esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3
## 2 Proposed Methodology
### Mask R-CNN  
The implemented Mask R-CNN extends the well-known Faster R-CNN, which in turn is an extension of the Region-based CNN but introduces an attention mechanism through a region proposal network (RPN). This latter suggests candidate bounding boxes pertaining to the object of interest. Afterward, features are extracted from each candidate bounding box using RoIPool, followed by classification and bounding box regression [42].  
In Mask R-CNN, the RPN part is retained, allowing potential object bounding boxes to be detected [43]. In the second stage, classification and bounding box regression are supplemented with a binary mask for each region of interest. Thus, for training purposes, the network follows a global loss, which sums up the classification loss, the bounding box loss and the mask (segmentation) loss:  
\[L=L_{class}+L_{bbox}+L_{mask} \tag{1}\]The first two terms of the total loss are the same as in Faster R-CNN [21]. Therefore, Mask R-CNN can be regarded as a Faster R-CNN reinforced with an instance segmentation feature. In brief, Mask R-CNN incorporates the following key steps:  
Feature maps are drawn from the image presented at the input with a CNN model (we selected ResNet-50).  
The region proposal network is adopted to generate multiple regions of interest by incorporating a CNN and a binary classifier. In return, object scores are obtained. In order to eliminate the anchor boxes that likely belong to the background, intersection over union mechanism is invoked, where only the anchor boxes with an intersection above 0.5 are considered. In order to tackle the bottleneck of picking up multiple bounding boxes over the threshold of 0.5, non-max suppression is introduced by selecting the bounding box with the highest intersection over union and discarding the remaining candidate boxes.  
A region of interest aligns the network and then produces multiple boxes of evidence of the same object and warps them into a fixed dimension.  
Afterward, the warped features are fed into fully connected layers to carry out the classification using a Softmax. The boundary box is further refined with the regression model.  
Further, for instance segmentation, the warped features are also fed into a mask classifier that consists of two CNNs to produce a mask for each region of interest. The pipeline of Mask R-CNN is depicted in Figure 1.
## 4 Experiments
### Experimental Setup Details
#### 4.1.3 Evaluation Metrics  
For quantitative evaluation, we adopt four full-reference metrics--PSNR [46], SSIM [46], LPIPS [47], and MSE [46]--and three no-reference metrics--NIQE [48], BRISQUE [48], and PIQE [49].  
PSNR and SSIM measure the fidelity of the restored/generated images against reference images, with higher values indicating better quality (PSNR is measured in dB, typically ranging from 10 to 50 in low-light enhancement tasks; SSIM ranges from 0 to 1). LPIPS evaluates perceptual similarity using deep features, with lower values indicating better perceptual quality (LPIPS ranges from 0 to 1); meanwhile, MSE measures pixel-level differences, where lower values are preferable (MSE \(\geq\) 0, and with pixel intensities in the range [0, 255], its maximum possible value is 65,025). The MSE formula is as follows:  
\[\text{MSE}=\frac{1}{H\times W\times C}\sum_{i=1}^{H}\sum_{j=1}^{W}\sum_{k=1}^ {C}\Bigl{(}I_{gt}(i,j,k)-I_{pred}(i,j,k)\Bigr{)}^{2} \tag{25}\]  
where \(H\), W, and \(C\) denote the height, width, and number of channels of the image, respectively, and \(I_{gt}\) and \(I_{pred}\) represent the pixel values of the ground truth and predicted images, respectively.  
Figure 5: The example of paired NRGD.  
Figure 6: The example of unpaired NRGD.  
For no-reference assessment, NIQE evaluates naturalness and is sensitive to noise and color distortion, BRISQUE detects blur and noise based on local statistics, and PIQE assesses perceptual consistency in detecting artifacts such as halos. These metrics use a 0-100 scale, with lower scores indicating better quality, and are widely used for evaluating low-light image enhancement methods, with PIQE providing stable assessments in extremely low-light scenarios.
