esa-sceva/satcom-chunk-collection huggingface dataset:
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 2 Methodology
### Loss Functions and Quantitative Metrics  
Deep learning-based microseismic velocity inversion is a regression problem. We use \(MSE\) as the loss function to fit the reference velocity model and the predicted values:  
\[L_{MSE}(x_{i},\overline{x}_{i})=\frac{1}{N}\sum_{i=1}^{N}{(x_{i}-\overline{x} _{i})^{2}} \tag{4}\]  
where \(N\) is the total number of pixels in a single velocity image; \(x_{i}\) and \(\overline{x}_{i}\) are a reference velocity value and a predicted value, respectively.  
We use the regression metrics peak signal-to-noise ratio \((PSNR)\), structural similarity \((SSIM)\), and mean absolute error \((MAE)\) to quantify the prediction results and evaluate the inversion performance [39; 40; 41]. \(PSNR\) reflects the degree of global reconstruction of the velocity image. The \(PSNR\) unit is dB, and the larger the value, the better the inversion performance:  
\[PSNR(\mathbf{x},\overline{\mathbf{x}})=20\times\log_{10}\left(\frac{Max( \mathbf{x})}{\sqrt{MSE(\mathbf{x},\overline{\mathbf{x}})}}\right) \tag{5}\]  
where \(\mathbf{x}\) and \(\overline{\mathbf{x}}\) denote the velocity label and inverted velocity, respectively.  
Local structure and detail are important factors when recovering a velocity model. To evaluate the performance of the network model in reconstructing the local details, we use \(SSIM\) to characterize the similarity between the predicted velocity model and the reference velocity model. The values range from 0 to 1. The higher the value, the lower the image distortion, indicating that the predicted velocity model is closer to the ground truth:  
\[SSIM(\mathbf{x},\overline{\mathbf{x}})=\frac{(2\mu_{x}\mu_{\overline{x}}+G_{ 1})(2\sigma_{x\overline{x}}+G_{2})}{(\mu_{x}^{2}+\mu_{\overline{x}}^{2}+G_{1} )(\sigma_{x}^{2}+\sigma_{\overline{x}}^{2}+G_{2})} \tag{6}\]  
where \(\mu_{x}\) and \(\mu_{\overline{x}}\) represent the mean values of \(x_{i}\) and \(\overline{x}_{i}\) values, respectively, \(\sigma_{x}\) and \(\sigma_{\overline{x}}\) are their standard deviations, \(\sigma_{x\overline{x}}\) denotes their covariance, and \(G_{1}\) and \(G_{2}\) represent the constants to avoid a zero denominator.  
\(MAE\) is utilized to evaluate the variation in velocity across the stratigraphic interface. The lower the value, the lower the error:  
\[MAE(x_{i},\overline{x}_{i})=\frac{1}{N}{\sum_{i=1}^{N}}|x_{i}-\overline{x}_{i}| \tag{7}\]
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.5. LOF Implementation  
Based on the optimal \(k\) value identified and Manhattan as the distance measure, the LOF score for each flight with the same tail number approaching the same airport was calculated. As a rule, any flight with LOF score greater than one is anomalous. All such flights need to be investigated further. Investigating all those flights is still a laborious process and requires a lot of human effort. Setting up a threshold can help to further filter the flights that have scores greater than one but are still normal. The following sub-section explains how the threshold is set.
## 4 Finite Element Modeling Approach
### Mesh Definition  
To model the 3D structure, 20-node solid elements (Type CHX60) were used. A maximum element size of 0.34 m was used to ensure that the model would consider five elements across the thickness of the wall to account for possible curvature in a non-linear model.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 1 Introduction
Therefore, in this paper,+ we propose the use of a methodology based on 2k factorial statistical analysis and a testbed specifically developed for the analysis of ad hoc IoT networks, to study and identify the impact of critical factors in the performance of reactive and proactive routing approaches in ad hoc IoT networks. The ST-INETMANET simulation testbed (available upon request) introduced in this paper was developed using OMNET++. To develop ST-INETMANET, we performed all the necessary programming to include realistic CubeSat mobility traces, radio transceivers, and propagation. The results obtained with ST-INETMANET for the proactive and reactive routing approaches in ad hoc IoT networking were then studied using 2k factorial analysis. Using this methodology, we were able to identify critical factors for ad hoc routing in IoT networks. The presented analysis assesses the baseline performance offered by reactive routing approaches by using the AODV protocol [51]. Similarly, the baseline proactive routing approach was analyzed using OLSR [52]. Without loss of generality, we used AODV and OLSR to provide a useful performance analysis baseline for the development of new routing protocols for ad hoc IoT networking, as both protocols are commonly used as reference when evaluating the performance of the routing task in emerging ad hoc networks. In the following section, we review previous relevant work related to ad hoc IoT networks. Then we introduce the proposed methodology in Section 3, followed by the discussion of the results in Section 4. Lastly, we provide our conclusions in Section 5.
