esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Pol Transponder with Frequency Shifter for
## 3 Results  
### Test Bench of the System  
Figure 4 shows the implementation of a upconverter realized with a mixer (SPECWAVE MM96P-L15), a LO at 2.0 GHz (HP 8672A), and a high-pass RF filter (FILTRONIC SB 029). Two band-pass filters (MINICIRCUITS VHF-3800+ and FBP-5.8-11G tuned in the band 5.8 GHz-10.1 GHz) are used for limiting the input band.  
Figure 3: Monostatic/bistatic GBSAR with frequency shifter transponder.  
The isolation of the transponder with the upconverter was measured with the test bench depicted in Figure 5.  
Figure 6 shows the amplitude plot in frequency of the signal before the upconverter (\"direct path\" in Figure 6) and after. The minimum isolation resulted in 71.90 dB, which was enough to prevent the oscillation of an amplifier with gain up to 50-60 dB.  
Figure 4: Upconverter operating at 10 GHz with 2 GHz frequency shifter.  
Figure 5: Test bench for measuring the isolation of a upconverter operating at 10 GHz with 2 GHz frequency shift.  
The upconverter requires a downconverter at the radar head. In order to test the capability of the up- and downconverters to correctly detect the phase of the radar signal, we implemented the test bench shown in Figure 7. In order to simulate the measured differential phase of a target, we inserted a manual phase shifter (ARRA 9428B) between the Vector Network Analyzer (VNA) and the upconverter. The two signal generators at 2.0 GHz were locked up with a 50 m coaxial cable that shared the 10 MHz reference signal.  
Figure 8 shows the phase measured when the signal passes through the up- and downconverters and the phase measured directly (bypassing the up- and downconverters). The x-axis represents the marker number of the manual roll of the phase shifter (ARRA 9428B).  
Figure 9 shows the plot of the differences. The average difference was 2.78 deg. This difference appears to be a systematic error (probably due to nonideality of the mixers and filters),  
Figure 6: Plot in frequency. Red represents the amplitude of the signal through the chain with up- and downconverters; blue represents the amplitude of the signal that bypass the chain through the “direct path”.  
Figure 7: Test bench for testing the phase shift detection.  
which is compensated by the calibration procedure and which cannot anyway affect the differential measurement of the targets' displacements in any case.
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 5 Results
### First Set of Tests
#### 5.2.2 Method Two  
This method is effective in all of the tests except for Test 6. With regard to Figure 12, which shows the limits achieved using this method, the relation between the detection limits and frequency is no longer linear. The minimum is increasing with the frequency until it reaches a maximum peak on 34 kHz; then, it starts to decrease when moving toward 37 kHz. The same trend is observable for the detection length and safe zone margins. This means that this algorithm is a function of both the SNR of the noise and the defect, as well as the frequency where the optimum frequency would be around 34 kHz. Nonetheless, it should also be pointed out that when using a lower frequency, the same happens as in the previous method: the minimum value for detection is closer to zero, which means that defects would be easier to detect. Regarding the second thresholding version, the increase in the minimum limits is not as much as those in the maximum, which therefore increases the safe zone margins in all of the tests. This is clearly observable in tests 1 and 2, where the minimum limits remained unchanged. The average and maximum detection lengths of the defect remain unchanged. Therefore, using the second threshold version provides a greater safe zone margin.  
Figure 11: Minimum, average, and maximum values of method one of the first set of tests.  
Figure 12: Limits achieved using method two on the first set of tests.  
In comparison to method one, this method has a greater range of operating frequencies and provides a larger safety margin for threshold selection. The optimum excitation frequency for this method is 34 kHz for the first set of tests.
## 3 Utilized Tools and Their Integration
### Setup for Software-In-The-Loop Simulation  
It is very common that Software-In-The-Loop (SITL) simulation is often used for testing the performance of an algorithm under development. This utility saves time and cost of repair of probable crashes in real flight test scenarios.  
