esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The task of the lossy compression of images corrupted by AWGN by the BPG coder is considered. The main attention is paid to the single-channel image case. It is shown that an OOP might exist according to the standard criteria as PSNR (and MSE). Moreover, an OOP might exist for the visual quality metrics such as MS-SSIM and PSNR-HVS-M, although, this occurs more rarely. It is demonstrated that OOP existence depends on the noise variance and image complexity where an OOP exists with a higher probability for simpler structure images and/or a higher intensity noise. With a noise intensity increase, the \(Q_{\text{OOP}}\) increases as well, and the corresponding expression is obtained. The approach to predicting the OOP's existence and the metric values in it is proposed. This is based on obtaining the approximating dependences in advance using scatter-plots and curve fitting. Then, for a given image, a simple statistical parameter has to be estimated, its value  
Figure 18: Dependences \(PSNR-HVS-M^{\text{tc}}(Q)\) for R (**a**), G (**b**), and B (**c**), and \(MS-SSIM^{\text{tc}}(Q)\) dependences for R (**d**), G (**e**), and B (**f**) component images.  
has to be used as an approximator input and the prediction with decision-making needs to be completed. Its accuracy is analyzed and shown to be appropriate for practice. The recommendations on an automatic \(Q\) setting for different practical situations are given and illustrated.  
The possibility of OOP existence for signal-dependent noise and three-channel images corrupted by AWGN is shown. This can be the direction of the future research. Another direction deals with improving the prediction accuracy for visual quality metrics by the joint processing of several input parameters that can be performed by a trained neural network.  
Conceptualization, V.L. and B.V.; methodology, S.K.; software, B.K.; validation, V.N., B.K. and S.K.; formal analysis, B.K. and V.L.; investigation, B.K.; writing--original draft preparation, V.L.; writing--review and editing, B.V.; visualization, B.K., S.K. and V.N.; supervision, V.L. and B.V. All authors have read and agreed to the published version of the manuscript.  
The research performed in this manuscript was partially supported by the French Ministries of Europe and Foreign Affairs (MEAE) and Higher Education, Research and Innovation (MESRI) through the PHC Dnipro 2021 project No. 46844Z.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Fundamental Theory of Obliquely Incident P Waves
### Verification of the Seismic Oblique Incidence Theory  
A two-dimensional homogeneous semi-infinite space model can be adopted to verify the accuracy and effectiveness of the established model. In this study, the length and width were set to 1000 m, divided into a 20 m grid size, as shown in Figure 3a. The material properties of this model are also provided herein. The midpoint A on the free surface was used as the monitoring point to compare the numerical and theoretical solutions. The time history of input displacement at the base of the free field is shown in Figure 3b, along with the input time history equation. The total time and time intervals were 2 and 0.005 s, respectively.  
Figure 3: Finite element model in the free-field and input displacement time history of the model base: (**a**) model; (**b**) input displacement time history.  
Figure 2: Schematic of oblique P wave in semi-infinite medium.  
Figure 4 presents the displacement time history of the free surface at point A when the P wave is obliquely incident (at incident angles of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 65\({}^{\circ}\)), as well as a comparison between the numerical and theoretical solutions in both the horizontal and vertical directions. Moreover, the peak values of the numerical and theoretical solutions (written as U\({}_{\rm num}\) and U\({}_{\rm the}\)) are also appropriately labeled. The numerical solution is consistent with the theoretical solution, with the maximum value of the relative error being less than 0.05%. The results show that the proposed model exhibits high accuracy and precision and could be used in numerical simulations of a concrete dam system.