###### Abstract  
The dynamic star simulator is a commonly used ground-test calibration device for star sensors. For the problems of slow calculation speed, low integration, and high power consumption in the traditional star chart simulation method, this paper designs a FPGA-based star chart display algorithm for a dynamic star simulator. The design adopts the USB 2.0 protocol to obtain the attitude data, uses the SDRAM to cache the attitude data and video stream, extracts the effective navigation star points by searching the starry sky equidistant right ascension and declination partitions, and realizes the pipelined displaying of the star map by using the parallel computing capability of the FPGA. Test results show that under the conditions of chart field of view of \(\Phi 20^{\circ}\) and simulated magnitude of \(2.0\sim\)6.0 Mv, the longest time for calculating a chart is 72 us under the clock of 148.5 MHz, which effectively improves the chart display speed of the dynamic star simulator. The FPGA-based star map display algorithm gets rid of the dependence of the existing algorithm on the computer, reduces the volume and power consumption of the dynamic star simulator, and realizes the miniaturization and portable demand of the dynamic star simulator.  
star simulator; FPGA; dynamic star map; star partitioning; parallel computing Article
###### Abstract  
The prediction of the unit grouting quantity is one of the key points in the design and construction of karst curtain grouting. Because of the concealment and complexity of the karst curtain grouting project, there is no reliable solution to this problem. In this paper, based on the calculation method of water permeability in water-pressure test in grouting engineering, Poiseuille flow equation in fluid mechanics, and cubic law, the relationship between the unit grouting quantity and the water permeability of rock strata in karst curtain grouting is studied. It is found that there is a nonlinear positive correlation between the unit grouting quantity and the water permeability of rock strata in karst curtain grouting, which is in the form of power function. At the same time, the comprehensive coefficient of karst curtain grouting (\(K\)) is introduced to describe the quantitative relationship between the unit grouting quantity and the water permeability of rock strata. \(K\) is comprehensively characterized by three controlling factors, namely, the characteristics of slurry fluid, the characteristics of grouted rock strata, and the technical factors of grouting in karst curtain grouting. Based on this theoretical relationship, a method for predicting the unit grouting quantity in karst curtain grouting by the water permeability of rock strata is proposed in this paper. Finally, based on the typical example of karst curtain grouting project, through the field grouting test and nonlinear function fitting, the comprehensive coefficient of karst curtain grouting (\(K\)) is 24.37 in the area of strong karst development, and it is 16.51 in the area of weak karst development. The proposed method is applied to the prediction of the unit grouting quantity in the main project of karst curtain grouting, and the results verify the rationality and applicability of the method. This study has a certain reference value and guiding role in the design, construction, and theoretical research of karst curtain grouting.  
karst curtain grouting; the unit grouting quantity; the water permeability of rock strata; prediction method; quantitative relationship +
Footnote †: journal: Applied Sciences
# Directional Core Drilling as an Approach to Reduce Uncertainty in Tunneling Construction
## 3 Methodology for the Uncertainty Economic Valuation
### Costs Related to the TBM Excavation
#### 3.3.2 Support and Lining  
The volume of concrete required in a lining segment is defined by Equation (12), and Equation (13) determines the concrete price. On the other hand, the quantity of rebar required and its associated cost can be determined by employing Equations (14) and (15), respectively.  
\[\mathrm{V_{h}}=\frac{\pi}{4}\cdot\left(\mathrm{D_{e}^{2}-D_{i}^{2}}\right) \cdot\left(\mathrm{L_{f}-L_{o}}\right) \tag{12}\]  
\[\mathrm{C_{h}}=\mathrm{P_{h}}\cdot\mathrm{V_{h}} \tag{13}\]  
Here, \(\mathrm{V_{h}}\) is the concrete volume (m\({}^{3}\)), \(\mathrm{D_{e}}\) is the external diameter (m), \(\mathrm{D_{i}}\) is the inner diameter (m), \(\mathrm{L_{f}}\) and \(\mathrm{L_{o}}\) refer to the stretch of the tunnel analyzed (m), \(\mathrm{C_{h}}\) is the total concrete cost (EUR), and \(\mathrm{P_{h}}\) is the concrete cost (EUR/m\({}^{3}\)). As references, we took HA-70 with a cost of EUR 219.93 per m\({}^{3}\) and HA-45 with a cost of EUR 146.49 per m\({}^{3}\).  
\[\mathrm{M_{a}}=\mathrm{m_{a}}\cdot\mathrm{V_{h}} \tag{14}\]  
\[\mathrm{C_{a}}=\mathrm{p_{a}}\cdot\mathrm{m_{a}} \tag{15}\]  
Here, \(\mathrm{M_{a}}\) is the total mass of the steel used in the lining (kg), \(\mathrm{m_{a}}\) is the mass of the steel per cubic meter of concrete (kg/m\({}^{3}\)), \(\mathrm{C_{a}}\) is the cost of the rebar (EUR), and \(\mathrm{p_{a}}\) is the unit cost of the rebar (EUR/kg). We considered EUR 0.87 per kg as a reference value.
