esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
[37] suggested that impurity atoms (not identified) in an electroplated Cu film assisted void formation. Moreover, [PERSON] et al. [38] suggested that excess H in a Cu pad introduced during electroplating condensed into voids in Cu\({}_{3}\)Sn. Although further studies are necessary, the submicron voids observed in the Cu\({}_{3}\)Sn phase only on the Cu particles in this study might have occurred because of impurity atoms in the Cu particles. Based on the observation results of the cross-section before/after thermal aging, the increase in the amount of the submicron voids could negatively affect the TLPS joint strengths. The microstructural evolution aged at 150-200 \({}^{\circ}\)C can be approximately described as the following evolving model, as shown in Figure 13. First, the original metallurgical microstructure of the TLPS layer was mainly composed of Cu\({}_{6}\)Sn\({}_{5}\) (or (Cu\({}_{3}\)Ni)\({}_{6}\)Sn\({}_{5}\) at the chip side interface), residual Cu particles, and Cu\({}_{3}\)Sn layers in between the Cu\({}_{6}\)Sn\({}_{5}\) and Cu particles, as shown in Figure 13a. Submicron voids formed at the interface between the Cu\({}_{3}\)Sn and Cu particles due to the possible mechanisms of the Kirkendall void. Secondly, with the increase in aging time, the transformation of the Cu\({}_{6}\)Sn\({}_{5}\) (or (Cu\({}_{3}\)Ni)\({}_{6}\)Sn\({}_{5}\) at the chip side interface) into Cu\({}_{3}\)Sn through the diffusion of Cu atoms from the residual Cu particles and the formation of submicron voids at the interface between the Cu\({}_{3}\)Sn and residual Cu particles occurred simultaneously, as shown in Figure 13b. Finally, with the increase in the aging time, the residual Cu\({}_{6}\)Sn\({}_{5}\), except for the small amount of (Cu\({}_{3}\)Ni)\({}_{6}\)Sn\({}_{5}\) at the chip side interface, fully transformed into Cu\({}_{3}\)Sn. In addition, the number of submicron voids gradually increased, as shown in Figure 13c. The analysis of the thermal aging characteristics for the TLP sintering shows that the joints aged at 150-200 \({}^{\circ}\)C for 1000 h maintained a shear strength exceeding 19 MPa on average, as shown in Figure 11 in Section 3, because of both the positive and negative impacts of thermal aging on the shear strengths, which include the transformation of Cu\({}_{6}\)Sn\({}_{5}\) into Cu\({}_{3}\)Sn and the formation of submicron voids at the interface between the Cu\({}_{3}\)Sn and residual Cu particles, respectively. In other words, the TLPS layer could produce relatively stable strengths for die-attach applications through metallurgical reactions during thermal aging.
## 3 The Vectorized Earth Observation Retrieval Classifier
### Inclusion of Discrete Features  
The features discussed so far with respect to VEOR training were of the continuous type, which allows for the computation of Euclidean distances and percentiles. In the case of discrete/categorical features (e.g., land cover), VEOR training is performed separately for each discrete class, which results in several separate LUV. Further, the integer labels describing classes are placed on the most significant bit ranges within each 64-bit ID value. This, in turn, allows the combination of all of the LUV using a bitwise_or operator, as each LUV with ID occupies a different bit range within the merged LUV. Obviously, several discrete features can be placed on the most significant bit ranges, giving great flexibility for the adjustment of VEOR settings to a wide range of environmental conditions. In any case, the limiting factor is the 64-bit value of the ID, which should encompass both continuous and discrete features.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Problem Formulation