Open-source software such as px4 flight control firmware, Gazebo simulator, and Robot Operating System (ROS) were integrated and used for the development and performance testing of 3D path planner. Gazebo is a dynamic 3d model simulation environment particularly suitable for obstacle avoidance and computer vision. This simulation environment is enriched with simulated sensors that mimic the real sensors on-board the UAVs. YOLO object detector, with its Darknet architecture wrapped with ROS, was also used to publish information about obstacles in the UAV's navigation environment. For training and validation of YOLO, images of the 3D models of the objects simulated in the gazebo simulation environment were taken. The Images were taken under various backgrounds and lighting conditions.  
The 3D path planner algorithm that prompts px4 flight controller to send actuator commands to quadcopter model in gazebo simulator was developed as an ROS node. Hardware models implemented in this SITL Gazebo simulation are iris quadcopter, depth stereo camera, three ultrasonic sensors, and LiDAR as shown in Figure 1.  
The camera is for the frontal environment's image input, LiDAR is for quadcopter's altitude estimation in combination with GPS, and the ultrasonic sensors are used to detect lateral obstacles that may be encountered during takeoff and rolling. The 3D path planner acquires information from the aforementioned sensors in Gazebo simulator using Gazebo_ros packages that enables sensors to publish their information. The whole process runs on the desktop computer, whose software specifications are given in Table 1.
## 4 Experimental Results  
In order to demonstrate the effectiveness and feasibility of our method, we conducted multiple experiments for two typical CT images--pelvic and abdomen images. This work utilized the algorithm in reference [48] to generate projection data. The size of the images is 256 \(\times\) 256. From the X-ray source to the detector/scanned object, the distance is 80 cm and 40 cm, respectively. The number of detectors is 512, with a length of 41.3 cm. In practical applications, noise is unavoidable, so the projection data have added some degree of Poisson noise, the level of which can be changed via the incident photons number.  
We set the value as \(10^{6}\) in this experiment. Five algorithms were used for comparison: FBP, OS-SART, TV minimization, PICCS, and a guided image filter reconstruction based on TV and prior image (TVPI-G) [49]. The initial image was set to zero for all iterative algorithms. The maximum number of iterations was set to 500, and the iteration stops when the algorithm converges. RMSE, SSIM [50], and PSNR are calculated to analyze the image quality quantitatively.
# Sensing Hydrogen Seeps in the Subsurface for Natural Hydrogen Exploration
## 1 Introduction
Electrochemical sensors are available with different concentration ranges and can typically detect hydrogen between 10-2000 ppm. Cross sensitivity to other gases is generally much lower than metal oxide sensors; however, some are strongly affected by CO and, to some extent, H\({}_{2}\)S, NO, and ethylene. Many of the commercial sensors incorporate a filter or membrane to reduce sensitivity to certain gases such as CO. Similar to metal oxide sensors, electrochemical sensors are also strongly affected by changes in temperature. However, most manufacturers do supply a transmitter board that will compensate for temperature, provided that the sensor is used within its specified humidity range. In many cases, electrochemical sensors have the disadvantage of using a liquid acid electrolyte that can absorb or lose water depending on the humidity level. Very high humidity's (above 90-95%) can even cause the electrolyte to swell and then leak. Some electrochemical sensors use a solid polymer electrolyte, which eliminates the issue of leakage at high humidity and extends the sensor lifetime to 5 years. This may be useful for autonomous long-term monitoring equipment. In addition to metal oxide and electrochemical sensors, several other technologies exist for hydrogen detection, which involve laser-based absorption spectroscopy [19] and Raman spectroscopy [20]. Numerous small handheld gas monitors have been designed to investigate gases emerging from landfill sites. More specifically, the commercial system from Geotech (GA5000) is one such device that has recently been deployed in various natural hydrogen research projects [10; 12]. The GA5000 gas analyser is relatively simple to operate and given that it is light to carry along with having the ability to provide long-term measurements, it is ideal for remote field studies. The aim of this study is to determine if a commercial-based hydrogen sensing technology is able to quantify hydrogen gas emanating from geological formations. Information on the exact hydrogen sensor used in the GA5000 is unavailable; however, it is stated by the manufacturer to be based on electrochemical detection [21]. The response of the GA5000 gas analyser was initially evaluated in the laboratory through calibration tests that comprised different gas mixtures. The second part of the investigation involved the application and field deployment of the gas analyser for hydrogen seep detection. The primary objective of this research is to investigate its sensitivity, selectivity, and suitability for directly determining hydrogen gas emissions relevant to source rock characterisation and hydrogen resource development.
