esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Validation of the Proposed Method  
#### Project Overview  
Excavation engineering for the Central Hospital Station of Xuzhou Rail Transit Line 2 is used to verify the feasibility of the proposed method. The station is a two-story underground station with an island platform. The main standard section is a double-layer, double span, single column box type frame structure. The cut-and-cover method is adopted, the total length of the excavation is 237.20 m, and the standard section is 21.7 m \(\times\) 16.59 m (width \(\times\) depth). Row piles and inner bracing are employed to maintain the stability in excavation. The commercial building on the west side of the station is the existing building closest to the excavation with a distance of 16.12 m from the main body of the station; thus, the settlement of this building caused by excavation will be used to verify the proposed method in this article.  
Figure 7 shows the relative position of the commercial building and the excavation. The monitoring items include the vertical displacement of the building, the ground vertical displacement, the deflection of the retaining structure, the vertical displacement at the top of the retaining structure, the horizontal displacement at the top of the retaining structure, the axial force of bracing, the vertical displacement of the column, and the groundwater table. The foundation pit is excavated from north to south, as indicated by the serial numbers of 1 to 9 in Figure 7.  
In this section, the monitoring data from 17 May 2017 to 16 June 2017 are selected for analysis. The area involved in this excavation process covers 1 to 9. Among this area, the excavation depths of 1 to 2 are 11.0 m-15.0 m and those of 3 to 9 are 7.0 m-9.0 m, the area from 7 to 9 is the route of the muck truck which is simply sloping without support and the excavation depth is shallow. During analysis, the excavation process is divided into 31 issues according to the daily reports; thus, we obtain 31 groups of original sample data. Figure 8 shows the history curve of building settlement, which indicates that the settlement values of different positions on the building are different, but the settlements increase with the advance of excavation.  
Figure 6: Implementation process of prediction model based on machine learning.
###### Abstract  
Free space optical (FSO) systems have become a reliable solution for modern communications networks, due to the high performance, availability, reliability and security they can provide. However, their characteristics depend strongly on the conditions of the atmosphere, which is the propagation path of the optical beam. In this work, this dependence is experimentally investigated through a terrestrial horizontal FSO link, which was installed a few meters above the sea. Thus, the procedure presented hereis an accurate empirical model for the estimation of the attenuation coefficient for an optical wireless link, as a function of the atmospheric temperature, the relative humidity, and the wind speed. Its accuracy is verified by comparing the estimated outcomes--obtained from the empirical model--versus the measured--experimental--ones. Such accurate empirical models can be used for designing high performance and reliability FSO links, as parts of the upcoming 5G/5G+ networks, for areas where the behavior of the atmospheric conditions and parameters are known.  
t pacs: 2
## 4 Numerical Experiments
### Experimental Examples and Settings  
Based on the varying proportions of joint and independent scheduling, we generated a total of fifteen instances. Instances 1 to 5 are small-scale examples containing only independent scheduling, instances 6 to 10 are small-scale examples with only joint scheduling, and instances 11 to 15 are large-scale examples with a high proportion of joint scheduling, closely matching real-world demands. Table 3 presents the specific composition of these instances.  