#### 2.1.2 Observation Model  
Let \(\mathbf{s}\) be the target periodically modulated signal. As previously pointed out, these signals manifest in the spectrum through a set of harmonic with sidebands, thus being perfectly sparse in the Fourier domain. Therefore, the Discrete Fourier Transform (DFT) is well suited to analyze these signals. Precisely, these harmonics are located at the carrier frequency and its integer multiples, with sidebands equally spaced by the modulation frequencies. The carrier frequency is assumed to be known a priori and it will be denoted by \(f_{c}\), while the frequency locations of the modulating signal are unknown. This is commonly the case when the modulating signal consists of many sinusoids of unknown frequencies, such as in vibration-based condition monitoring of planetary gearboxes [46] or in complex drive-trains. In some rare cases, the machine kinematics may be unknown, and so is the meshing frequency. This particular case is out of the scope of the present paper. The proposed method aims at estimating the target signal \(\mathbf{s}\in\mathbb{R}^{N}\) from a non-regular sub-sampled signal \(\mathbf{y}\in\mathbb{R}^{M}\) with \(M<<N\) (i.e., samples are not taken as equally spaced in time). The considered problem can be formulated, as follows  
\[\mathbf{y}=\mathbf{H}\mathbf{\Phi}^{-1}\mathbf{x}+\mathbf{\omega} \tag{1}\]  
where \(\mathbf{\Phi}\) is the normalized DFT ( \(\phi_{k,n}=\frac{1}{\sqrt{N}}\exp(-i\pi(k-1)n/N)\), so that \(\mathbf{\Phi}^{\dagger}\mathbf{\Phi}=\mathbf{I}_{N}\), where \(\mathbf{\Phi}^{\intercal}\) denotes the conjugate transpose of matrix \(\mathbf{\Phi}\) and \(\mathbf{I}_{N}\) is the identity matrix of size \(N\)), \(\mathbf{H}\in\mathbb{R}^{M\times N}\) is the non-regular sampling matrix being formed with 0 and 1, \(\mathbf{x}\in\mathbb{C}^{N}\) is the vector of sparse unknown Fourier coefficients to be estimated (i.e., \(\mathbf{x}=\mathbf{G}\mathbf{s}\)), and \(\mathbf{\omega}\) is an additive noise.  
It should be mentioned that the sparsity hypothesis in the DFT basis may be altered due the spectral leakage for a short or/and non-stationary observation. The spectral CS problem under these conditions has been addressed in a large number of works (see [12] for an example). In this paper, we assume that the signal to be recovered is sufficiently long to ensure a good frequency precision and that the speed variations are sufficiently small to be neglected.
###### Abstract  
Orthogonal frequency-division multiplexing (OFDM) is an attractive multicarrier technique for the simplicity of equalization and high data throughput. However, the transmitted OFDM signal has a very high peak-to-average power ratio (PAPR), which severely degrades the performance of practical OFDM systems and reduces the efficiency of high-power amplifiers (HPA). The selected mapping (SLM) scheme is an effective PAPR reduction method of OFDM signals. However, this approach usually requires side information (SI) transmission, which increases the difficulty of the hardware implementation with high complexity and reduces the data transmission rate. In this paper, based on designing phase rotation vectors in the time domain, a novel blind SLM method with low complexity is proposed to reduce the PAPR of OFDM signals. At the transmitter, the proposed method properly designs the phase rotation vectors in the time domain, which can be considered as an equivalent wireless channel without SI transmission. At the receiver, the effect of phase rotation vectors can be removed by the conventional channel estimation method, and the data demodulation processing can be easily performed by the frequency domain equalization. Simulation results show that the proposed scheme can achieve low complexity in PAPR reduction and has great robustness in bit error rate (BER) performance compared to the other low-complexity SLM PAPR schemes.  
orthogonal frequency division multiplexing (OFDM); selected mapping (SLM); peak-to-average power ratio (PAPR); side information (SI) +
Footnote †: journal: Information Information  
1
Footnote 1: institutetext: School of Physics and Electronic Information, Luoyang Normal University, Luoyang 471022, China  
2
Footnote 2: institutetext: Correspondence: [EMAIL_ADDRESS]; Tel.: +86-379-68618309
# Microseismic Velocity Inversion Based on Deep Learning and Data Augmentation
## 4 Result
### Robustness Testing  
In order to further evaluate the superiority of the proposed data augmentation method and hybrid training strategy, we carry out robustness tests on the real data of the first stage. We denoise the real data by wavelet filtering to obtain the clean signals, and then calculate the SNR of the real data [43]:  
\[S/N=10\times\log_{10}\left(\frac{S_{c}}{S_{n}}\right)=10\times\log_{10}\left( \frac{S_{c}}{S_{r}-S_{c}}\right) \tag{9}\]  
where \(S/N\) is the SNR, \(S_{r}\) is the real data signal, \(S_{c}\) is the clean signal after denoising the real data signal, and \(S_{n}\) is the noise of the real data signal.  