## 3 Related Works
### Dco-Ofdm  
DCO-OFDM is preferred more than ACO-OFDM for IM/DD techniques by some industrial companies and institutes, such as PureLiFi and Fraunhofer Heinrich Hertz Institute in Europe, as shown in [12; 13], since the DCO-OFDM scheme provides better bandwidth efficiency when all subcarriers are used rather than just odd subcarriers. DCO-OFDM needs a post-processing step to guarantee that the light source waveform is unipolar. Adding DC-bias to generate a non-zero waveform within the LED operation range is the most accessible post-processing approach. Despite having a higher bandwidth efficiency, DCO-OFDM has a lower BER than ACO-OFDM because of the presence of DC bias.  
Prior to arriving in the IDFT block, the OFDM signal \(X=[X_{0},X_{1},X_{2},\ldots,X_{N-1}]\), must be restricted to have Hermitian symmetry:  
\[X_{m}=X_{N-m}^{*}\text{ for }0\leq m\leq\frac{N}{2}\text{and}X_{0}=X_{N/2}=0 \tag{2}\]  
With \(m\) as the numerical order of OFDM elements and \(N\) as the size of OFDM symbol. The signal \(X\) is a real signal in the time domain after the IDFT block. The \(k\)th time-domain sample of \(X\) is revealed:  
\[x_{k}=\frac{1}{2}\sum_{m=0}^{N-1}X_{m}e^{\frac{\left|\frac{\partial\Pi Lk\,m}{ N}\right|}} \tag{3}\]  
The signal still has a negative component after this operation. The signal must then be DC-biased to ensure that it is non-complex and non-negative.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Constraints
#### 2.4.1 Communication  
The communication between software components limits their allocation to disparate hardware units, since a network link is needed between two hosts if two software components must communicate. Hence, if there is a positive transition probability between two software components \(c_{i}\) and \(c_{j}\), \(p_{ij}>0\), these two components must be embeded either on the same hardware host or on different hosts that are connected with a network link with a positive data rate, \(r_{ij}>0\). The ensuing constraint is:  
\[x_{ik}+x_{jl}\leq 1,\quad\text{if }p_{ij}>0\text{ and }r_{kl}=0, \tag{9}\]  
where \(x_{ij}\) is a decision variable has value 1 if component \(c_{i}\) is deployed on hardware host \(h_{j}\); Otherwise it is 0.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 2 Fundamentals of FSO Signal Transmission
### Geometrical Loss
For \(\lambda_{1}\) = 1550 nm and \(\lambda_{2}\) = 850 nm, the difference in geometrical loss is about 5.2 dB; see the constant shift between blue and red lines in Figure 4.
# A High Performance 0.3 V Standard-Cell-Based OTA Suitable for Automatic Layout Flow
## 7 Simulations of the Proposed Standard-Cell-Based ULV OTA
#### Simulations in the Different PVT Conditions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Voltage Variations** & **Temperature Variations** \\ \hline \(T\) [\({}^{\circ}\)C] & 27 & 27 & 0 & 80 \\ \(V_{DD}\) [mV] & 270 & 330 & 300 & 300 \\ \(A_{V_{0}}\) [dB] & 49.2 & 51.19 & 51.38 & 48.14 \\ \(A_{V_{c}}\) [dB] & 6.32 & 8.32 & 11.69 & 1.061 \\ \(GBW\) [MHz] & 6.73 & 15.57 & 6.59 & 19.68 \\ \(m\varphi\) [deg] & 54.13 & 54.13 & 54.31 & 60.32 \\ \(P_{D}\) [\(\mu\)W] & 19.6 & 19.6 & 6.59 & 30.45 \\ \(Offset\) [mV] & \(-\)2.3 & \(-\)2.32 & \(-\)2.54 & \(-\)1.65 \\ \(SR_{p}\) [\(V\)/ms] & 367.2 & 1.04 k & 488.8 & 1.006 k \\ \(SR_{m}\) [\(V\)/ms] & 464.5 & 1.33 k & 614 & 1.178 k \\ \(THD\) [\%] & 0.27 & 0.20 & 0.33 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the proposed OTA in the different supply voltage and temperature conditions. time, and high effort for the layout step. Robustness of the proposed ULV standard-cell-based OTA to mismatch variations has been tested by means of Monte Carlo simulations, and results are summarized in Table 4. Results in Table 4 show that the proposed OTA is very robust to mismatch variations. Indeed, the differential gain is always good and close to the nominal one, as well as the GBW, the phase margin, the \(P_{D}\), and also slew rate performance. However, the common mode gain ends up being quite different from the nominal one. This is due to the fact that, in nominal conditions, the \(gm_{i}\) of the inverters in the \(D2S_{E}\) block are well-matched and thus current cancellation is very accurate, whereas under mismatch variations the accuracy of the current cancellation in the D2S converter is degraded.