The parameter settings of heuristic algorithms have a significant impact on their performance. The main parameters involved in this paper's algorithm include the maximum number of iterations \(T_{max}\), the mutation probability \(P_{m}\), and the crossover probability \(P_{c}\). Based on preliminary experiments, we set \(T_{max},P_{m}\), and \(P_{c}\) to 1000, 0.01, and 0.8, respectively, to ensure that each experiment can be completed within 60 min. Since the experimental dataset comes from Chinese universities, we specifically consider the preference of Chinese university teachers to avoid consecutive classes. Therefore, in the penalty function \(V_{t}\), we set the weight parameter \(\omega_{1}\) to 1 to reflect the penalty for consecutive teaching and \(\omega_{2}\) to 0 to disregard the penalty for non-consecutive teaching.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & **Combined** & **Independent** & \multirow{2}{*}{**Teachers**} & \multirow{2}{*}{**Total Courses**} \\  & **Courses** & **Courses** & & \\ \hline Instance 1 & 0 & 6 & 5 & 6 \\ Instance 2 & 0 & 16 & 8 & 16 \\ Instance 3 & 0 & 26 & 13 & 26 \\ Instance 4 & 0 & 33 & 19 & 33 \\ Instance 5 & 0 & 57 & 38 & 57 \\ Instance 6 & 18 & 0 & 7 & 18 \\ Instance 7 & 42 & 0 &
## 4 Discussion
### Limitations and Future Improvements  
The data used in this study were obtained from satellite remote-sensed products, which typically suffer from a relatively low spatial resolution. Although we used eddy covariance data from flux towers to validate the GPP and ET results, owing to the limitations of field monitoring data, unavoidable uncertainties were observed in our validation process. In the future, further comparison and optimization should be carried out in combination with analyses of more monitoring data. Although the WUE trend in the study area contained some insignificant pixels, to fully analyze the contribution of the driving factors of vegetation coverage to the WUE trend in the STC, performing statistical analyses on all pixels may introduce certain errors. In future research, we will strive to pursue both the stability of the trend and integrity of the study area. In this study, both vegetation growth and elevated Ca were affected by human activity. However, the impacts of human activities differ and vary in different landscapes, and identifying a surrogate index that represents various human activities is difficult; therefore, regression analysis alone cannot determine the contribution of anthropogenic activities to changes in WUE. Accurately assessing the impact of human activities on ecosystems remains a focus of future research. Although this study identified the indirect effects of driving factors on WUE trends, an effective method of quantifying the indirect contribution has not been developed. Therefore, the contribution rate is still estimated based on the trends and direct effects of the variables, which may lead to an underestimation of the contribution of climate change. It is expected that the estimation method for indirect contributions will be improved in future research.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 3 Proposed Decentralized Self-Aligned Carrier Method  
In order to reduce the system configuration and reconfiguration time as well as to overcome some restrictions of conventional updating rules mentioned in Section 2, a digital method is proposed in this section for rapid and exact self-aligning carriers, and it can be applied to any number of carriers and to both phase-shifted and level-shifted carriers. Control algorithm, system topology, and evaluation of the proposed algorithm are discussed in detail, and simulation and experimental results are presented in the following sections.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
## 6 Results and Evaluation  
We compared our methods with four other state-of-the-art CNN-based methods: SingleHDR [10], HDRCNN [8], Expand-Net [9], FHDR [24], and HDRUNet [7]. We fed our test dataset to each network and obtained results. It can be seen from Table 5 that our proposed method performs quantitatively better than other methods.  
Figure 12 shows the visual comparison of result images between our method, HSVNet, and others. In most cases, HSVNet produces satisfying results which are closest to ground truth images than others. Missing details in both undersaturated and oversaturated areas, as well as the rest areas of input LDR images, are fairly well reconstructed by HSVNet. Especially in the color checkerboard in the fifth and the sixth row, most of colors are restored well even though the checkerboard in the input LDR image is partially undersaturated due to shadows or oversaturated due to sunlight. In addition to the hue of the checker board, saturation and brightness are well recovered by our method.  
Our approach has some limitations, as well. Our method might fail to infer missing detailed information, especially when the majority of the input image is oversaturated. When the input is extremely overexposed (see the first and second row of Figure 13), missing details in input LDR images are not reconstructed well with our method as with all other methods. However, our HSVNet predicts brightness and saturation better than other methods even in extremely oversaturated regions. Additionally, as the input image of the third and the fourth row in Figure 13 is largely overexposed, there is a huge gap between the result images and the ground truth image. There is also a limitation on the resolution of images. Our model is developed not for high resolution, but for relatively low resolution (\(256\times 256\) pixels). The performance might be debased when the size of the input image is large.  
Figure 12: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **L1** & **HDR-VDP-2.2** & **PU21-SSIM** & **PU21-PSNR** \\ \hline SingleHDR [10] & 0.1789 & 50.7160 & 0.7433 & 13.8755 \\ HDRCNN [8] & 0.1548 & 48.7974 & 0.6497 & 11.3025 \\ Expand-Net [9] & 0.1099 & 49.055 & 0.6731 & 11.1831 \\ FHDR [24] & 0.1031 & 50.1611 & 0.7328 & 14.5347 \\ HDRUNet [7] & 0.3400 & 47.5183 & 0.6596 & 9.3127 \\ HSVNet(ours) & **0.0672** & **52.5142** & **0.7761** & **16.6479** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The test results of our HSVNet and existing methods in four metrics. The value in bold indicates the best score for each metric.  
Figure 12: The result images generated by our method HSVNet and existing methods. The first and the second images are LDR input and the corresponding ground truth HDR images. The next images are the result images from existing methods, SingleHDR, HDRCNN, Expandnet, FHDR, and HDRUNet, respectively. The last image is our HSVNet result image.  