## 3 Experimental Verification  
To validate the accuracy of the simulation results, a metasurface sample including \(10\times 10\) unit cells on an overall scale of \(100\times 100\) mm\({}^{2}\) is fabricated using the printed circuit board (PCB) methods as depicted in Figure 9a. Figure 9b shows that an Agilent N5230A network analyzer is operated in an anechoic chamber to avoid electromagnetic interference. Figure 9c,d display the measured amplitude reflection spectra of the cross-polarization, co-polarization, and PCR, respectively. Figure 9c shows that the measured \(r_{yy}\) is less than \(-10\) dB, while \(r_{xy}\) is greater than \(-1\) dB in the frequency range from 9.67 to 13.47 GHz. At the same time, two resonant frequencies are present in this frequency range, located at 11.16 and 12.41 GHz. Two more resonant frequencies at 16.49 GHz and 19.26 GHz are present in the second band from 14.96 to 20.45 GHz. The PCR value in these two bands is close to 1. The figures illustrate that the experimental results are highly consistent with the simulated results. Therefore, it is verified that the proposed metasurface can transform \(y\)-polarized light into \(x\)-polarized light with a high efficiency over a wide band. The following reasons can explain the small differences between the simulation results and experimental results. Firstly, the near-field interactions are neglected in the simulations. Secondly, in the simulation, periodic boundary conditions are adopted, meaning that the structures along the \(x\)- and \(y\)-directions are infinite, whereas the prepared samples have a finite size. Thirdly, a perfect normally incident electromagnetic wave is used in the simulation, but the device uses a quasi-plane wave which cannot be realized in the experiments.  
Figure 8: (**a**) PCRs with various values of \(\theta\). (**b**) The central frequencies and bandwidths of the two bands corresponding to different values of \(\theta\).  
Figure 9: _Cont._
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 3 Results
### Accuracy Assessment  
In this study, AERONET PWV data were used to evaluate the accuracy of DPC PWV data. The matching of AERONET PWV data and remote sensing PWV data was achieved according to the spatial and temporal matching method: The temporal mean of AERONET PWV data within half an hour of satellite transit was used to match the spatial mean of remote sensing PWV data in the range of 10 \(\times\) 10 km\({}^{2}\) centered on the ground station [38]. We used three metrics to evaluate the accuracy of remote sensing PWV data, including mean absolute error (MAE), mean bias (MB), relative error (RE), and the coefficient of determination (\(R^{2}\)). The MAE was used to characterize the absolute error of remote sensing PWV data, the RE was used to characterize the relative error of remote sensing PWV data, and the MB was used to characterize whether the remote sensing PWV data were overestimated or underestimated. The calculation formulas of MAE, MB, and RE are as follows:  
\[MAE=\sum_{i=1}^{N}\left|P_{i}-P_{i}^{\prime}\right|/N \tag{5}\]  
\[MB=\sum_{i=1}^{N}\left(P_{i}-P_{i}^{\prime}\right)/N \tag{6}\]  
\[RE=\sum_{i=1}^{N}\left|P_{i}-P_{i}^{\prime}\right|/\sum_{i=1}^{N}P_{i}^{\prime} \tag{7}\]  