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 2 System Model and Problem Statement
Low channel resource consumption allows an increased system capacity, i.e., increasing the number of concurrent flows with satisfied QoS requirements and/or increasing the load of each flow. Figure 1: System model illustration.
## 4 Discussion
### Cooperative Perception Information Fusion  
The existing image-image, point cloud-point cloud, and image-point cloud perception information fusion methods coexist, and it has been found that multimodal and multi-view image-point cloud fusion cooperative perception is best. The stages of information fusion and sharing are different, which have an important impact on computing speed and perception accuracy. The fusion of processed data can reduce the amount of transmitted data and significantly reduce the network communication load while the fusion of raw sensor data has a better cooperative perception effect, but it will increase the communication burden and has a certain negative impact on the cooperative perception results. Therefore, under the premise of ensuring the performance of target detection, the transmission of low-value data should be reduced as much as possible, and the accuracy and real-time performance of cooperative perception results should be improved. In addition, the dataset construction of vehicle-road collaboration will be a key factor to ensure the research and evaluation of vehicle-road cooperation algorithms.
## 4 Results and Discussion
### Modeling of SVAR for Experimental Data
#### 4.5.2 Effect of Generator Temperature on COP and Energy of SVAR  
Figure 13 shows the variation of energy consumed and efficiency of the SVAR system with varying generator temperatures. The figure shows that both the energy and efficiency of the SVAR increase with an increase in the generator temperature. The maximum and minimum energy values of 7.12 kWh and 2.24 kWh and maximum and minimum COP values of 0.72 and 0.22 are obtained at generator temperatures of 99.55 \({}^{\circ}\)C and 80.78 \({}^{\circ}\)C, respectively.
## 3 Approach
### \(L_{2,1}\)-Norm Minimization UFS for Incomplete Data
#### 3.2.4 Convergence Analysis  
To prove the convergence of the proposed algorithm, we need the following lemma [29].  
**Lemma 1**.: _For any integers \(p\), \(q\), the following inequality is always true:_  
\[\sqrt{p}-\frac{p}{2\sqrt{q}}\leq\sqrt{q}-\frac{q}{2\sqrt{q}}. \tag{16}\]  
The convergence of the Algorithm 1 is summarized in the following theorem.  
**Theorem 1**.: _The objective function value in problem (6) monotonically decreases until convergence by updating matrix \(\mathbf{W}\) in Algorithm 1._  
Proof.: For convenience, we define  
\[\rho(\mathbf{W})=\|\mathbf{I}\circ(\mathbf{X}-\mathbf{X}\mathbf{W})\|_{2,1}+ \gamma\|\mathbf{I}\circ(\mathbf{V}-\mathbf{V}\mathbf{X})\|_{2,1}+\beta\sum \limits_{i,j}v_{i,j}\big{\|}\mathbf{I}_{i}\circ(\mathbf{x}_{i}\mathbf{w})- \mathbf{I}_{j}\circ(\mathbf{x}_{j}\mathbf{w})\big{\|}_{2}^{2}\]  
\(\widetilde{\mathbf{W}}\) is denoted as the updated \(\mathbf{W}\), since \(\mathbf{W}\) becomes smaller in each iteration and, in the light of L\({}_{2,1}\)-norm minimization [29], we have  
\[\sum\limits_{j=1}^{d}\big{\|}\widetilde{\mathbf{w}}_{j}\big{\|}_{2}^{2}\leqslant \sum\limits_{j=1}^{d}\big{\|}\mathbf{w}_{j}\big{\|}_{2}^{2}. \tag{17}\]  
and  
\[\rho(\widetilde{\mathbf{W}})+\lambda\sum\limits_{j=1}^{d}\frac{\|\widetilde{ \mathbf{w}}_{j}\|_{2}^{2}}{2\big{\|}\mathbf{w}_{j}\big{\|}_{2}^{2}}\leqslant \rho(\widetilde{\mathbf{W}})+\lambda\sum\limits_{j=1}^{d}\frac{\big{\|} \mathbf{w}_{j}\big{\|}_{2}^{2}}{2\big{\|}\mathbf{w}_{j}\big{\|}_{2}}. \tag{18}\]  
According to Lemma 1, we obtain  
\[\big{\|}\widetilde{\mathbf{w}}_{j}\big{\|}_{2}-\frac{\big{\|}\widetilde{ \mathbf{w}}_{j}\big{\|}_{2}^{2}}{2\big{\|}\mathbf{w}_{j}\big{\|}_{2}}\leqslant \big{\|}\mathbf{w}_{j}\big{\|}_{2}-\frac{\big{\|}\mathbf{w}_{j}\big{\|}_{2}^{2 }}{2\big{\|}\mathbf{w}_{j}\big{\|}_{2}}. \tag{19}\]  
by combining (18) with (19), we have  
\[\rho(\widetilde{\mathbf{W}})+\lambda\sum\limits_{j=1}^{d}\big{\|}\widetilde{ \mathbf{w}}_{j}\big{\|}_{2}\leqslant\rho(\mathbf{W})+\lambda\sum\limits_{j=1} ^{d}\big{\|}\mathbf{w}_{j}\big{\|}_{2}. \tag{20}\]  