Figure 13: The result images in which our method did not successfully infer missing details.
## 4 Simulation Results and Analysis
### Effects of the Radial Clearance of Bearings  
When the other conditions are the same, the shaft orbits for different radial clearances are displayed in Figure 12. It can be noticed that the orbits of the rotor and right bearing get smaller, while the orbits of the left bearing get bigger when the radial clearance is 1 \(\upmu\)m, 3 \(\upmu\)m, and 5 \(\upmu\)m. When the radial clearance increases, the bearing forces become smaller and the bearing closer to the rotor is mainly affected by the rotor, while the bearing farther away from rotor is less affected by the rotor. As a consequence, the dynamic responses of the left bearing get larger due to the large dynamic responses of the rotor, while the dynamic responses of the right bearing get smaller. It can be inferred that the effects of radial clearance on the left and right bearing are different and the effects on the bearing closer to the rotor are greater than the other side. It also can be seen that the orbits are the same when the radial clearance is 5 \(\upmu\)m and 10 \(\upmu\)m. The reason for that is that the bearing forces cannot be formed when the radial clearance reaches a certain value and the effects of the bearing forces on the system do not exist under this situation.  
From the time history of the rotor dynamic responses in Figure 13, we can know that the effects on the dynamic displacement responses and angle responses are similar. The spectra of dynamic responses for different radial clearances are displayed in Figure 14 and the combination of VC frequency and rotating frequency can be observed in Figure 14b,c. The combined frequencies \(f_{\rm{vc}}+3\Omega\), \(2f_{\rm{vc}}+3\Omega\), \(2f_{\rm{vc}}+5\Omega\) and \(3f_{\rm{vc}}+5\Omega\) indicate that the coupling effects of bearing forces, unbalanced mass force and UMP exist when the radial clearance is 1 \(\upmu\)m and 3 \(\upmu\)m, while the coupling effects disappear when the radial clearance is 5 \(\upmu\)m and 10 \(\upmu\)m. According to the above analysis, the bearing forces cannot be formed when the radial clearance reaches a certain value, and then the coupling effects disappear. It can also be inferred that the coupling effects on the angle responses are greater than the displacement responses and the coupling effects on the bearings are greater since the combined frequencies are more obvious. The combined frequencies are more obvious in the Figure 14c and this means that the coupling effects on the bearings are greater than the rotor.  
Figure 11: The spectra of dynamic responses for different rotor offsets. (**a**) The spectra of \(x\); (**b**) The spectra of \(\theta_{x}\); (**c**) The spectra of \(x_{A}\).  
Figure 14: _Cont._  
Figure 12: Rotor shaft orbits and bearing shaft orbits for different radial clearances. (**a**) Rotor shaft orbits; (**b**) Left-bearing shaft orbits; (**c**) Right-bearing shaft orbits.  
Figure 13: Time history of the dynamic responses for different radial clearances. (**a**) Time history of \(x\); (**b**) Time history of \(\theta_{x}\).
## 4 Experimental  
### Chemistry  
#### 4.1.1 Materials and Methods  
Chemicals were obtained from commercial sources and were used without further purification. All NMR experiments were performed at 25 \({}^{\circ}\)C on Bruker Avance II HD 400 MHz spectrometer. \({}^{1}\)H NMR data were internally referenced to CD\({}_{3}\)OD (3.31 ppm), DMSO-D\({}_{6}\) (2.50 ppm) or TMS (0.00 ppm). The IR (KBr pellets) spectra were recorded on a Thermo Scientific Nicolet 380 FT-IR spectrometer. The mass spectra were recorded on a Shimadzu single quadrupole LCMS 2010 EV mass spectrometer. Melting points were determined on an X-4 melting point apparatus with a microscope and were uncorrected. Elemental analysis was performed with Vario El Cube CHNS, Elementar. Analytical TLC was performed on silica gel Merck 60 F254 plates (0.25 mm) with UV light visualization (mobile phase: CHCl\({}_{3}\)/MeOH 0.9:0.1 v/v). 4,6-Dimethylisoxazole1[3,4-\(b\)]pyridin-3(1\(H\))-one (**1**) and isoxazole1[3,4-\(b\)]quinolin-3(1\(H\))-one (**4**) were obtained according to previously described procedures [3,4,20].  
