esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Friction Resistance for Slurry Pipe Jacking
## 6 Conclusions  
Some typical prediction models of friction resistance have been presented and detailed comparisons and analyses have also been made. Then, a new approach considering both the effect of lubrication and the interaction of pipe-soil-slurry, by introducing an effective friction coefficient, has been established. Values of friction resistance calculated using them have been compared with values measured in four field cases and a numerical simulation case with various soils and design parameters. Better agreements are obtained, which indicate a more flexible and wider applicability of the approach in this paper as compared to the existing prediction models and numerical simulation approach. Explanations have also been sought for limited use of the existing models that may be attributed to their hypotheses not that suitable for slurry pipe jacking. The numerical simulation approach can accurately predict the friction resistance, but it is hard to determine the contact angle of pipe-soil reasonably with respect to various soils, overcut, and other conditions to obtain good prediction results. Using the approach of this paper, for higher prediction accuracy, the cohesion of soil has to be taken into account in the calculation for drives in clayey soils. The [PERSON]'s silo model together with parameters determined by the approach of UK PJA is verified as the most well-considered to calculate earth pressure. For better design in the future, the influences of design factors (buried depth, pipe diameter, and overcut) on friction resistance and lubrication efficiency have been analyzed too. The increase of pipe diameter has a strong influence on the increase of friction resistance; however, the friction amplitude appears not to be greatly affected by the buried depth. As the selection of pipe diameter and buried depth are limited by various objective conditions, special attention should be paid on the design of overcut. The overcut has to be sufficiently wide. When the overcut is small (for example smaller than 15 mm), the decrease of overcut strongly affects the decrease of lubrication efficiency, and, therefore, leads to a notable increase in friction resistance. Moreover, pipe diameter has an obvious influence on the effect of overcut on lubrication efficiency, the larger the pipe diameter the larger the overcut needed. Figure 10: The influences of factors of \(h\), \(D_{p}\), and \(\Delta R\) on the pipe-soil contact angle \(2\varepsilon\) (**a–c**) and lubrication efficiency \(\chi\) (**d–f**). **Author Contributions**: [PERSON] and [PERSON] put forward the methodology; [PERSON] and [PERSON] collected cases' data; [PERSON] derived the formulas and wrote the paper; [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors acknowledge the financial support of the National Natural Science Foundation of China (No. 51878670). **Conflicts of Interest**: The authors declare no conflict of interest. **Abbreviations**
# Analysis of Feed Inlet and Optimal Feeding Amount of Waste Ground Film Impurity Removal Equipment
## 2 Materials and Methods
### Overall Structure and Working Principle of the Film Miscellaneous Wind Separator
#### 2.1.2 Working Principle  
During the continuous operation of the film miscellaneous wind separator, a high-speed airflow from the centrifugal fan entered the drum screen through the air duct. A certain amount of film was transported upward by the conveying device and fed into the wind separator via the inlet. The amount of residual film-impurity mixture fed was controlled by adjusting the speed of the conveyor belt. When the residual film-impurity mixture entered the device, it moved toward the back end of the device under the action of airflow and collided with the tremmel screen. Under the joint action of airflow and a tremmel screen, the residual film-impurity mixture was gradually depolymerized and separated. The heavier straw and soil fell from the lower end of the drum and were separated for further processing in the device. The lighter residual film moved further under the action of airflow, entered the film collection box through the drum sieve, and was cleaned and processed. The operating principle of the wind separator is presented in Figure 5.  
Figure 4: Tumbler sieve-type film trash wind separator: (1) centrifugal fan; (2) duct; (3) inlet; (4) upper seal cover; (5) pipe support frame; (6) centrifugal blower; (7) tumbler screen; (8) spiral blade; (9) screen hole clearing device; (10) film collection box; (11) support roller; (12) frame; (13) lower seal cover; (14) controller; (15) motor; (16) conveying device; (17) spacer conveyor.
## 4 Experiment
### Selection of Low-Pass Filters  
In this section, we present a series of experiments on the effect of different low-pass filters. We use the Means Filter, Median Filter, Gaussian Filter, Non-local Means (NLM) Filter [39], and Bilateral Filter [40] on rain streak removal experiments.  
The results of different filtering methods on a rainy image are shown in Figure 9. The contour information of the NLM filter and the bilateral filter are similar in the high-frequency part. In contrast, the other three filters contain more background information. We obtained the high-frequency part of the rain image by different filters with different window sizes and input them into the subsequent GAN. Then, we obtained the de-rained image from the proposed network and calculated the SSIM value for evaluation. The results are shown in Figure 10 (since the median filter and Gaussian filter had odd numbers of filter windows, their true filter windows were 19, 29, 39, and 49 when the window size was 20, 30, 40, and 50, respectively). The bilateral filter achieved the optimal SSIM value, and, at the filter window size of 50, the SSIM value hardly changed anymore. Therefore, in order to obtain the optimal experimental results, we chose to use a bilateral filter in this paper.  