Thus, according to the inequalities above, we achieve  
\[\rho(\widetilde{\mathbf{W}})+\lambda\big{\|}\widetilde{\mathbf{W}}\big{\|}_{2,1}\leqslant\rho(\mathbf{W})+\lambda\big{\|}\mathbf{W}\big{\|}_{2,1}. \tag{21}\]  
This states that the objective function monotonically decreases by updating \(\mathbf{w}\) in each iteration.
## 2 Materials and Methods
### Planning and Optimization of the Network Using ATOLL  
ATOLL software is used for the design and optimization of Wi-Fi networks. It allows optimizing frequency distribution within the outdoor environment to minimize the co-channel interference and to find the optimal location and number of access points. The following steps are considered in planning a wireless network using the ATOLL software [21]:  
* Creation of a new project by choosing wireless technology.
* The precision of the geographic coordinate format, in our case related to the coordinates of the ENST and the region of Algiers.
* Importing maps of the relevant region with terrain features.
* Configuring radio frequency bandwidth work parameters.
* Creation of a new type of antenna by specifying its gain and radiation pattern.
* Creation and configuration of sites or cells according to the GPS parameters of the client.
* Interference adjustment with the Automatic Frequency Planning (AFP) module.
* Optimization with the Automatic Cell Planning (ACP) module.  
The ACP module is critical and allows the optimization of networks and automatically calculating the optimal parameters in terms of coverage and quality of the signal. ACP  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Service** & **Bandwidth** \\ \hline Web browsing & 19 Mbit/s \\ Streaming & 114 Mbit/s \\ Video conference & 10 Mbit/s \\ Email & 11 Mbit/s \\ VolP & 11 Mbit/s \\ Chatting/messaging & 8 Mbit/s \\ Social media & 19 Mbit/s \\ Video call & 11 Mbit/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bandwidth per service [20] used in this study.  
removes unnecessary sites or sectors or chooses from candidate sites that can be added to improve network performance. The ACP module can also adjust the power, tilt, and azimuth of antennas. The optimization process is based on a cost function. The cost decreases when the level of coverage and quality of the signal increase. The iterative algorithm is used for each iteration when one network parameter is changed. Scenarios obtained from the different changes are ranked based on the cost function. When using the ACP, we need to specify the objectives and enable only the parameters that we wish to change during the optimization.
## 5 Conclusions  
In this paper, a modified wavenumber method for full-matrix imaging of multi-layered structures with oblique array incidence was proposed. The proposed method utilizes a frequency-domain coordinate rotation to map the original wave field onto the virtual measurement line parallel to the object's surface, performs both transmission and reception wave-field extrapolation in the rotated coordinate system, and finally obtains a total focused image of the multi-layered structure by applying a correlation imaging condition. Since the coordinate transformation is accurately defined in the mathematics, the proposed method can deal with any incident angle without precision loss. It can be used in many applications, such as immersion detection of large objects with slightly curved surfaces and oblique incidence inspection of welds with an angled wedge. The performance of the proposed method was evaluated by FMC imaging in an oblique incidence situation conducted both in simulation and experimentally. Compared with the TFM, the proposed method provides a more mathematically rigorous solution based on the wave equation, and it can suppress the artifacts presented in the TFM. In addition, the algorithm efficiency is significantly improved; for example, it only takes about 4.25 s to reconstruct an FMC dataset with a size of 4096 \(\times\) 64 \(\times\) 64 on an ordinary PC. It was demonstrated that the proposed method is superior to the TFM in both accuracy and efficiency.  