## 2 System and Signal Model
### Mode of Relay Selection  
In this work, AWACS selects one relay node among UAVs that can offer optimal channel quality based on the accurate CSI feedback. Four different relay selection modes are considered: Max-Select relaying, Source-Relay-Select relaying, Relay-Destination-Select relaying, and Dual-Distributed-Select relaying.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 4 Analysis of Discharge Risk Coefficients of Overhead Conductors Under Different Icing Conditions
### Impact of Strong Winds on Safety Distances for Conductors  
Strong winds can induce oscillations in lead wires, potentially leading to contact or collision between them. The wind load on the wires causes these oscillations, influenced by the wind speed, direction, and the wire's shape, size, and suspension. Wind speeds of 10,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pitch/m** & **Maximum Sag of the** & **The Maximum Sag of** & **Wire De-icing** & **Minimum Spacing Between** & **Phase-to-Ground** \\  & **Conductor Wire/m** & **the Wiretin** & **Jump Height/m** & **Ground and Conductor Wires/m** & **Discharge Risk Factor** \\ \hline
200 & 5.475 & 5.176 & 6.456 & 6.557 & 0.305 \\
300 & 10.506 & 10.141 & 12.020 & 2.093 & 0.956 \\
400 & 14.188 & 16.646 & 18.815 & 2.000 & 1.000 \\
500 & 21.992 & 24.760 & 26.429 & 2.000 & 1.000 \\
600 & 29.156 & 34.551 & 33.264 & 2.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The influence of different gear distances on the safety distance of the ground conductor.  
Figure 9: Risk range of de-icing and bounce of wires with different pitches.  
As wind speed increases, both the sag of the earth wire and conductor decreases. With wind perpendicular to the conductor axis, the horizontal distance between the conductor and the ground line diminishes. At 25 m/s wind speed, this distance reaches a minimum of 1.336 m. Increasing wind speed from 5 to 25 m/s reduces the maximum longitudinal displacement of the de-iced conductor by approximately 3 m, while the maximum transverse displacement increases by 0.675-12.816 m.  
Consequently, the vertical separation between the conductor and the ground line decreases, reducing the sag. Since the conductor and ground are on different horizontal planes, analyzing only the midpoint of the conductor is inadequate for assessing the minimum spacing between them. A comprehensive analysis is required due to the spatial nature of de-icing bounce. At wind speeds of 5 and 10 m/s, the minimum distance between the conductor and the ground line falls below the safety threshold, with phase-to-ground discharge risk coefficients exceeding 1 (ranging from 1.170 to 1.323), thereby increasing the risk of discharge. At 5 m/s, discharge occurs 56% of the time, rising to 59% when the wind speed is 10 m/s. Lower wind speed results in smaller horizontal displacements, heightening the risk of discharge during de-icing. Excessive wind speeds, however, cause significant horizontal displacement of the earth wire relative to the conductor, paradoxically increasing the conductor-to-ground distance and reducing discharge risk.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 6 Challenges and Suggestions for Future Development of CCUS in China
For example, less than critical pressure is required for gaseous CO\({}_{2}\) transport; temperature control is required for liquid CO\({}_{2}\) transport; both temperature control and fixed pressure range are required for dense-phase CO\({}_{2}\) transport; and above critical temperature and pressure are required for supercritical state CO\({}_{2}\) transport. Therefore, it is necessary to comprehensively consider these influencing factors in the pipeline design. The potential improvements in future research should focus on optimizing the pipeline design, developing precise design specifications, and using suitable construction materials for pipelines, so as to achieve the cost-effective and safe transport of CO\({}_{2}\) in the pipeline. The potential for CO\({}_{2}\) leakages is another challenge for CO\({}_{2}\) transport and subsequent utilization and storage in China. Generally, CO\({}_{2}\) leakages are less likely to occur in tank-based transport, because tanker trucks are equipped with reliable safety features to minimize the risk of spills, but CO\({}_{2}\) leakages may still occur due to the accident of the truck or the failure of the equipment. CO\({}_{2}\) leakages during transport by pipeline are usually caused by CO\({}_{2}\) corrosion [204, 205] or mechanical damage to pipelines [206, 207]. Therefore, it is essential to employ high-quality equipment and advanced materials [208, 209] for the transport system to avoid or minimize the potential risk of CO\({}_{2}\) leakages during transport. Meanwhile, it is necessary to implement monitoring and leakage detection systems [210, 211] to rapidly identify and address any leakages. The potential reasons for leakages in CO\({}_{2}\) utilization and storage may include unstable underground reservoir conditions and inappropriate injection equipment. Therefore, a comprehensive evaluation of geological conditions is required in the site selection for CO\({}_{2}\) utilization and storage, and high-quality corrosion-resistant injection equipment is necessary. In addition, the implementation of reliable monitoring and leakage detection systems is also crucial. However, immature technologies of monitoring, leakage prevention and control, and corrosion prevention for post-CO\({}_{2}\) injection are the main problems [50] faced by existing CO\({}_{2}\)-EOR projects in China. Furthermore, the challenge of accurate monitoring is even more prominent in China's CO\({}_{2}\)-ECBM projects since accurate monitoring of the movement of one gas is more difficult in a gas-gas system than in a gas-liquid system. Therefore, future research on CO\({}_{2}\) utilization and storage in China should focus on improving accurate monitoring and leakage detection technologies, leakage prevention and control technology, and anti-corrosion technology, to achieve effective utilization and safe storage of CO\({}_{2}\).