## 2 Materials and Methods
### Initial Dataset
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Parameter Description** & **Unit/Format** \\ \hline Time & Local time & dd.MM.yy hh:mm \\ T & Air temperature at a height of 2 m above the Earth’s surface (in this table, & \({}^{\circ}\)C \\  & all heights are measured from the surface of the Earth) & \\ Po & Atmospheric pressure at station level & mm Hg \\ P & Atmospheric pressure normalised to mean sea level & mm Hg \\ Pa & Pressure trend: atmospheric pressure fluctuations over the last three hours & mm Hg \\ U & Relative humidity at a height of 2 m & \% \\ DD & Wind direction at a height of 10–12 m & text description (text) \\ Ff & Wind speed at a height of 10–12 m & m/s \\ ff10 & Maximum wind gust at a height of 10–12 m & m/s \\ Clover & Total cloudiness & \% + text \\ LowLevelCl & Stratocumulus clouds, stratus clouds, cumulus clouds and cumulonimbus & text \\  & clouds (lower clouds—up to 2 km in mid-latitudes) & \\ AmountLowLevelCl & Number of observed low-level clouds; the level of medium clouds in their & \% + text \\  & absence & \\ ClCeil & Height of the base of the lowest clouds & m + text \\ MidLevel & Altocumulus clouds, altostratus clouds, nimobstratus clouds (mid-level & text \\  & clouds—from 2 km to 6 km in mid-latitudes) & \\ HighLevel & Cirrus clouds, circumcumulus clouds and cirrostratus clouds (cloud & text \\  & tops—from 6 km to 13 km in mid-latitudes) & \\ VV & Horizontal visibility range & km \\ Td & Dew point temperature at a height of 2 m & \({}^{\circ}\)C \\ RRR & Anount of precipitation & mm \\ tR & Period of time during which the specified amount of precipitation was & h \\ E & Condition of the soil surface without snow or measurable ice cover & text \\ Tg & Minimum soil surface temperature overnight & \({}^{\circ}\)C \\ E’ & Condition of the soil surface with snow or measurable ice cover & text \\ sss & Snow depth & cm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Meteorological factors. * The volume of data for various components represents time series with several thousand, and sometimes tens of thousands, of values.
## 2 Surgical Tool Detection
### Missing Tool Detection with Motion Vector Prediction  
The missing tool detection process is subdivided into the following steps--a tool mapping and a tool redetection. In the tool mapping step, the presence of a missing tool is checked. To be specific, the tools identified in the current frame (t) are compared to that of the previous frame (t-1), based on the number and class. If one or more of the tools of this frame (t) have the same class as the tools of the previous frame (t-1), the tool that is closest to the tool of the previous frame (t-1) is considered as the same tool in the current frame (t). Conversely, if a tool only exists in the previous frame (t-1), it is determined that a missing tool exists.  
Once the existence of the missing tool is confirmed, motion vector prediction is performed as shown in Figure 2. As the YOLO9000 classifies the surgical tool using a predetermined bounding box, if the main feature of the surgical tool is located at the boundary of the bounding box due to the movement of the surgical tool, it cannot be detected. Therefore, the proposed algorithm predicts the position of the surgical tool in the current frame using the position of the surgical tool in the previous two frames. This prediction is based on the center point of the surgical tool. More specifically, the motion vector (MV) of the surgical tool is calculated using the position of the surgical tool in the previous two frames (Equation (1)).  
\[MV_{t}\ =\ (x_{t-1}-x_{t-2},\ y_{t-1}-y_{t-2}) \tag{1}\]  
By adding the value of this motion vector to the position vector of the previous frame (\((x_{t-1},\ y_{t-1})\)), the position in the current frame (\((\hat{x}_{t},\hat{y}_{t})\)) is predicted (Equation (2)).  
\[(\hat{x}_{t},\hat{y}_{t})\ =\ MV_{t}+\ (x_{t-1},\ y_{t-1}) \tag{2}\]  
Figure 2: Difference between the first version (**a**) and the second version (**b**) of You Only Look Once (YOLO): input image is divided into grid cells (\(\mathsf{S}\times\mathsf{S}\)). Then, prediction of each grid cell is encoded as a tensor. \(\mathsf{x}\) and \(\mathsf{y}\) are the center position, and \(\mathsf{w}\) and \(\mathsf{h}\) are the width and height of each bounding box. \(\mathsf{p}\) is the probability of whether it is an object or not, and \(\mathsf{p}_{\mathsf{c}}\) is the probability corresponding to each class.  
Tool detection is performed again with the use of the trained network by inputting the cropped image at the pre-determined size based on the predicted position of the tool. The size of the newly input image is set to be less than or equal to \(416\times 416\), which is the size of the input image of YOLO9000, so that the smaller objects can also be visible more easily. Comparison of the second result to the first result obtained based on the tool detection process, and if the intersection of the union (IOU) of the bounding box of the two results is more than 0.5, we regard that the same tool is detected twice. Accordingly, we discard the second result.
