esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 1 Introduction
Our first contribution is to identify privacy problems in the situation where honest-but-curious parties (in our case, MNO, MEC, and MEC applications) work together to provide services to customers. Our second contribution is a set of privacy requirements for this situation. As a third contribution, we introduce a solution and analyze how it meets the privacy requirements. Our research follows the design science methodology [17], which has six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication [18]. The paper is structured as follows. Review of relevant information for the paper and related works are explained in Section 2. We continue by presenting the statement of the problem in Section 3. In this section, we introduce the system model in Section 3.1, we define the adversary model in Section 3.2, and we list the requirements that will help to protect the privacy of the user in Section 3.3. Then, Section 4 is for the results and discussions. Considering the adversary model and privacy requirements, we present a solution for privacy-preserving access to MEC applications in Section 4.1. In Section 4.2, we explain how our solution meets the privacy requirements. We finalize our paper with final remarks, which include the conclusion and the future work in Section 5. Figure 1: Overview of the network after deployment of MEC based on [13, 16].
## 4 Results
### Comparison with Existing Algorithms
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Instance** & **Scale** & \multicolumn{3}{c}{**IMOPSQ**} & \multicolumn{3}{c}{**MCHO**} & \multicolumn{3}{c}{**NSGA-II (Using AM)**} & \multicolumn{3}{c}{**MOWPS**} \\ \cline{3-13}
**Type** & \(\kappa\times v\) & \(aNS\) & \(aHV\) & \(aDR\) & \(aNS\) & \(aHV\) & \(aDR\) & \(aNS\) & \(aHV\) & \(aDR\) & \(aNS\) & \(aHV\) & \(aDR\) \\ \hline \multirow{8}{*}{small} & A \(\times\) 1 & 3 & 0.0074 & 0 & 7 & 0.7080 & 0.0534 & 46.2 & 0.4538 & 0.0406 & 584.6 & 0.9895 & 0.9105 \\  & B \(\times\) 1 & 1.6 & 2.82 \(\times\) 10\({}^{-4}\) & 0 & 19.4 & 0.1612 & 0.2651 & 26 & 0.0609 & 0.0270 & 49.4 & 0.7865 & 0.6964 \\  & A \(\times\) 3 & 3.2 & 0.0046 & 0 & 6.6 & 0.5695 & 0.2249 & 36.6 & 0.3531 & 0.0445 & **170** & 0.9986 & 0.9908 \\  & B \(\times\) 3 & 2.2 & 0 & 0 & 17.2 & 0.3215 & 0.2216 & 54.4 & 0.1430 & 0.0097 & **266.6** & **0.9155** & 0.9640 \\  & A \(\times\) 5 & 4.2 & 0.0193 & 0 & 13.2 & 0.4061 & 0.3558 & 49.6 & 0.2489 & 0.0852 & **91.2** & **0.9999** & **1** \\  & B \(\times\) 5 & 2.4 & 1.80 \(\times\) 10\({}^{-4}\) & 0 & 11.6 & 0.1930 & 0.3005 & 138.2 & 0.0860 & 0.0556 & **194.4** & **0.9891** & 0.9994 \\ \hline \multirow{8}{*}{medium} & C \(\times\) 2 & 2.2 & 0.0012 & 0 & 16.8 & 0.7287 & 0.5179 & **103** & 0.2823 & 0.0427 & 77.4 & **0.9519** & 0.7718 \\  & D \(\times\) 2 & 1.4 & 5.66 \(\times\) 10\({}^{-4}\) & 0 & 18.4 & 0.3424 & 0.1039 & 64.8 & 0.1584 & 0.0064 & **774** & **0.8065** & 0.9943 \\ \cline{1-1}  & C \(\times\) 5 & 1.8 & 0 & 0 & 13 & 0.3037 & 0.2933 & 208 & 0.1829 & 0.0116 & **242** & **0.9357** & 0.9953 \\ \cline{1-1}  & D \(\times\) 5 & 1.8 & 2.40 \(\times\) 10\({}^{-5}\) & 0 & 7.8 & 0.3318 & 0.1662 & 111.60 & 0.2633 & 0.0109 & **434.8** & **0.9193** & 0.9950 \\ \cline{1-1}  & C \(\times\) 8 & 2.4 & 0.0012 & 0 & 6 & 0.4119 & 0.4786 & **168.2** & 0.2175 & 0.0466 & 83 & **0.9496** & 0.9751 \\ \cline{1-1}  & D \(\times\) 8 & 1.4 & 3.28 \(\times\) 10\({}^{-4}\) & 0 & 8.8 & 0.2710 & 0.2494 & 161.4 & 0.1674 & 0.0116 & **241.8** & **0.8994** & 0.9878 \\ \hline \multirow{8}{*}{large} & E \(\times\) 5 & 4.4 & 0.0291 & 0 & 9 & 0.1826 & 0.0117 & 84.6 & 0.1696 & 0.0247 & **481.4** & 0.8657 & 0.9929 \\  & F \(\times\) 5 & 2.8 & 0.0117 & 0 & 8.2 & 0.1828 & 0.0585 & 215.2 & 0.1977 & 0.0426 & **412.2** & **0.7256** & 0.9783 \\ \cline{1-1}  & E \(\times\) 10 & 3.4 & 0.0494 & 0 & 6.8 & 0.1756 & 0.0803 & 99.4 & 0.1849 & 0.0370 & **281** & **0.7306** & **0.9919** \\ \cline{1-1}  & F \(\times\) 10 & 1.2 & 0.0032 & 0 & 9 & 0.1961 & 0.1275 & 158.6 & 0.1684 & 0.0297 & **234.6** & **0.5744** & **0.8337** \\ \cline{1-1}  & E \(\times\) 15 & 2.4 & 0.0148 & 0 & 4.8 & 0.1374 & 0.1972 & **271.6** & 0.1384 & 0.1774 & 93 & **0.4188** & **0.5080** \\ \cline{1-1}  & F \(\times\) 15 & 2.2 & 0.0198 & 0 & 9.6 & 0.4417 & 0.3541 & **120.6** & 0.3103 & 0.0937 & 11.2 & **0.4675** & **0.6564** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison results of different algorithms.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 5 Conclusions  
The isothermal drying process of cement paste, mortar, and concrete, as well as drying effect on the compressive strength were investigated in this paper, and the main conclusions are as follows:  
(1) The isothermal drying process of concrete at three scales is very similar and can be divided into three stages: rising-rate drying, falling-rate drying, and slow drying stage. Water-losing rate, final water loss, and the time required to completely dry state are all related to the paste content.  
(2) After isothermal drying, cement paste, mortar, and concrete all exhibit apparent shrinkage; The porosity and average pore diameter of cement paste and mortar increase. This porosity increase in cement paste is mainly attributed to pores less than 100 nm, while in mortar it is attributed to the pores within 100-1000 nm.  
(3) Drying has a strengthening effect on the compressive strength of cement paste, mortar, and concrete, which increases with the increase of paste content; the increase in compressive strength of concrete mainly comes from the increase in paste strength and water/cement ratio has little influence on this strengthening effect.  
(4) The drying effect on compressive strength of concrete is the result of competition between the strengthening effect and the weakening effect. The former results from the densified microstructure and further hydration due to drying, and the latter results from the propagation of microcracks and the increase of porosity caused by drying.  
Conceptualization, X.D. and Z.L.; formal analysis, X.D.; funding acquisition, Z.L. and X.D.; methodology, X.D., T.T., and B.L.; supervision, T.T., B.L., and H.L.; visualization, X.D. and Z.L.; writing--original draft, X.D., Z.L, T.T., and B.L.; writing--review and editing, X.D., Z.L., and H.L.  