Figure 8: The de-rained results on real-world dataset. From the first column to the sixth column: Rainy image, GMM [9], UGSM [8], DCNN [16], FS-GAN [28], Ours.  
Figure 7: Display results of histogram of MSE, SSIM, PSNR, VIF, and LPIPS.
# Indoor MIMO-VLC Using Angle Diversity Transmitters
## 5 Discussions on Practical Implementation of ADTs in MIMO-VLC Systems  
In this section, we discuss the practical implementation issues of applying ADTs in indoor MIMO-VLC systems. Considering the low emitted optical power of a single LED chip, a practical LED lamp usually consists of an array of LED chips, which naturally has an array structure [25]. Generally, a planar array structure is adopted in practical LED lamps due to its simplicity and low complexity. As a result, an ADT can be achieved by modifying the planar array structure of a practical LED lamp.  
More specifically, we only need to purposely tilt some of the LED chips in the array with the pre-determined tilting angles, as illustrated in Figure 2, and then a planar array can be transformed into an ADT. Differing from conventional planar LED arrays, which usually have a two-dimensional (2D) structure, the ADTs might have a three-dimensional (3D) structure. Hence, the implementation complexity and cost of an ADT are generally higher than those of a conventional LED lamp with a planar LED array. Despite the difference in the array structure, ADTs can adopt the same directional LED chips and the same driving circuit as those used in conventional LED lamps.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 3 Coordinated Control Strategy
### Modified Efficient Modal Control  
The introduced active damping ratio defined in the previous section represents a key parameter for the derivation of the control law. In case of several devices placed on the same structure, it is possible to compare and rank the actuators according to the damping introduced by each one.  
The next step is the definition of a procedure to evaluate the optimal gain for each stand-alone device of the control system, as a function of the controlled modes. In the EMC, a relative weight between the control gains is introduced, which allows to reduce the degrees of freedom of the problem. Therefore, only the absolute magnitude of this gains has to be evaluated, in order to avoid instability or saturation phenomena of the actuator, as mentioned before.  
The EMC strategy represents a valid alternative to the centralized solutions like the so-called Independent Modal Space Control (IMSC) and Modified IMSC (MIMSC), since allows to decrease the amplitude of vibration in a minimum period of time, thus limiting the control effort. In this strategy the gains applied for each mode are weighted according to the modal vibration displacement or to the modal energy.  
In this work, a modified version of the EMC is proposed. The aim is to provide a control law with a simpler implementation, by limiting as much as possible the time for on-line computing. In the EMC, the gains are defined through an optimization algorithm which is unfeasible in this case, due to the the limited performance of the microcontroller. Then, the goal is to merge the information related to the introduced damping found before and the amplitude of vibration associated to each mode. Therefore, the maximum gain will be multiplied by two weights which are related to the modal vibration displacement and the introduce damping. In the following, the definition of these weights is presented.
## 2 Data and Methods
### 3D Geological Modeling Scheme
#### 2.3.2 Layered Modeling Scheme
Figure 6A is a highly abstracted geological section drawn based on the stratigraphic structural characteristics of the study area. As illustrated in Figure 6B, since the primary data types for bedrock modeling primarily consist of sections and bedrock geological maps, in order to fully integrate these two types of modeling data, it is initially necessary to determine the lateral constraint information of stratum c through proportional interpolation based on the relative positions of strata \(a\), \(b\), and \(c\) on the bedrock geological map, using the lateral constraint information of strata \(a\) and \(c\) from the sections as a basis. The interpolation model is specifically shown in Equation (2). \[\mathbb{R}(b)=\omega_{a}^{j}\mathbb{R}_{i}(a)+\omega_{c}^{j}\mathbb{R}_{j}(c) \tag{2}\]  
where \(\mathbb{R}(b)\) denotes the unknown lateral constraint information of stratum \(b\), \(\omega_{a}^{j}\) denotes the weight of stratum \(a\) on section \(i\), \(\omega_{c}^{j}\) denotes the weight of stratum \(c\) on section \(j\), \(\mathbb{R}_{i}(a)\) denotes the known lateral constraint information of stratum \(a\) on section \(i\), and \(\mathbb{R}_{j}(c)\) denotes the known lateral constraint information of stratum \(c\) on section \(j\). Subsequently, within the three-dimensional space, all the constraint information (derived from the bedrock geological map) on the two-dimensional horizontal plane of each stratum, as well as the lateral constraint information (either from sections or obtained through interpolation), are integrated to form the 3D modeling constraint surface of the stratum. Figure 8 presents the 3D modeling constraint surfaces of all bedrock strata within the study area. The 3D geological modeling area in Wuhan's MDA spans approximately 3400 km\({}^{2}\), with a vertical modeling depth of 200 m and a horizontal/vertical ratio of 17,000, representing a typical ultra-thin 3D geological modeling type. Notably, the Quaternary loose strata are mainly distributed in the depth range of 50 m below the surface. Therefore, to precisely depict the spatial variation characteristics of the strata, this study employs the SGrid modeling approach of GOCAD. Considering that the thinnest bedrock strata in the study area is less than 100 m thick, the modeling grid standard is determined as 50 \(\times\) 50 \(\times\) 0.5 m\({}^{3}\) to precisely capture the 3D spatial structure of the bedrock. The optimal scheme for 3D geological structure modeling of Wuhan's MDA is detailed in Table 2. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Factors**} & \multicolumn{1}{c}{**Parameters**} \\ \hline Hardware parameters & 16 cores, 512 g CPU \\ Software & GOCAD 19 \\ Modeling scheme & Grid modeling (SGrid) \\ Standard of grid & 50 \(\times\) 50 \(\times\) 0.5 m\({}^{3}\) \\ Modeling objects & 26 strata, 195 faults and 37 folds \\ Number of grid & 573 million \\ Volume of grid & 63.4 G \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scheme for 3D geological structure modeling of Wuhan’s MDA.