1.2 Synthesis of 1-(hydroxymethyl)-4,6-dimethylisoxazole1[3,4-\(b\)]pyridin-3(1\(H\))-one (**2**) and 1-(hydroxymethyl)isoxazole1[3,4-\(b\)]quinolin-3(1\(H\))-one (**5**)  
4,6-Dimethylisoxazole1[3,4-\(b\)]pyridin-3(1\(H\))-one (**1**) or isoxazole1[3,4-\(b\)]quinolin-3(1\(H\))-one (**4**) (1.9 mmol, 312 or 354 mg, respectively) was dissolved in methanol (20 mL), then 35% water solution of formaldehyde (0.60 mL, 7.6 mmol) was added and the resulting mixture was stirred for 4 h at room temperature. The precipitated solid was filtered off, washed with diethyl ether (3 \(\times\) 5 mL) and recrystallized from methanol prior to characterization.  
1-(Hydroymethyl)-4,6-dimethylisoxazole1[3,4-\(b\)]pyridin-3(1\(H\))-one (**2**). Yield: 80% (0.296 g); m.p. 201-202 \({}^{\circ}\)C; \({}^{1}\)H NMR (300 MHz, CD\({}_{3}\)OD): \(\delta\) = 2.58 (s, 3H, CH\({}_{3}\)), 2.60 (s, 3H, CH\({}_{3}\)), 5.29 (s, 2H, CH\({}_{2}\)), 7.04 (s, 1H, CH); IR (KBr): 3383, 3003, 2960, 2842, 1748, 1616, 1590, 1445, 1371, 1177, 1061, 1034, 998, 883, 864, 805, 706, 656, 638 cm\({}^{-1}\); MS (ESI) m/z: 195 [M+1]\({}^{+}\).  
1-(Hydroymethyl)isoxazole1[3,4-\(b\)]quinolin-3(1\(H\))-one (**5**). Yield: 54% (0.222 g); m.p. 237-242 \({}^{\circ}\)C; \({}^{1}\)H NMR (300 MHz, DMSO-D\({}_{6}\)): \(\delta\) = 4.79 (bs, 1H, OH), 5.31 (d, 2H, CH\({}_{2}\)), 7.65 (t, J = 8.1 Hz, 1H, CH), 7.79 (t, J = 8.1 Hz, 1H, CH), 8.03 (d, J = 8.1 Hz, 1H, CH), 8.23 (d, J = 8,1 Hz, 1H, CH), 9.20 (s, 1H, CH); IR (KBr): 3302, 2921, 1771, 1627, 1506, 1427, 1356, 1171, 1125, 1090, 1057, 1028, 989, 934, 891, 766 cm\({}^{-1}\); MS (ESI) m/z: 217 [M+1]\({}^{+}\).
## 1 Introduction
The sensitivity of the CE system is more than two orders of magnitude better than the sensitivity of the CMO system. It should be noted that the degree of the electronic integration of our CE system is much higher than the integration of our CMO system, which is built with discrete electronics. Even if we try to integrate the electronics for the CMO system, it is very difficult to integrate the laser and to reduce the optical path without losing further sensitivity. The article is organized as follows: Section 2 describes the principles of the operation of the chemo-mechanical sensor with optical detection, the structure of the cantilever with chemical modification of the sensing surface, the optical detection principle, and an estimation of the Signal to Noise (_S/N_) ratio supported by our measurements. Section 3 examines the capacitive sensor with electronic detection, and describes the principle of operation, the capacitive sensor design, the fabrication and modification, the architecture of the electronic detection system together with the estimates of achievable _S/N_ ratio or detection sensitivity, the implementation of a complete demonstrator, and our measurements that support the detection sensitivity estimates. Section 4 describes vapour generator, used in testing the sensors' response, while Section 5 presents the sensitivity comparison of both detection systems. Section 6 concludes the article.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & \(\mathbf{\dot{Ex}_{mass}}\) & \(\mathbf{\dot{Ex}_{in}}\) & \(\mathbf{\dot{Ex}_{out}}\) & \(\mathbf{\dot{Ex}_{loss}}\) & \(\mathbf{\dot{Ex}_{waste}}\) \\ \hline Gasification & 604.23 & 4207.65 & 127.45 & 4080.20 & 639.76 \\ Pyrolysis & 43.21 & 54.85 & 6.09 & 48.75 & 18.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Exergy analysis results for gasification and pyrolysis processes. The energy usage is much higher in gasification than in pyrolysis. This result somewhat explains the outcomes of the exergy analysis. Another important parameter for evaluating sustainability was the CO2 emissions of each process. The gasification process generates 220.41 kg of CO2/kg of product. In the case of pyrolysis, this quantity corresponds to 2.09 CO2/kg. This outcome is also associated with emissions coming from energy generation, which is more significant for the gasification process. This relation might indicate a better environmental profile of the pyrolysis process. Another evaluated parameter is the energy efficiency (or net energy). This parameter measures energy balance, considering the energy consumed in the system and the energy content in the product. Ideally, it is expected that this value would be at least equal to the unity, reflecting an energy neutral design. High energy-efficient processes would show an energy efficiency >1