This research was funded by National Natural Science Foundation of China, grant number 51379178, National Key R&D Plan of China, grant number 2017 YFC0405101-2.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this paper, a low-complexity timing and carrier synchronizer for multi-\(h\) CPM is proposed based on the LPA method. We derive an ML estimator of the timing and carrier phase error and reduce the derivative MF banks for the TED using LPA. To lower the complexity further, we propose the SLPA-based TED with a sign function replacing multiplication. The S-curve analysis reveals that the proposed LPA-based and SLPA-based TEDs have stable lock points at the correct timing instant. We implement the overall system for the three promising multi-\(h\) CPM schemes with the SLPA-based synchronizer on FPGA. Compared with the conventional EL-based method, the SLPA-based receivers have ignorable performance loss and save about 27% on average slices and 67% dedicated resources with no loss of BER performance under the numerical simulation and onboard test, which achieves a better tradeoff between the performance and implementation complexity. The proposed synchronization algorithm is studied in the coherent and point-to-point communication system, and the blind estimation will be exploited by the LPA method for burst-mode transmission in the future.  
Conceptualization and methodology, L.W.; software, formal analysis, and validation, L.W., H.W. and X.W.; investigation, H.W.; resources, X.W.; data curation, H.W.; writing--original draft preparation, L.W.; writing--review and editing, Q.S.; visualization, L.W.; supervision, L.W.; project administration, L.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by by National Natural Science Foundation of China (Grant No. 62101006, 62001008), R&D Program of Beijing Municipal Education Commission (Grant No. KM202110009003), and Beijing Natural Science Foundation (Grant No. 4204095)  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 2 MIMO FMCW Radar Principle and System Analysis
### MIMO Principle Analysis  
MIMO radar systems form a virtual aperture by combining multiple transmitting and receiving antennas, which is equivalent to a more extensive physical antenna aperture [22]. The fundamental principle behind utilizing MIMO techniques to enhance the radar antenna aperture is depicted in Figure 1. This figure illustrates a radar configuration with two transmitting (TX) antennas and four receiving (RX) antennas. Using the first RX antenna as a reference, the signal from TX1 generates a phase sequence \([0,\omega,2\omega,3\omega]\) across the four RX antennas. Given that the receiving antennas at TX2 are spaced \(4d\) apart from TX1, the signal from TX2 travels an additional path length of \(4d\sin(\theta)\) relative to that from TX1. Consequently, the signals at each RX antenna exhibit an incremental phase shift of \(4\omega\) compared to the signal from TX1 [23]. Therefore, the phase sequence at the four RX antennas becomes \([4\omega,5\omega,6\omega,7\omega]\). By amalgamating the phase sequences from the two transmissions across the four RX antennas, the complete sequence becomes \([0,\omega,2\omega,3\omega,4\omega,5\omega,6\omega,7\omega]\). This configuration of 2 TX-4 RX antennas effectively synthesizes a virtual array comprising eight RX antennas [24].  
In MIMO radar systems, the angular resolution is intrinsically linked to the configuration of the antenna array. The angular resolution can be further enhanced by optimizing the aperture of the virtual array, which typically exhibits a larger effective aperture than the actual physical antenna array [25]. The aperture of the virtual array is defined by the following equation:  
\[D_{\text{virtual}}=(N_{t}\cdot N_{r}-1)\cdot d, \tag{8}\]  
where \(N_{t}\) is the number of transmitting antennas, \(N_{r}\) is the number of receiving antennas, and \(d\) is the spacing between antennas.  