## 5 Experimental Results
### Experiment 5: Results Based on the Blood-Cell Dataset
With the PSO features, the SVM had a performance with accuracy = 0.8858, BA = 0.8858, F1-score = 0.8877, recall = 0.8858, and precision = 0.906. With the MFO features, the SVM had accuracy = 0.8838, BA = 0.8838, F1-score = 0.8859, recall = 0.8838, and precision = 0.9055. With the WOA features, the SVM had accuracy = 0.8838, BA = 0.8838, F1-score = 0.8838, F1-score = 0.8856, recall = 0.8838, and precision = 0.9041. With the AO features, the SVM achieved a performance of accuracy = 0.8838, BA = 0.8838, F1-score = 0.886, recall = 0.8838, and precision = 0.9058. With the AHA features, the SVM achieved results of accuracy = 0.8846, BA = 0.8846, F1-score = 0.8863, recall = 0.8846, and precision = 0.9035. The DT classifier achieved the worst results with all optimizers, except with AO, with which it achieved a result better than that of LDA (accuracy = 0.8806, BA = 0.8805, F1-score = 0.8822, recall = 0.8806, and precision = 0.898). To sum up, the proposed AHA-AO optimizer succeeded in selecting the best feature set of 65 features out of 512 deep features. The total number of features was smaller than the numbers of features selected by other optimizers. However, these features achieved the best results and the best time compared to those of the other optimizers. These results prove the superiority of the proposed algorithm and how it enhances the results of its base components of AO and AHA.
# Robustness of 3D Navier-Stokes System with Increasing Damping
## 4 Proof
### Existence of Pullback Attractor  
According to the estimations on the system solution, we get  
\[\|u(t)\|^{2}+\|u(t)\|_{\gamma+1}^{\gamma+1}\] \[\leq Ce^{-\delta(\varsigma-t)}(\|u(\varsigma)\|^{2}+\|u(\varsigma)\| _{\gamma+1}^{\gamma+1})+C_{1}\varepsilon^{2}e^{-\delta t}\int_{-\infty}^{t}e^{ \delta s}|g|^{2}ds+C_{2}. \tag{9}\]  
We denote \(R_{\varepsilon}(t)=(2C_{1}\varepsilon^{2}e^{-\delta t}\int_{-\infty}^{t}e^{ \delta s}|g|^{2}ds+2C_{2})^{\frac{1}{2}}\), \(\tilde{D}_{\varepsilon}(t)=\{u\in\mathcal{V}:\|u\|\leq R_{\varepsilon}(t)\}\), then the subset family \(\tilde{D}_{\varepsilon}(t)=\{\tilde{D}_{\varepsilon}(t)\}_{t\in\mathbb{R}}\) is just the pullback absorbing set of system (3) in \(\mathcal{V}\).  
Choose \(0<\delta<\delta_{1}<\
u\lambda_{1}/2\), and replace \(\delta\) with \(\delta_{1}\), then (9) still holds. For any \(u(t-\varsigma)\in\tilde{D}_{\varepsilon}(t-\varsigma)\), there holds  
\[\|S_{\varepsilon}(t,t-\varsigma)u(t-\varsigma)\|^{2}+\|S_{ \varepsilon}(t,t-\varsigma)u(t-\varsigma)\|_{\gamma+1}^{\gamma+1}\] \[\leq Ce^{-\delta_{1}\varsigma}R_{\varepsilon}^{2}(t-\varsigma)+ C_{1}\varepsilon^{2}e^{-\delta_{1}t}\int_{-\infty}^{t}e^{(\delta_{1}-\delta)s}e^{ \delta s}|g|^{2}ds+C_{2}\] \[\leq Ce^{-\delta_{1}\varsigma}R_{\varepsilon}^{2}(t-\varsigma)+ C_{1}\varepsilon^{2}e^{-\delta t}\int_{-\infty}^{t}e^{\delta s}|g|^{2}ds+C_{2}.\]  
Since \(\lim_{\varsigma\to+\infty}e^{-\delta_{1}\varsigma}R_{\varepsilon}^{2}(t- \varsigma)=0\), there is \(T_{3}=T_{3}(t,\tilde{D}_{\varepsilon}(t))>0\) satisfying  
\[S_{\varepsilon}(t,t-\varsigma)\tilde{D}_{\varepsilon}(t-\varsigma)\subset \tilde{D}_{\varepsilon}(t),\ \forall\ \varsigma>T_{3}.\]  
Lemma 4 and the fact that \(D(A)\hookrightarrow\hookrightarrow\mathcal{V}\) lead to that \(\{S_{\varepsilon}(t,\varsigma)\}\) possesses the pullback asymptotical compactness in \(\mathcal{V}\). Finally, we obtain Theorem 4 from Theorem 1.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 5 Conclusions  
Location recommendation is essential for many map-based mobile applications. In general, a good location should be surrounded by preferable facilities and away from unpreferable facilities. In our previous work, we proposed a grid-based area skyline algorithm, GASKY, to recommend such locations. In this paper, we proposed a novel distributed algorithm, MRGASKY, to improve the performance of area skyline in the MapReduce framework. The experiments are conducted to demonstrate the effectiveness and efficiency of our proposed algorithm. With the number of grids, the number of facility types and the number of objects increases, the processing time of MRGASKY algorithm increases smoothly and slowly. It is confirmed to handle the \"big data\" effectively.  