## 2 Compatibility between Indoor Radon Exposure and Buildings' Energy Efficiency Improvement  
### Radon Public Health Risk  
Radon is a natural radioactive gas that belongs to the noble gas family, characterized by being invisible, odorless, and tasteless. It is released from the decay chain of uranium to the atmosphere by emanation, mainly from the mineral grains in soils and granitic rocks, and it is transported to the surface of the earth by exhalation. Radon exhalation is driven through the porous zones of minerals, carried out by the air and/or through the water. In smaller amounts, radon can also be liberated from the construction materials of buildings and water supply systems [5].  
Radon can enter the body by inhalation, ingestion of drinking water, food, and der-mal exposure. Nevertheless, since it is ubiquitous in the outdoor atmosphere, inhalation is the primary source of human radon radiation exposure. Outdoors, radon level varies from 5 to 15 Bq/m\({}^{3}\), and it is consensual among the scientific community that concentration in this range does not offer any kind of public health concerns. However, indoors the scenario is different due to the lack of ventilation since in enclosed spaces indoor radon concentration can be considerably higher than outdoors, varying from 10 to more than 10,000 Bq/m\({}^{3}\)[5]. Furthermore, people spend most of their time inside buildings, where they live, work, shop, and relax. To guarantee this protection, the International Agency for Research on Cancer (IARC) in 1988, in Lyon, classified radon as a carcinogenic agent belonging to the 1 st group-carcinogenic for humans, alongside other agents such as smoking tobacco, nickel, particles resulting from diesel-burning, etc. According to the WHO, it is recognized worldwide that radon and its direct descendants are inducers of pulmonary tumors in humans [6].  
The higher the radon concentration in the air we breathe and the longer the exposure time, the greater is the risk of developing lung cancer. According to several studies [7; 8; 9], the risk increases linearly due to long-term permanent exposure to high radon concentrations within the buildings. In general, radon is the major contributor to the ionizing radiation dose to which the human being is naturally exposed, and it is, therefore, essential to adopt all measures that can mitigate the presence of this gas indoors [2]. Sources of ionizing radiation include high voltage machines--used in radiotherapy treatments or in therapeutic diagnosis, radioactive materials--natural or man-made, and cosmic radiation that can reach the earth's surface. External exposure to ionizing radiation occurs when the radiation source is outside the body, deposited on the skin or clothes, such as dust, liquid, or aerosols, and can be removed simply by washing. Internal exposure occurs when the radioactive material is inhaled, ingested, or absorbed by the skin. These materials can be dispersed by the human organism through physical, chemical, and biological processes causing radiation exposure to other parts of the body [10].
###### Abstract  
This paper presents a novel and compact vector modulator (VM) architecture implemented in 130 nm SiGe BiCMOS technology. The design is suitable for use in receive phased arrays for the gateways of major low Earth orbit (LEO) constellations that operate in the 17.8 to 20.2 GHz frequency range. The proposed architecture uses four variable gain amplifiers (VGA) that are active at any given time and are switched to generate the four quadrants. Compared to conventional architectures, this structure is more compact and produces double the output amplitude. The design offers 6-bit phase control for 360\({}^{\circ}\), and the total root mean square (RMS) phase and gain errors are 2.36\({}^{\circ}\) and 1.46 dB, respectively. The design occupies an area of 1309.4 \(\upmu\)m \(\times\) 1783.8 \(\upmu\)m (including pads).  
phased array; SiGe; vector modulator (VM); vector-sum phase shifter (VSPS)
## 2 Methodology
### Background's Disparity Correction  
EI-based disparity estimation allows for the recovery of angular information. However, as can be seen in Figure 10a, incorrect disparity may emerge from large texture-less areas such as the background because the EIs only represent segments of the whole scene. Thus, a solution is implemented in which background extraction is first performed to create a background mask, and then the disparity is corrected.  
