the FPSE method development exercises, it is recommended that an analyst select the 3 best FPSE membranes, and subsequently determine the best FPSE membrane by exposing them under identical FPSE conditions. A good starting time can be:  
FPSE membrane size: 2.5 cm \(\times\) 2.0 cm;  
Sample volume: 10 mL;  
Analyte concentration: 1 mg/mL;  
Extraction time: 1 h;  
Stirring speed: 800 rpm;  
Desorption solvent: methanol;  
Desorption solvent volume: 500 mL;  
Desorption time: 10 min.  
Figure 2: Factors and their relative importance on fabric phase sorptive extraction method development.  
The prepared sample can be injected into a gas chromatograph or high-performance liquid chromatograph to obtain the chromatographic signal area for an analyte or group of analytes.  
Absolute recovery calculations for major FPSE sorbent chemistries are presented in Table 2.  
The predicted absolute recovery values often corroborate with the actual recovery values obtained from real experimentation, as demonstrated by several researchers [11; 12]. It is important to note that this model was developed using analyte solution in deionized water. When the sample matrix contains too many matrix afferents, substantial deviation from the expected recovery of the analyte may be observed [13].
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations
### Justice: Promoting Prosperity and Preserving Solidarity
#### 4.4.1 Geographic Scope  
The geographic scope of an event-based disease surveillance system may vary [21; 27]. It could cover a region, a country, a continent, or the entire globe. The following jurisdictions have established, hosted, and maintained such systems: United States, European Union, Canada, Japan, Australia, Brazil, Singapore, Mexico, and Thailand [11; 15]. While there is immense potential opportunity for AI to support event-based surveillance of aggressive disease outbreaks such as COVID-19, the issue of missing data is especially present in low- and middle- income countries (LIC and LMIC), which may also lack the infrastructure and human capital required to maintain these systems [17; 75; 76; 74; 77]. Geographic inequalities in event-based system maintenance may constitute significant barriers to achieving justice in global health outcomes, hence improvements in geographic coverage should become a priority.
## 3 Results & Discussion
### MAE of Bioactive Compounds
#### 3.1.3 Effect of Temperature  
The effect of increasing temperature, from 40 to 50 and eventually 60 \({}^{\circ}\)C, on the MAE at 15 min, 55 \(\mathrm{mL}_{\mathrm{solv}}/\mathrm{g}_{\mathrm{biom}}\) and 550 watts is illustrated in Figure 3. The initial increase from 40 to 50 \({}^{\circ}\)C did not favor the extraction yield, while further temperature elevation led to improved recovery of total extract at 60 \({}^{\circ}\)C (Figure 3a). Moreover, the extract's total pigment content was moderately improved during temperature increase (Figure 3c,e). The above observations could be justified by the decrease of solvent's viscosity from 40 to 60 \({}^{\circ}\)C, and therefore an increase of its solvation power [57; 58].  
On the other hand, the extract's antioxidant activity deteriorated during the temperature increase (Figure 3f). This could be attributed to the reduced content of lutein (Figure 3d), which presents much higher antioxidant activity compared to other carotenoids, e.g., 15- and 10-time folds of lycopene and \(\beta\)-carotene [59]. In particular, the selected carotenoid content presented a downward trend, probably due to the domination of their degradation instead of their extraction, also noted in the literature during microwave radiation at temperatures close to 60 \({}^{\circ}\)C [60]. Moreover, chlorophylls present appreciable antioxidant activity at high concentrations [61], and thus the moderate increase of chlorophyll content could not significantly affect the extract's antioxidant activity.  
Finally, total phenolic content presented a slight decrease, but the temperature rise was considered imperceptible (Figure 3b). A similar weak temperature effect was also observed in the literature [53; 62].
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 2 Methodologies  
### Optimization Framework  
The aerodynamic shape optimization framework is illustrated in Figure 1, which includes a CST-based morphing airfoil parameterization, a mesh deformation tool, a flow and adjoint solver, and a gradient-based optimizer. For each iteration, the optimizer invokes each component sequentially if necessary. The baseline airfoil is initially prepared as a mesh file, and the corresponding shape is identified by the shape parameterization method as a vector of design variables. Then, the optimizer launches the primary flow solver and adjoint solver successively, in order to evaluate the objective function and obtain the sensitivities with respect to the surface mesh. The obtained surface sensitivities are then projected into design space through geometric sensitivities (also known as design velocities). The optimizer drives the optimization loop, finds new design variables, deforms the mesh, and evaluates the new shape.