In addition, some other specific scenarios can also utilize this approach:  
* In the business field: Suppose a real estate developer would like to find a region to build a community. In general, a good region of the community should be close to some highly popular places such as bus/train stations, malls, and schools. Besides, it should be far from some unpopular places such as noisy factories and open landfills. Our proposed algorithm can help the real estate developer find some potential areas on a map, which could reduce the survey cost the whole regions.  
Figure 19: Processing Time of US_C.  
Figure 18: Processing Time of US_B.  
* In the travel field: It is very common for tourists to utilize map applications on mobile devices during a trip. In most instances, tourists would like to find some sightseeing spots where should be surrounded by preferable facilities and away from unpreferable facilities. Our algorithm can recommend such locations to the users of mobile devices in a short time.  
In the future, we would like to consider the \(k-\)dominant problem and the non-spatial properties such as price, population density, etc., in the MRGASKY algorithm. In addition, we also would like to apply our algorithm in the map applications to help more people to make location recommendations.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived the original idea for the study, analyzed the experiment results and revised the manuscript. [PERSON] performed the experiments and wrote the manuscript; [PERSON], [PERSON] and [PERSON] analyzed the data and validated the experiment results. All authors have read and approved the submitted manuscript.  
This work is partially supported by KAKENHI (16K00155,17H01823) Japan and IZUMI Co., Ltd., Japan. [PERSON] is supported by Japanese YAHATA Scholarship. [PERSON] and [PERSON] are supported by Japanese Government MEXT Scholarship.  
The authors declare no conflict of interest.
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
## 3 Mobile Fronthaul Link (MFL)  
The MFL is the physical transport link between the BBU and the RRH. In this paper, the fronthaul link is investigated for an SFM with varying fiber lengths of up to 35 km. Figure 5 illustrates the MFL. An optical amplifier and a Gaussian optical filter are used to improve the signal quality along the fiber link. The SMF is characterized by parameters shown in Table 1. In this work, the gain of optical amplifier 1 and both has noise figure of 4 dB. After filtering and amplification, the signal is launched into the SMF.
established a mining area model, simulated the mining situation using FLAC3D (6.0) software, obtained the movement and deformation of overlying strata, and proposed corresponding prevention and protection measures on this basis [29]. [PERSON] used FLAC3D software to study the law of ground moving deformation and ground slope deformation under different coal seam dip angles [30]. [PERSON] and [PERSON] et al. adopted FLAC3D numerical simulation finite difference method to conduct numerical simulation and obtained the surrounding rock movement and ground moving deformation laws of five mining areas under different mining technical parameters [31]. According to different InSAR parameters, [PERSON] used D-InSAR technology to monitor the settlement of the Yunjailing mining area and invert mining settlement damage model parameters. [PERSON] used SBAS technology to obtain large-scale deformation in mining area, and took Shendong mining area as an example, using the distributed dislocation model to invert mining earthquake magnitude [32; 33]. It can be found that, at present, FLAC3D or InSAR and other single technical means are mostly used to study the mine land subsidence, and there is a lack of comprehensive analysis of multiple technical means. At present, the Ehuobulake Coal Mine is facing the problem of land subsidence caused by mining of multiple coal seams. In order to realize the dynamic monitoring and analysis of the land subsidence law of the Ehuobulake mining area, this paper intends to adopt the method of combining FLAC3D numerical simulation and SBAS-InSAR technology to carry out research on the geological conditions of the first and fifth layers of coal production in the Ehuobulake Coal Mine. The research results can provide effective guidance for the prediction of the land subsidence law in the later mining area.