In the future, the efficiency of the proposed method can be further improved to meet the demands of real-time imaging and can be extended to 3D cases. Furthermore, the TFM is shown to outperform the wavenumber algorithm at large angles relative to the array [16], pointing the way for the improvement of the wavenumber algorithm.  
Conceptualization, H.J. and E.W.; data curation, B.Y.; formal analysis, Y.M.; methodology, B.Y. and H.J.; project administration, J.C. and K.Y.; software, Y.M.; supervision, E.W. and K.Y.; validation, Y.M.; writing--original draft, B.Y.; writing--review and editing, H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. This work was supported by the Key R&D program of Zhejiang Province (grant number 2020C01101) and the National Natural Science Foundation of China (grant number 52075486).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
[PERSON] [22] analyzed the correlations among pore volume, pore size distribution and material bearing strength, and established the prediction model of compressive strength of hardened mortar. [PERSON] [23] combined multiple linear regression with grey correlation analysis, and studied the influence of pore size distribution and porosity on the compressive strength of materials in mortar. [PERSON] [24] obtained the pore volume and pore size distribution of concrete specimens with different proportions by mercury pressing experiment, then established the statistical model of microscopic characteristics (porosity and pore size distribution) and compressive strength. At present, experimental studies on the microstructure (porosity, pore size distribution) and macroscopic mechanical properties of porous materials are mainly analyzed by mercury intrusion test, while the mercury intrusion test not only causes damage to the internal pore structure but also can not reflect 100% of the pore space distribution information adequately. Compared with the traditional mercury intrusion test, the NMR test can quickly and non-destructively obtain 100% pore spatial distribution information inside the material. Therefore, the study of rock-like rocks based on the NMR test will be helpful to grasp the internal relationship between pore structure distribution and mechanical properties. With the development of fractal theory, it has been widely used in the field of porous materials pore structure complexity and irregular characterization [25; 26; 27]. [PERSON] [28] studied and analyzed the relationship among the pore structure, fractal dimension and strength of the stone silt tailings backfill. [PERSON] [29] analyzed the relationship among pore fractal dimension, compressive strength and permeability of wollastonite concrete by mercury pressing test and electron microscope scanning test. [PERSON] [30] carried out the mercury pressing test and X-ray diffraction test on compacted concrete materials and discussed the microstructure and fractal characteristics. [PERSON] [31] studied the influence of electric breakdown energy on the microstructure, and the fractal dimensions of coal block was studied by means of NMR. [PERSON] [32] discussed the relationships between pore structure, fractal dimension and material thermal maturity of India shale by systematic analysis. Based on NMR and fractal theory, this study deeply studies and analyzes the internal relationship between microscopic pore structure characteristics and macroscopic mechanical properties of sand-like materials by carrying out the proportion test of sandstone-like materials, and establishes a strength prediction model based on the porosity and pore size distribution of rock-like materials, thereby providing reference and innovation space for the subsequent research on rock-like materials related experiments.
## 3 Results and Discussion
### Thermal Conductivity
In any case, the conductivities do not differ greatly. With respect to the design of TES bins, the conductivities of all these materials are similar and are considerably lower, by an order of magnitude, than the conductivity of typical insulating materials expected to be used in TES bins in CSP systems. Consequently, the heat loss from the TES bins will be nearly insensitive to the choice of HTM, and the HTM can be selected on the basis of economics and other performance criteria with relatively little effect from thermal conductivity.
# An Efficient Information Retrieval System Using Evolutionary Algorithms
## 2 Background and Literature Review
### Information Retrieval System (IRS) Concept  
#### 2.1.1 IRSs Models  
The development of the documents and user query representing information as well as retrieval method or processes are all described by the information retrieval (IR) model. Three models make up the fundamental IR models (i.e., Boolean, vector space, and probabilistic) [27, 28, 29]. Table 1 depicts each of them with limitations.  
Figure 1: The general structure of the IRS.