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 2 Methodology  
In this work, the m-m technique is used to perform sonic imaging by processing a single synthetic aperture radar (SAR) image in the single-look-complex (SLC) configuration. The technique involves the m-m estimation belonging to the Vesuvius volcano and is generated by the intensive underground seismic activity that reflects superficial vibrations. The m-m estimation is carried out through MCA, performed in the Doppler direction. Multiple Doppler sub-apertures, SAR images with lower azimuth resolution, are generated to estimate the vibrational trend of some pixels of interest. The infra-chromatic displacement is calculated through the pixel-tracking technique [30; 31], using high-performance sub-pixel coregistration [28; 29]. Vibrations observed along the tomographic view direction, embedded into the multi-chromatic Doppler diversity, are focused along the height (or depth) dimension, and develop high-resolution tomographic underground imaging.  
The SAR synthesizes the electromagnetic image through a \"side looking\" acquisition, according to the observation geometry shown in Figure 1, where:  
* \(r\) is the zero-Doppler distance (constant);
* \(R\) is the slant-range;
* \(R_{0}\) is the reference range at \(t=0\);
* \(d_{a}\) is the physical antenna aperture length;
* \(V\) is the platform velocity;
* \(d\) is the distance between two range acquisitions;
* \(G_{sa}\) is the total synthetic aperture length;
* \(t\) is the acquisition time variable;
* \(T\) is the observation duration;
* \(t=0\) and \(t=T\) are the start and stop time acquisition, respectively;* \(L=\frac{\Delta r}{d_{a}}\) is the azimuth electromagnetic footprint width;
* \(\theta\) is the incidence angle of the electromagnetic radiation pattern.  
All the above parameters are related to the staring-spotlight SAR acquisition that is adopted in this work. Considering what is formalized in Appendix A, the MCA technique, based on Doppler sub-apertures, is used to estimate the following master-slave pixel shift complex parameters (A12):  
* \(\epsilon_{r_{1}}=\frac{\
u_{T}}{V}\) (due to range velocity);
* \(\epsilon_{r_{2}}=\frac{a_{r}R_{0}}{V^{2}}\) (due to range acceleration);
* \(\epsilon_{c_{1}}=\frac{\
u_{T}}{V}\) (due to azimuth velocity).  
Thus, the above terms modify the received signal, as shown in [32], and should be taken into account in Equation (A6).
# Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools
## 4 Materials and Methods
### Differences between CRNN and SVTR Models  
Both CRNN and SVTR are machine learning-based OCR engine architectures proposed to increase the accuracy and efficiency of OCR systems. Here are a few significant distinctions between the two models:  
1. Inputs: CRNNs are designed to operate with image inputs, but SVTRs can process pictures, videos, and live video streams. CRNNs use a combination of CNNs and RNNs to detect text in images, whereas SVTRs use a combination of deep learning and conventional image processing techniques.
2. Text recognition: CRNNs are adept at recognizing texts of various font styles, sizes, and orientations, as well as multiple lines of text in an image, and can perform well on scene text recognition, whereas SVTRs are specifically designed to handle scene text in images, which can vary significantly in terms of lighting, orientation, and skew.  
Attention mechanism: CRNNs lack an attention mechanism, but SVTRs possess one. This attention mechanism enables the network to concentrate on an image's significant parts and increases the model's noise and distortion resistance. Because of the attention mechanism they employ, SVTRs are more resistant to noise and distortion. CRNNs are susceptible to noise and distortion.
4. Training and fine-tuning: Both CRNN and SVTR architectures are adaptive and can be fine-tuned with more data to increase their accuracy. However, CRNN models require substantial training data to produce accurate results. SVTR models, in contrast, can also learn from fewer data. CRNNs are quicker than conventional OCR engines because they can run on parallel architectures such as GPUs. In contrast, SVTRs can run on various platforms, including smartphones and embedded systems.