The distribution of the SNRs for all events in the first stage is shown in Figure 6. Most of the SNRs of the real events are lower than 5 dB. We select a sample event (\(S/N=3.44\)) to quantitatively evaluate the stability and robustness of the network. The predicted two-dimensional and one-dimensional velocity profiles of the Unet model using the three training strategies are shown in Figures 7 and 8. The detailed values of quantitative metrics are listed in Figure 7. The results suggested that the data augmentation method can significantly improve the prediction accuracy of purely synthetic data by introducing real data information. Moreover, the hybrid training strategy effectively utilizes the useful  
Figure 5: Two-dimensional profiles of the reference and predicted velocity values of an arbitrary event from the first stage. (**a,b**) The reference P- and S-wave velocities. (**c,d**) Predictions of P- and S-wave velocities trained with the synthetic dataset (when the epoch is 80). (**e,f**) Predictions of P- and S-wave velocities trained with both the synthetic and the augmented dataset (when the epoch is 200).  
information of the synthetic data in the low-frequency components and yields the best inversion results.  
Figure 6: The distribution of SNRs of the events of the first stage.  
Figure 7: Two-dimensional profiles of the predicted velocity values of the sample event. (**a,b**) Predictions of P- and S-wave velocities trained with the synthetic dataset only. (**c,d**) Predictions of P- and S-wave velocities trained with the augmented dataset only. (**e,f**) Predictions of P- and S-wave velocities trained with the hybrid strategy involving both synthetic and augmented data. The reference velocity models are shown in Figure 5a,b.
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 3 Experiment and Analysis
### Real Data Verification  
#### 3.2.1 Experimental Data  
In order to further verify the accuracy of the proposed method, experiments were carried out in a high-speed railway multi-functional shaking-table test system. The experimental scene is shown in Figure 11, and one of the experimental images is shown in Figure 12; from the image, we can see that due to the high frequency of shooting, the image has serious noise. In this experiment, a displacement sequence was set to control the vibration of the shaking table to drive the bridge to vibrate. A high-frequency camera was used to obtain time-series images of the bridge vibrations. The above-mentioned five methods were used to calculate the displacement sequences, and then they were compared to the corresponding results with the vibration-table-control input sequence, to verify the accuracy of the proposed method.  
The high-speed railway multi-function shaking-table test system consists of a 4 m \(\times\) 4 m six-degree-of-freedom fixed table and three 4 m \(\times\) 4 m six-degree-of-freedom mobile tables. The four shaking tables are all built on the same straight line and can be used independently or form a variety of spacing arrays. A single shaking table has the  
Figure 11: The experiment scene of indoor experiment.  
Figure 12: One of the experimental images.  
characteristics of three directions and six degrees of freedom, a large stroke, and a wide frequency band. The whole test system is mainly composed of the following parts: four sets of six-degree-of-freedom vibration tables (actuator, servo valve, differential pressure sensor, bearing, and steel platform); the adjustment system for the installation position of the movable table (support system, clamping system, winch system, and pipeline chain); a set of PULSAR double array digital control systems (servo controller, modulator, computer hardware/software, and uninterruptible power supply); hydraulic power source (hydraulic oil source, hydraulic oil source distributor, motors/pumps, accumulators, and oil-water heat exchangers), etc. The main technical parameters of the shaking table of the high-speed railway shaking-table test system are shown in Table 3.  
During the experiment, four shaking tables were used to vibrate up and down synchronously, and the vibrations were transmitted to the simulated rail bridge to simulate a rail bridge under an earthquake state; a high-speed train was simulated by the proportional model of the high-speed locomotive. This paper uses a fixed shooting frequency of 200 HZ to shoot the track bridge, obtain the image data, and store it in a notebook connected to the camera. The camera distance was about 3 m from the lens to the monitoring surface. Before the vibrating table started to vibrate, the camera was turned on to shoot for a total of 30 s, and 5579 images were obtained (with a small frame drop phenomenon). The entire vibration process of the vibrating table was completely recorded in the images.