Therefore, the angular resolution of the MIMO radar can be calculated by using the aperture of the virtual array as follows:  
\[\Delta\theta_{\text{MIMO}}=\frac{\lambda}{D_{\text{virtual}}} \tag{9}\]  
While MIMO technology significantly enhances the capabilities of radar systems, it also introduces complexities. MIMO radar systems increase the number of virtual antennas by transmitting and receiving multiple times, leading to a substantial increase in data volume. This surge in data volume exerts considerable pressure on the storage resources of the MIMO radar system. Moreover, to fully leverage the benefits of MIMO technology, the system must perform complex signal processing on a large data set within a minimal timeframe, challenging the real-time performance of the system [26]. Exploring more effective engineering implementation strategies is imperative to ensure the efficient processing of large data volumes in MIMO radar systems.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 2 WaveNet-Style Model
### Experiments  
To determine suitable parameters for the WaveNet-style model, a number of experiments were conducted, in which models of three guitar distortion effects were created: the Ibanez Tube Screamer, the Boss DS-1, and the Electro-Harmonix Big Muff Pi. Detailed circuit analysis of all three pedals can be found online. Digital models for the Tube Screamer [4; 7; 11; 27], DS-1 [5; 27], and Big Muff Pi [11; 28] have been proposed previously. More analysis on these pedals can also be found in our previous work [25].  
To estimate the required receptive field for modelling of the distortion effects, their linear impulse responses were estimated using the swept-sine technique [9; 29]. A low-level sine sweep was used in order to minimize the effect of circuit nonlinearities in the measurement. The estimated lengths of the impulse responses were approximately 35 ms for the Big Muff, and approximately 45 ms for the Tube Screamer and the DS-1. With a 44.1-kHz sample rate, these correspond to required receptive fields of approximately 1500 to 2000 samples, respectively.  
Figure 4: The processing speeds of models with different numbers of layers and convolution channels. The models use the gated activation. The cases above the horizontal dashed line can run in real time.  
Figure 5: The processing speeds of the 18-layer model with different activation functions and different numbers of convolution channels. The cases above the horizontal dashed line can run in real time.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 5 Some Prospects of the Microseismic Monitoring Technique in Coal Mines
### Improving the Location Accuracy of Microseismicity  
The location accuracy of microseismicity directly affects the accuracy of the source parameters, which will constrain the application of microseismic monitoring in the study of coal mining-induced dynamic disasters. The coal/rock fracture emits seismic waves traveling in layered rocks, and a complicated anisotropic velocity model should be adopted to improve the location accuracy of microseismicity instead of the isotropic velocity model. Furthermore, new methods should be developed to improve the precision and efficiency of locating the microseismic source in the coming years. Lagos and Velis [117] used Very Fast Simulated Annealing and Particle Swarm Optimization to optimize the location of microseismicity generated in hydraulic fracturing scenarios, which solves the non-linear optimization problem for travel time differences in the seismic waves and results in a remarkable increase in the speed of calculations for microseismic source location. [PERSON] et al. [118] accurately located the microseismic source in a uranium mine executing the Kirchhoff Prestack Depth Migration approach, and the localization algorithm enhanced the location of microseismicities by employing the P-wave arrival time if an anisotropic 3-D velocity model was adopted. [PERSON] et al. [119] proposed a new location method employing interferometric imaging and cross-wavelet transform to overcome the shortcomings of the  
Figure 11: Characterizing the generation process of coal and gas outbursts by the variation in gas pressure [116].  
cross-correlation calculation method and diminish location error, which provides higher source location accuracy compared to the traditional method.  
With the development of deep learning methods, the massive microseismic data generated by coal mining can be applied for deep learning to improve the efficiency and location accuracy of microseismicity. [PERSON] et al. (2020) proposed a deep learning model of a convolution long short-term memory network to pick the first arrival time of microseismic waves in a coal mine, which directly improves the accuracy of microseismic localization. [PERSON] et al. (2021) trained quality control models for P-phase arrival picks using a convolutional neural network and five other traditional deep-learning methods, and the result suggested that the convolutional neural network has the best performance for recognition of P-phase arrival picks. Furthermore, the automation of locating microseismities in coal mines based on a dynamic, in-depth analysis model for microseismic signals and artificial intelligence will be a major trend in the future.
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
###### Abstract  
Dual-band RF amplifiers play increasingly important roles in next-generation mobile communication systems including 5G, and the out-of-band intermodulation products are often not negligible since they generate interference to adjacent channels. In this article, following our previous modeling of cross-modulation for amplified dual-band signals, an analytical expression of out-of-band intermodulation for dual-band orthogonal frequency-division multiplexing signals is derived using the third-order intercept points \(1P_{3}\). The experimental measurement results validate the proposed analytical expression.  