where \(i\) represents a serial number, \(N\) represents the number of valid matching results between remote sensing PWV data and AERONET PWV data, \(P_{i}\) represents remote sensing PWV data, and \(P_{i}^{\prime}\) is the date corresponding to AERONET PWV data related to \(P_{i}\). \(R^{2}\) indicates the correlation between satellite PWV and AERONET PWV. To more comprehensively evaluate the accuracy of the DPC PWV data developed in this study, this study not only used ground-based data to verify the DPC PWV data but also validated the accuracy of MODIS PWV data released by NASA for comparison. The verification results of DPC PWV data and MODIS PWV data are shown in Figure 6. The MAE and RE of DPC PWV data were 0.32 cm and 0.30, respectively, and the MAE and RE of MODIS PWV data released by NASA were 0.23 cm and 0.26, respectively. The DPC and MODIS PWV results presented an overestimation situation with MB values of 0.29 and 0.21, respectively. Both DPC PWV and MODIS PWV had a high correlation with AERONET PWV, with \(R^{2}\) values of 0.93 and 0.96, respectively. In general, DPC PWV data and MODIS PWV data had similar accuracy, with relatively close absolute error and relative error values. This meant that the overall accuracy of DPC PWV data was close to that of MODIS PWV data released by NASA. Since MODIS PWV data are high-precision PWV data [23], it can be considered that the DPC PWV data developed in this study have high accuracy.  
Figure 5: Global mean PWV variation from 20 January 2022 to 29 January 2022.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 4 Experiments and Analysis of Results
### Evaluation Metrics  
Since a medical image segmentation approach is presented in this research, the model accuracy is assessed using certain image segmentation criteria. The model's segmentation efficacy is gauged using evaluation metrics such as the Dice Similarity Coefficient (DSC), Mean Intersection Over Union (mIoU), and Accuracy (Acc) to provide an impartial analysis of the methodology's effectiveness.  
The dice represents the similarity coefficient, which shows how similar the actual target area and the forecasted target area are. The cumulative similarity coefficients for all the test outcomes within the test set are represented as Dice, with its mathematical expression provided in Equation (21).  
\[Dice=\frac{2\times TP}{2\times TP+FP+FN} \tag{21}\]  
The intersection and merger ratio coefficient (IoU) is the average of two sets of predicted and actual values. It illustrates the proportion of overlap and amalgamation between two sets. The aggregate of the intersection and union coefficients for all the test outcomes in the test set is defined as mIoU. mIoU is represented by the formula shown in Equation (22):  
\[mIoU=\frac{TP}{TP+FP+FN} \tag{22}\]  
Acc is Accuracy: The ratio of properly identified pixels to total pixels is how accuracy is expressed. The efficacy of the classification process improves with an increased accuracy rate. The calculation for Acc is given by:\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{23}\]  
where TP, TN, FP, and FN signify the counts of correctly identified positives, correctly identified negatives, erroneously identified positives, and erroneously identified negatives within the feature's image pixel points, respectively.