## 4 Conclusions  
This work describes the effect of FMFRF on the flame, the blowoff limit, and the blowoff transient characteristic using a synergetic high-speed experimental method at 20 kHz, including OH*-CL, CH\({}_{2}\)O-, and SO\({}_{2}\)-PLIF. The frequency of the external acoustic excitation was fixed at 400 Hz, and different forcing amplitudes were implemented in the experiment. It was demonstrated that the blowoff limits are extended with less blowoff precursor occurrence for the forced non-premixed flames on the fuel side. The probable reasons proposed in this work include (a) the increased fuel penetration distance and residence time induced by the rapid pulsation, (b) the enhanced flame surface caused by vortex stretch, (c) the preheated fuel attributed to the hot product, and (d) the temperature rise in the CRZ due to improved combustion efficiency. A deep understanding of the blowoff mechanism will help the gas turbine combustors to operate within broader operability regimes. This work used gaseous fuel, but the majority of engines in practical applications utilize liquid fuels. Our future work will focus on the influence of the atomization and evaporation of liquid fuels on the blowoff performance with varying FMFRF. The velocity field and the mixture fraction will be evaluated by particle image velocimetry and acetone-PLIF, providing more evidence of the flame's stability under acoustic forcing.  
Conceptualization, Y.G.; Data curation, C.F.; Formal analysis, C.F.; Funding acquisition, Y.G.; Investigation, C.F., X.W. and Y.W.; Methodology, C.F. and Y.G.; Project administration, Y.G.; Supervision, Y.G.; Writing--original draft, C.F.; Writing--review and editing, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers U2141221 and 52076136; the National Major Science and Technology Project, grant numbers J2019-III-0004-0047 and Y2022-III-0001-0010; the Center for Basic Science of Aero Engines and Gas Turbines Project, grant number P2022-B-II-019-001; the Natural Science Foundation of Shanghai, grant numbers 22 ZR1467900 and 23 ZR1481400.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors appreciate [PERSON] from AECC Commercial Aircraft Engine Co., Ltd., for his suggestions in the discussion part.  
The authors declare no conflicts of interest.  
Figure 11: Global OH*-CL signal intensity and the occurrence probability of blowoff precursors under various FMFRF amplitudes at a mean fuel mass flow rate of 0.142 g/s.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Interference Cancellation-Based Protocols
#### 5.3.2. Irregular Repetition Slotted Aloha (IRSA)  
This protocol represents an improvement over CRDSA. Although it also requires the nodes to send copies of packets in randomly chosen slots, the difference lies in that the number of duplicates for each packet varies depending on an optimized distribution probability. The protocol is intended to improve the performance over uplink satellite channels [65].  
Irregular repetition slotted Aloha (IRSA) achieves a maximum performance of \(S=0.8\) for a given load of \(C=0.85\). In the case of a \(PLR=10^{-3}\), the supported normalized offered load rises to \(C\simeq 0.7\). The complexity of this protocol is similar to the complexity of CRDSA, but it adds the difficulty of calculating the different number of repetitions per packet. Given its performance, IRSA is considered an energy efficient protocol for normalized offered loads near \(C=0.7\), because of its low \(PLR\).
## 1 Introduction  
Agains the background of global informatization and digitization, traditional agriculture is gradually turning into digital agriculture. Greenhouse cultivation is the major method of vegetable production in many areas of China. Although some modern greenhouses are emerging, traditional greenhouses account for the majority of those used in China. Since most greenhouses are poorly equipped with backwards facilities, farmers have to be on duty all day and work very hard in the greenhouses due to management inefficiencies.  
Wireless sensor networks are a modern technology which integrates the knowledge of sensors, automation control, digital network transmission, information storage, and information processing. Currently wireless sensor network technology has been mostly applied to environmental monitoring. In this paper, a vegetable greenhouse architecture is proposed to achieve scientific cultivation and lower management costs in the aspect of environmental monitoring. According to the analysis of the features of greenhouse environment, a practical and low-cost greenhouse monitoring system is designed based on wireless sensor network technology in order to monitor key environmental parameters such as the temperature, humidity, and soil moisture [1; 2; 3; 4; 5; 6].
## 3 Method and Model Preparation
### Experiment Preparation and Machine Learning Model
#### 3.3.1 BPNN (Back Propagation Neural Network) & ELM (Long Short-Term Memory)  
BP neural network is the most basic neural network, and its output results adopt forward propagation, and errors adopt backward propagation (Back Propagation). BP neural network has a three-layer structure of the input layer, hidden layer, and output layer. The input layer receives data, and the output layer outputs data. The neurons of the previous layer are connected to the neurons of the next layer, information transmitted by the neurons of the previous layer is collected, and the value is passed to the next layer after \"activation\".  