## Appendix E Integrals  
\[\int_{0}^{2\pi}\frac{d\vartheta}{a+b\cos\vartheta}=\frac{2\pi}{\sqrt{a^{2}-b^{2} }}=\frac{2\pi}{|c^{2}-1|},\ a=c^{2}+1,\ b=2c \tag{100}\]  
\[\int_{0}^{2\pi}d\vartheta\frac{e^{\mathrm{i}\vartheta}}{a+b\cos\vartheta}= \int_{0}^{2\pi}d\vartheta\frac{\cos\vartheta}{a+b\cos\vartheta}=\frac{-b}{a+ \sqrt{a^{2}-b^{2}}}\frac{2\pi}{\sqrt{a^{2}-b^{2}}} \tag{101}\]  
Let us take the following integral  
\[I(a)=\int_{0}^{\pi}\frac{d\vartheta}{a^{2}+1-2a\cos\vartheta}=\frac{\pi}{|a^{ 2}-1|}=\left\{\begin{array}{cc}\pi/a^{2},&a^{2}\gg 1\\ \pi,&a^{2}\ll 1\end{array}\right.. \tag{102}\]  
Obviously, \(I(1)=\infty\), but  
\[I(1)=\frac{1}{2}\int_{0}^{\pi}\frac{d\vartheta}{1-\cos\vartheta} =\frac{1}{4}\int_{0}^{\pi}\frac{d\vartheta}{\sin^{2}\frac{\vartheta}{2}}= \frac{1}{2}\int_{0}^{1}\frac{dx}{(1-x^{2})^{3/2}}\] \[=\frac{1}{4}\int_{0}^{1}\frac{dy}{y^{1/2}(1-y)^{3/2}}=B(1/2,-1/2 )=\frac{1}{4}\frac{\Gamma(1/2)\Gamma(-1/2)}{\Gamma(0)}=0,?!\] \[B(\alpha,\beta)=\int_{0}^{1}dxx^{\alpha-1}(1-x)^{\beta-1}= \frac{\Gamma(\alpha)\Gamma(\beta)}{\Gamma(\alpha+\beta)},Real\ \ \alpha,\beta>0. \tag{103}\]  
In our case \(a=\exp(r/R)>1\). Let us take corresponding integral,  
\[I=\frac{1}{a}\int\frac{d\vartheta}{b+2\cos\vartheta}=\frac{1}{ \mathrm{i}a}\int\frac{dz}{z^{2}+bz+1}\] \[=I(z,a)=\frac{1}{\mathrm{i}a(a-1/a)}\ln\frac{z+a}{z+1/a},\] \[I(a)=I(-1,a)-I(1,a)=\frac{1}{\mathrm{i}a(a-1/a)}\ln\frac{(-1+a) (1+1/a)}{(-1+1/a)(1+a)}=\frac{\pi}{a^{2}-1},\] \[b=a+1/a,\ z=e^{\mathrm{i}\vartheta}. \tag{104}\]  
We may calculate the same integral by residue formula  
\[\oint\frac{dz}{(z+a)(z+1/a)}=2\pi\mathrm{i}\left\{\begin{array}{cc}1/(-a+1/ a),&|a|<1\\ 1/(-1/a+a),&|a|>1.\end{array}\right. \tag{105}\]  
\[I(a)=\frac{1}{\mathrm{i}a}2\pi\mathrm{i}\left\{\begin{array}{cc}1/(-a+1/a), &|a|<1\\ 1/(-1/a+a),&|a|>1.\end{array}\right\}=\frac{2\pi}{|a^{2}-1|}. \tag{106}\]  
Now,  
\[I=\int_{0}^{2\pi}\frac{d\vartheta}{a^{2}+1-2a\cos\vartheta}=I(a) +I(-a)=\frac{2\pi}{|a^{2}-1|}=\frac{\pi\exp(-r/R)}{\sinh(r/R)},\] \[\frac{1}{2\pi}\int_{0}^{2\pi}\frac{d\vartheta}{\cosh(r/R)-\cos \vartheta}=\frac{2a}{a^{2}-1}=\frac{1}{\sinh(r/R)},\ a=\exp(r/R). \tag{107}\]
# Parallel Ghost Imaging with Extra Large Field of View and High Pixel Resolution
## 3 Discussion on Speckle Patterns, Sampling Rate, and Reconstruction of Extra-Large Speckle Space
(**c**) Statistical histogram of the correlation between the speckle patterns. (**d**) Histogram of the modulation capability of the mask. (**e**) Fourier ring correlation analysis of the mask resolution. Parallel ghost imaging reconstruction results with sampling rates of (**f**) 15%, (**g**) 20%, (**h**) 30%, (**i**) 50%, (**j**) 80%, (**k**) 100%, (**l**) 200%, and (**m**) 300%. Finally, we discuss several methods for reconstructing an extra-large speckle space. In fact, due to the sub-micron precision required, reconstructing an extra-large speckle space is also a significant experimental challenge. We propose three approaches to achieve this:  
1. The first method is the most efficient: Directly move the sandpaper to seamlessly stitch together a complete sandpaper plane, then simulate the collection by virtually moving a 14,000 \(\times\) 10,000 pixel window within it. This approach requires only a few dozen sandpaper patterns to be captured and takes only a few seconds, theoretically making it feasible. However, in practical experiments, due to the presence of background stripes in the captured images, simulating the collection was not feasible. Moreover, in such high-precision experiments, we found that tiny gaps between the seven layers of the mask, as well as slight tilting of the entire sandpaper in the U/V directions, caused the speckles to change in size as they moved, with nearby speckles appearing larger and distant ones smaller. This approach requires high-precision alignment of the mask-carrying stage, which is difficult to achieve in time-constrained synchrotron radiation experiments. 