Keywords:out-of-band intermodulation; dual-band RF amplifiers; orthogonal frequency-division multiplexing; cognitive radio; 5G +
Footnote †: journal: Accepted: 20 April 2020; Accepted: 20 April 2020; Published: 24 April 2020
## 3 Proposed Scheme
### MAML-Based ASC  
In this section, we formulate the MAML algorithm for ASC. First, we divide whole animal classes into meta-training set \(M_{train}\) and meta-test set \(M_{test}\). The meta-training and meta-test processes use \(M_{train}\) and \(M_{test}\), respectively. Then, we sample the ASC task from both \(M_{train}\) and \(M_{test}\). Here, \"task\" represents ASC for \(m\) animal sound classes with \(k\) samples (i.e., \(m\)-way \(k\)-shot). From \(M_{train}\), we sample \(S_{task}\), which represents a set of meta-training tasks consisting of \(N\) different tasks, \(T_{1}\),..., \(T_{N}\) (Line 2). From the \(M_{test}\), we sample \(T_{target}\), which implies the target ASC task.  
In the meta-learning stage, MAML encodes common prior knowledge derived from the meta-training task set \(S_{task}\) into the initial parameters \(\theta\) of DeepASC \(f_{\theta}\). This initialization can be used as a good starting point and allows \(f_{\theta}\) to quickly adapt to an unseen target task, \(T_{target}\), in the adaption stage. In the original MAML, this stage consists of two loops, an inner loop and an outer loop. In the inner loop (Line 15-17), the weights of \(f_{\theta}\) are adapted to \(T_{i}\) for a small number (\(k\)) of animal sound samples, \(D_{T_{i}}\) (support set from \(T_{i}\)), and a loss function \(\mathcal{L}_{T_{i}}\) as follows:  
\[\theta t_{i}=\theta-\alpha\
abla_{\theta}\mathcal{L}_{T_{i}}^{D_{T_{i}}}(f_{ \theta}) \tag{1}\]  
In the outer loop (Line 21), the model \(f_{\theta}\) is evaluated for unseen-animal sound samples, \(D\prime_{T_{i}}\) (query set from \(T_{i}\)), to provide feedback on the generalization performance. This feedback is used to update the initial \(\theta\) for all the \(S_{task}\) tasks to achieve a better generalization of the common knowledge as follows:  
\[\theta\leftarrow\theta-\eta\
abla_{\theta}\sum_{i=1}^{N}\mathcal{L}_{T_{i}}^{ D^{T_{i}}}\Big{(}f_{\theta_{i}}{}^{\prime}\Big{)} \tag{2}\]  
After the meta-learning stage, DeepASC \(f_{\theta}\) learns the specific knowledge of the target classification task \(T_{target}\) during the adaptation stage. Here, the classes used in this stage are unseen in the meta-learning stage, and MAML allows the model to quickly adapt to the specific knowledge, starting from the initial \(\theta\). In this stage, fine-tuning is conducted according to Equation (1) using a small number of samples, \(D_{T_{target}}\) (support set), obtained from the target task to make \(f_{\theta}\) into \(f_{\theta_{target}}\), a model trained with the specific knowledge of the target task. After the adaptation stage, \(f_{\theta_{target}}\) is used to classify the new samples (query set) obtained from the target task.