## 2 Numerical Modeling
### Numerical Models  
According to [26; 27; 28; 29], the main factors that influence ground movement include the excavation depth, ratio of the wall depth to the difference between the wall depth and excavation depth (\(\lambda\)), undrained shear strength of the soil (\(S_{\text{u}}\)), and stiffness of the retaining piles, as listed in Table 2. The inclined angle of the retaining pile walls (\(\theta\)) was also considered in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Soil Layer** & **Soil Type** & **Thickness** & **\(\gamma\)** & \(c^{\prime}\) & \(\varphi^{\prime}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{E_{50}^{\text{ref}}}\) & \(\mathbf{\gamma_{0.7}}\) \\
1-2 & Plain fill soil & 1.0 & 17.1 & 5.0 & 8.0 & 4.2 & 4.2 & 12.6 & 36.0 & 0.2 \\
4-1 & Silty clay & 2.0 & 19.3 & 20.1 & 18.0 & 4.2 & 4.2 & 12.6 & 43.2 & 0.2 \\
6-3 & Silt & 6.0 & 19.4 & 7.0 & 33.0 & 8.0 & 8.0 & 24.0 & 65.0 & 0.2 \\
6-4 & Silty clay & 4.6 & 19.0 & 21.8 & 17.7 & 4.5 & 4.5 & 20.0 & 100.0 & 0.2 \\
8-1 & Silty clay & 4.9 & 19.8 & 24.3 & 16.2 & 5.1 & 5.1 & 15.3 & 76.5 & 0.2 \\
8-2 & Silt & 1.5 & 20.1 & 9.9 & 32.3 & 9.7 & 9.7 & 45.3 & 226.5 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of the soil.  
Figure 5: Comparison of wall deflection and settlement of ground surface.  
Figure 6 shows the numerical model. A total of 3600 hypothetical excavations were generated by varying the factors listed in Table 2. Among these 3600 cases, some exhibited unrealistic excavation conditions, such as significant deformation. By excluding such unreasonable cases, models with a maximum settlement of the ground surface of less than or equal to 200 mm were considered to establish a simplified evaluation method for excavation-induced ground movements.
## 3 Demonstration of Tunable Shielding Effectiveness in GHz Bands
### Design and Experiments of Different Shielding Film Structures
#### 3.1.2 The Waveguide Testing Method  
In the microwave frequency band, it is difficult to directly test the S parameters of samples in a wide frequency range using the space\(-\)field method, so the waveguide method is widely used [23]. Besides, as learned in Figure 1, the conductivity of graphene in the microwave frequency band is independent of frequency. Therefore, based on the existing experimental conditions and sample size, we chose the frequency bands from 6 to 8 GHz as a representative case, which is widely used for the communication of satellites and television broadcasting and only a pair of waveguides are needed to obtain the SE properties of the sample.  
The schematic and setup of the waveguide method are shown in Figure 4a,b, where the sample is placed between the waveguides, and the bias voltage is applied by the DC power supply connected to two electrodes. The transmission curves with different voltages are recorded on the vector network analyzer, and the SE curves can be acquired from Equation (3). Here we chose the standard waveguide WR-137 for the measurement, whose working frequency band is 5.38-8.17 GHz and the size of its test window is 34.849 mm \(\times\) 15.799 mm. During the test, the operating mode of the electromagnetic wave in the waveguide was TE\({}_{10}\) mode, and the SE analysis was in the near field.
###### Abstract  
We report a low voltage (V\({}_{\rm DD}\)) and power (P\({}_{\rm DC}\)) 12.4-32 GHz CMOS down-conversion mixer with high conversion gain (CG) for 28 GHz 5G communications. A quarter-wavelength (\(\lambda\)/4) transmission line (TL) and a coupling capacitor (C\({}_{\rm c}\)), named the \(\lambda\)/4-TL-C-based coupler, is proposed. This is the way to attain low-V\({}_{\rm DD}\), independent RF transconductance (gm)-stage bias, harmonic suppression, and near perfect coupling from the RF gm stage to the LO switch transistors. The body-self-forward-bias (BSFB) technique, i.e., connection of the gm-stage transistors' body to drain via a large body resistance, is used for threshold voltage (V\({}_{\rm th}\)) and V\({}_{\rm DD}\) reduction and substrate leakage suppression. CG and noise figure (NF) enhancement at the same or even a lower P\({}_{\rm DC}\) is achieved because lower V\({}_{\rm DD}\) and higher gm (due to larger bias current) are used. To facilitate the RF measurement, a compact Wilkinson-power-divider-based balun with small-phase deviation and amplitude imbalance is included at RF and LO inputs. The mixer consumes 6.5 mW and achieves a CG of 14.4 \(\pm\) 1.5 dB for 12.4-32 GHz (i.e., 3 dB bandwidth (f\({}_{\rm 3 dB}\)) of 19.6 GHz), a lowest noise figure (NF\({}_{\rm min}\)) of 7 dB, and figure-of-merit (FOM) of 0.023, which is one of the best results ever reported for millimeter-wave (mm-wave) down-conversion mixers with an f\({}_{\rm 3 dB}\) larger than 10 GHz and P\({}_{\rm DC}\) lower than 10 mW.  
CMOS; low voltage; low power; transmission line; coupler; down-conversion mixer 2023