2. The second method is to move the sandpaper to construct a large mask space. For each measurement, 400 images are captured, repeated for 35 sets. This method is time-consuming but feasible. However, the precision of the five-axis stage carrying the sandpaper cannot be guaranteed, making it difficult to manually register the speckles in both the reference and object arms. For this reason, we used the third method in the experiment. 3. To ensure absolute precision, we ensured that the entire optical platform, OT2, which holds two detectors, was perfectly calibrated. This ensures that the 6.5 um and 9 um detectors, located at a significant distance from each other, can switch without error. The movement of the sandpaper can be equivalently represented by the reverse relative movement of the detectors, and the precision of the detectors' movement is the highest. Our calculations show that even on the scale of over ten thousand pixels, the skew is only a few pixels, which improves registration and reconstruction results. Therefore, we chose to construct the extra-large speckle space by moving the detectors. In fact, methods (2) and (3) are equivalent in principle, both being highly time-consuming. We will continue to refine and improve method (1) in the hopes of compressing the several hours of reference arm data collection into just a few minutes.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
###### Abstract  
Eight-band one-shot MSFA (multispectral filter array) cameras are innovative technologies used to capture multispectral images by capturing multiple spectral bands simultaneously. They thus make it possible to collect detailed information on the spectral properties of the observed scenes economically. These cameras are widely used for object detection, material analysis, and agronomy. The evolution of one-shot MSFA cameras from 8 to 32 bands makes obtaining much more detailed spectral data possible, which is crucial for applications requiring delicate and precise analysis of the spectral properties of the observed scenes. Our study aims to develop models based on deep learning to estimate the spectral response of this type of camera and provide images close to the spectral properties of objects. First, we prepare our experiment data by projecting them to reflect the characteristics of our camera. Next, we harness the power of deep super-resolution neural networks, such as very deep super-resolution (VDSR), Laplacian pyramid super-resolution networks (LapSRN), and deeply recursive convolutional networks (DRCN), which we adapt to approximate the spectral response. These models learn the complex relationship between 8-band multispectral data from the camera and 31-band multispectral data from the multi-object database, enabling accurate and efficient conversion. Finally, we evaluate the images' quality using metrics such as loss function, PSNR, and SSIM. The model evaluation revealed that DRCN outperforms others in crucial performance. DRCN achieved the lowest loss with 0.0047 and stood out in image quality metrics, with a PSNR of 25,5059, SSIM of 0.8355, and SAM of 0.13215, indicating better preservation of details and textures. Additionally, DRCN showed the lowest RMSE 0.05849 and MAE 0.0415 values, confirming its ability to minimize reconstruction errors more effectively than VDSR and LapSRN.  
spectral response; MSFA one-shot; deep learning; multispectral image +
Footnote †: journal: _Algorithms_