The weights of the hidden layer nodes of the ELM network are randomly generated or manually defined. The learning process only needs to calculate the output weights. Through such rules, the generalization performance of the model is excellent, and the speed is much improved. The most prominent feature of ELM is that for traditional neural networks, it is faster than conventional learning algorithms under the premise of ensuring learning accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Specific Heat Capacity** & **Density** & **Heat Transfer Coefficient** \\  & _c(k)/(kg\(\times\)\({}^{\circ}\)C)_ & _p(kg/m\({}^{3}\))_ & _l(W/m \(\cdot\)\({}^{\circ}\)C)_ \\ \hline Air & 1.4036 & 1.4527 & 0.02234 \\ Cyanuramide,2,4,6-Triamino-s-Triazine & 0.9707 & 1095.27 & 0.159 \\ Asbestine & 0.84 & [140,200] & 0.043 \\ PU & 0.75 & 125 & 0.024 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Properties of materials in heat conduction networks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature Difference (\({}^{\circ}\)C)** & **Input to the System Over 1 h Heat (KJ)** & **Heating Power (W)** \\ \hline
0–5 & 0–344.7 & 0–95.75 \\
5–10 & 344.7–689.4 & 95.75–191.5 \\
10–15 & 689.4–1034.1 & 191.5–287.25 \\
15–20 & 1034.1–1378.8 & 287.25–383 \\
20–25 & 1378.8–1723.5 & 383–478.75 \\
25–30 & 1723.5–2068.2 & 478.75–574.5 \\
30–35 & 2068.2–2412.9 & 574.5–670.25 \\
35–40 & 2412.9–2757.6 & 670.25–766 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Corresponding heating power table under different temperature differences.
## 4 Current OCTA Devices
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**OCTA System** & **Central Wavelength (nm)** & **Scanning Speed (Scans/s)** & **Resolution (Axial \(\times\) Transverse, \(\mu\)m)** & **Imaging (mm)** & **Imaging Size (mm)** & **OCTA Approach** \\ \hline Zeiss AngioPlex, Cirrus HD-OCT 6000 & 840 & 100,000 & \(5\times 15\) & 2.0–2.9 & \(\begin{array}{c}3\times 3,6\times 6,\\ 8\times 8,12\times 12\end{array}\) & Combined intensity and phase variance \\ \hline Optovue AngioVue, RITVue XR AVANTI & 840 & 70,000 & \(5\times 15\) & 2.0–3.0 & \(\begin{array}{c}3\times 3,6\times 6,\\ 8\times 8\end{array}\) & Intensity decorrelation \\ \hline Topcon Triton, DRI Triton & 1050 & 100,000 & \(8\times 20\) & 2.6 & \(\begin{array}{c}3\times 3,6\times 6\end{array}\) & Intensity ratio analysis \\ \hline Heidelberg Spectralis, OCT2, Angiography & 870 & 85,000 & \(5\times 6\) & 2 & \(\begin{array}{c}3\times 3\end{array}\) & Intensity decorrelation \\ \hline Nidek AngioScan RS-3000 Advance 2 & 880 & 85,000 & \(7\times 20\) & 2.1 & \(\begin{array}{c}3\times 3,4.5\times 4.5,\\ 6\times 6,9\times 9\end{array}\) & Combined intensity and phase decorrelation \\ \hline Canon Angio, Xepphilio OCT-S1 & 855 & 100,000 & NA & 5.3 & \(\begin{array}{c}3\times 3,6\times 6,10\\ \times 10,20\times 23\end{array}\) & NA \\ \hline \end{tabular}
\end{table} 
Table 1: Different main features provided by the manufacturer of the different OCTA devices. acquisition time compared with SD-OCT, (ii) its reduced sensitivity roll-off, resulting in enhanced light penetration through the RPE, as well as a better detection of signals from the sub- RPE layer due to its reduced sensitivity roll-off, which allows a better light penetration through the RPE and thus a greater detection of signals from the sub-RPE layer [31], which is particularly important in case of drusen or RPE thickening (iii). Since that resolution depends mainly on the wavelength, increasing this parameter allowed a better axial resolution, resulting in a better characterization of the different layers even in the presence of obstacles such as the presence of cataracts or vitreous opacities. The higher wavelength in combination with the reduced sensitivity roll-off improves the detection the weaker signals from the deeper layer, resulting in a better detection of type 1 MNV compared with SD-OCTA imaging [30; 31].