Initially, a disparity map \(D\) of the same size as the Holoscopic image is filled with zeros. Then, the Holoscopic image's EIs are iterated over to select the left and right pairs:  
\[EI_{L}=EI(i,j),EI_{R}=EI(i,j+1);\text{ where }i\in[0,n],j\in[0,m) \tag{10}\]  
where \(i\) and \(j\) are the EIs' locations in the Holoscopic image of size \((n,m)\). The disparity for each left and right pair of EIs is computed and the resulting disparity is filtered. \(D(i,j)\) is filled with the computed disparity map.  
To separate background EIs from foreground EIs, the background threshold value \(bg_{th}\), which is in the range [0, 1] based on the disparity map, is defined. The ratio between non-zero disparity values and the total number of values in the disparity map is computed as \(r\). If this ratio is greater than \(bg_{th}\), \(EI(i,j)\) is labelled as a foreground EI; otherwise, it is labelled as a background EI. Increasing the value of \(bg_{th}\) will add more images to background EIs, and vice versa.  
\[EI(i,j)\left\{\begin{array}{l}M_{bg},r<=bg_{\text{th}}\\ M_{fr},r>bg_{\text{th}}\end{array}\right. \tag{11}\]  
Background EIs' disparity values are corrected using the correct background disparity values in the foreground EIs, as seen in Figure 11 using colour descriptors [19; 20]. To obtain a mask for background regions within foreground EIs (\(bg_{fg}\)), the mean and standard deviation of each channel (RGB) of foreground EIs are generated. A pixel in the foreground of an EI is considered to be part of the background if its value is less than one standard deviation from the mean (across all three RGB channels). This presupposes that the majority of foreground image pixels are part of the background region. This implies that the average pixel value should be within one standard deviation of the intensity of the background pixels at the very least. Finally, calculate the mode of the disparity values for \(bg_{fg}\).  
Figure 10: (**a**) The final disparity image using EIs before background correction. (**b**) The final disparity image using EIs after background correction. In this disparity map, the darker the pixel, the closer it is to the camera for clarity.  
Finally, the mode (mean or median) of the disparity values for \(bgr_{fg}\) is substituted for the disparity values (in \(D\)) of all background EIs. This acts as a disparity correction step for EIs that only contain background images since stereo SGBM will fail to work for such pairs. Instead, the background disparity is corrected by replacing it with disparity from background regions in foreground EIs. The output result can be seen in Figure 10b, where the background disparity information is fixed.
# Fire Video Recognition Based on Local and Global Adaptive Enhancement
## 5 Experiments
### Computational Complexity  
The computational complexity of LGAE was analyzed. The number of parameters and testing time are reported in Table 7, where the testing time was measured in seconds. In this table, the \"Average\" column indicates the average time expended for testing a video using the corresponding method, and the \"Total\" column represents the total time spent testing all videos using the corresponding method. Regarding the testing time, all of he experiments were conducted on the test set of Split 1 with an NVIDIA GeForce RTX 3090 GPU. To be specific, all of the videos in the test set were fed into these neural networks separately, and the testing time of each video was calculated. Moreover, all of the methods employed the same experimental settings.  
As reported in Table 7, VST had the shortest testing time due to its minimal number of parameters. Among all methods, LGAE took the longest time to test a video. The average time for predicting a video by LGAE was 0.03374 s more than that by VST. However, the experimental result of LGAE was 2.52% higher than that of VST. In conclusion, the additional  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **F1 Score** \\ \hline
13D [4] & 88.71\% & 92.13\% \\ TimeSformer [5] & 75.81\% & 82.76\% \\ VST [6] & 87.10\% & 90.70\% \\ LGAE & **90.32\%** & **93.18\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between LGAE and other methods on FireNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**F1 Score**} & \multirow{2}{*}{**Parameters (M)**} & \multicolumn{2}{c}{**Testing Time**} \\ \cline{3-5}  & & & **Average (s)** & **Total (s)** \\ \hline VST [6] & 86.41\% & **27.85** & **0.40317** & **1374** \\ VST+GAME & 88.42\% & 28.15 & 0.40346 & 1375 \\ VST+LAME & 87.87\% & 29.98 & 0.43574 & 1485 \\ LGAE & **88.93\%** & 30.28 & 0.43691 & 1489 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the computational complexity.  
time cost was worthwhile compared with the improvement in experimental results, and LGAE attained a balance between computational complexity and recognition performance.