# Ultrafast Modulation of Magnetization Dynamics in Ferromagnetic (Ga, Mn)As Thin Films
## 3 Results
The total magneto-optical signal is the combined response caused by the out-of-plane component of the magnetization, which is sensed by the polar Kerr effect (PKE), together with the in-plane component of the magnetization, which is sensed mainly by the magnetic birefringence for which the magnetization-induced refractive index change is sufficiently significant to contribute to the observed MO signal in (Ga,Mn)As film [36; 37; 38; 39]. To avoid complications arising from the superposition of PKE and MB in the gyromagnetic calculation, we analyze the behavior of \(M_{z}\) and \(M_{x\text{-}y}\) components separately. For details regarding the gyromagnetic theory and the simulation, we refer the reader to Reference [13]. In Figure 4, the LLG-2 MO calculation results show that the precession frequency is a linear function of the in-plane magnetic anisotropy field \(H_{xy}\). These results suggest that, as the pump intensity increases, the reduction of the in-plane magnetic anisotropy field caused either by laser heating or by increasing carrier density only contributes to a linear decrease of precession frequency. However, Figure 5 shows an increase of the out-of-plane magnetic anisotropy field \(H_{z}\) can lead to an exponential increase of the precession frequency. From the FMR results, we note that only the out-of-plane anisotropy field increases due to the non-thermal effect of increasing carrier concentration due to the increase of pump intensity. Thus, the decrease of magnetization precession frequency can be regarded as a slow-down caused by an increase of the out-of-plane magnetic anisotropy field that follows from the increase of carrier density due to optical excitation. In this case, we conclude that the nonlinear variation of the precession frequency shown in Figure 2 results mainly from the non-thermal contribution of photoexcited carriers and their effect on the out-of-plane magnetic anisotropy field. It should be noted that the increase of photoexcited carrier density causes an increase in the value of \(4\pi\)\(M_{eff}\) of about 380 Oe, but laser heating causes a decrease in \(4\pi\)\(M_{eff}\) of about 21 Oe/K. If we ignore the saturation for thermal effects, optical excitation at an intensity of 15 uJ/cm\({}^{2}\) leads to a local lattice temperature increase of 10 K based on earlier studies [12]. Figure 4: (Color online) Precession frequency simulated by LLG equation as a function the in-plane magnetic anisotropy field \(H_{x\text{-}y}\) for annealed compressively-strained (Ga,Mn)As film.
# Retrofitting of Steel Structures with CFRP: Literature Review and Research Needs
## 2 Bond Behavior between CFRP and Steel
### Bond-Slip Models  
The bond-slip model presents a formula that predicts the interfacial fracture energy based on the adhesive properties [59]. The effective bond length and bond strength can be achieved by the bond-slip relationship. This relationship can be experimentally determined from strain gauges along the bond length through bonded joint tests [62]. The bond-slip curve for linear adhesive is different from that of nonlinear adhesive. Linear adhesives have an approximately bi-linear shape. However, nonlinear adhesives exhibit an approximately trapezoidal shape, as presented in Table 4. As for the CFRP/concrete joints, the bond-slip curve always has a roughly bi-linear shape due to the brittle behavior of concrete [34]. Therefore, the bond-slip models for CFRP/steel joints are different and should be developed based on the adhesive behavior. A number of bond-slip models have been proposed for CFRP/steel bonded joints, as shown in Table 5.  
\begin{table}
\begin{tabular}{c  
The bond-slip models are categorized as bi-linear, tri-linear, and simplified models. Bi-linear and simplified models were proposed for FRP/concrete and FRP/steel interfaces. The parameters of the bond-slip models for FRP/concrete bonded joints are expressed based on the tensile strength of the concrete, as the concrete is commonly the weakest link of the joint [59]. However, for CFRP/steel joints, these parameters are defined based on the tensile strength of adhesive since the weakest link of the joint is usually adhesive. In addition, the tri-linear model was presented for only CFRP/steel interfaces and utilized for nonlinear adhesive. It should be noted that the area under the curve is representative of the interfacial fracture energy in these models [102; 103].
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 1 Introduction  
Medical image analysis plays a crucial role in clinical assessment. However, the success rate of the diagnosis depends upon the visual quality and the information present in medical images [1]. In real-world medical imaging, denoising [2, 3] or texture information processing [4, 5] is a necessary preprocessing step to improve the fused image's visual quality further.  
Nowadays, several imaging modalities are available to capture specific medical information of a given organ [6, 7, 8]. X-ray, MRI, CT, positron emission tomography (PET), and single-photon emission computed tomography (SPECT) of a human brain displayed in Figure 1 are crucial medical imaging modalities among them. For example, the magnetic resonance imaging (MRI) modality captures the anatomical information of the soft tissue. In contrast, computed tomography (CT) significantly provides hard tissue information such as bones structures and tumors [8]. Moreover, for clinical needs, the information provided by a single modality may not be sufficient, especially during the diagnosis of diseases [9]. The image fusion mechanism can effectively address this problem, enhancing the information by combining the complementary details provided by two or more modalities into a single image.