# Stressing State Analysis of SRC Column with Modeling Test and Finite Element Model Data
## 3 Build Finite Element Model  
The finite element model developed in this paper is based on an axially loaded SRC column made by [PERSON] [27]. The slenderness ratio \(\mathrm{lo}/\mathrm{d}=5\), the cross-section is circular, the diameter is 200 mm, and the net height is 1000 mm. The concrete strength grade is C40, the strength grade of the vertical reinforcement of each column is 335 MPa, and the thickness of the concrete protective layer is 25 mm. The finite element model established by ABAQUS software refers to the Chinese standard GB/50010 (2015). The concrete damage plasticity model was selected in ABAQUS for concrete member simulation with Poisson's ratio \(\
u=0.2\). The intrinsic reinforcement model was selected from GB/50010 (2015) with a bilinear model with [PERSON]'s ratio \(\
u=0.3\). Among them, the elastic modulus of vertical reinforcement E1 = \(2.0\times 10^{5}\) MPa, the strain-hardening modulus E2 = 0.005 E1, the high strength spiral reinforcement (PC steel bar) modulus of elasticity E1' = \(2.06\times 10^{5}\) MPa and reinforcement modulus E2' = 0.005 E1'. The ends of the SRC column finite element model are simplified as end plates with 1000 times the modulus of elasticity of steel and Poisson's ratio \(\
u=0.1\).  
The boundary conditions at both ends of the SRC column finite element model: one end is solidified and one end has degrees of freedom in the axial loading direction. The element types of each part of the finite element model: steel tube element type S4R, steel reinforcement element type T3D2, and concrete element type C3D8R, where C3D8R is a reduced order integration element, 4-node general-purpose shell, reduced integration with hourglass control and is a very good choice. It is worth mentioning that reduced integration is a very good strategy [28; 29]. The approximate global size of the concrete and reinforcement part of the mesh of the finite element model is 20 mm. It is worth mentioning that the size of the finite element model mesh affects its comparison with the experimental results, but it has less effect on the results of the force state modeling analysis. Figure 2 shows the meshing of each part of the SRC column finite element model and highlights the elements used for modeling correlation as well as stressing state analysis.  
Figure 2: Meshing and element selection: (**a**) SRC column; (**b**) Concrete; (**c**) Vertical bar; (**d**) Spiral bar.  
The force-displacement curves of the SRC column test and the finite element model are shown in Figure 3. The finite element model established in this paper agrees with the experimental results.
## 3 Materials and Methods
### Feature Detection and Warping
#### 3.2.2 Applying Warping with DST  
After obtaining all matching sets of points for all detected object images, we can supply all of them to DST [4] to obtain our set of warped images. DST works by defining different loss metrics like those found in STROTSS [13] and Gatys [1], more specifically, a \(I_{content}\) and a \(I_{style}\), the latter considering both the stylized un-warped image, as well as the stylized warped image. They also introduce an \(L_{warp}\) loss term to consider the potential loss after the warping is done. All of these loss terms are weighted with different parameters that we also consider. Equation (3) presents a slightly modified version of the original equation to fit more in line with the already proposed terms.  
\[\begin{array}{c}L\big{(}I_{D_{DST}}(k),I_{O}(k),I_{S_{NBB}}(k),P_{NBB}(I_{O }(k)),\theta\big{)}\\ =\alpha L_{content}\big{(}I_{O}(k),I_{O_{DST}}(k))+L_{style}\big{(}I_{S_{NBB}}( k),I_{O_{DST}}(k)\big{)}+L_{style}\big{(}I_{S_{NBB}}(k),W\big{(}I_{O_{DST}}(k), \theta\big{)}\big{)}\\ +\ \beta L_{warp}(P_{NBB}(I_{O}(k)),\theta)+\gamma R(\theta)\end{array} \tag{3}\]  
where \(I_{O_{DST}}(k)\) is the un-stylized image obtained by DST from the \(k\)-th object image and its NBB-identified geometric-style counterpart, \(\alpha,\ \beta,\ \gamma\) represent the associated weights for each of the different loss functions, and \(\theta\) is a parametrization factor for the spatial deformation. Finally, \(R(f_{\theta})\) represents the regularization function proposed in DST to allow smooth warping by making nearby pixels move in similar directions. Equation (4) presents the final output of the DST block.  
\[\begin{array}{c}\mathrm{I}_{\mathrm{DST}}\ =\ \big{\{}W\big{(}I_{O_{DST}}(k), \theta\big{)},k=\overline{1,n}\big{\}}\end{array} \tag{4}\]