esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Resolved Imaging of Femtosecond Laser-Induced Plasma Expansion in a Nitrogen Microjet
## 4 Conclusions  
In this work, we applied time-resolved 2D fluorescence imaging for tracking the spatial evolution of a nitrogen plasma driven by high-energy femtosecond laser pulses. Plasma dynamics are ubiquitous in ultrafast high-energy laser-matter interactions and might significantly affect the process under investigation. In this framework, the role of plasma in high-order harmonic generation inside a gas jet is of fundamental importance, especially when high repetition rate lasers are used for driving the process. In this framework, we fabricated glass de Laval micro-nozzles by femtosecond laser micromachining, followed by chemical etching. We investigated the expansion dynamics of the plasma generated in the gas microjet at different gas pressures and pulse energies by measuring the spatial distribution of the N\({}_{2}\)\({}^{+}\) fluorescence at 391 nm. The measured fluorescence maps allowed us to investigate the morphology and the propagation of ions shockwaves upon continuous gas flux on a nanosecond timescale. The strong coupling between the radiative decay mechanism and the kinetics of plasma species fundamentally affects the possibility to exploit the 2D imaging scheme to fully characterize the plasma properties. On the other hand, the use of this technology coupled with conductivity and interferometric measurements, as well as spectrally-resolved approaches, will offer the potentials for achieving a deeper understanding of laser-induced plasma physics, addressing interest in many research fields, including surface treatment, high-energy physics, and ultrafast optics.  
Conceptualization, A.F., S.S., C.V., R.O., A.G.C. and R.M.V.; validation, A.G.C., R.M.V., G.V., D.C., M.D. and G.C.; formal analysis, A.F., A.G.C., R.M.V. and G.V.; investigation, A.G.C., R.M.V., G.V., D.C., M.D. and G.C.; writing--original draft preparation, A.G.C., R.M.V. and A.F. writing--review and editing, all authors; supervision, S.S., C.V., R.O.; funding acquisition, R.O., S.S., C.V., M.D. and R.M.V. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 964588 (X-PIC), by the Italian Ministry of Research and Education with the projects ELI ESFRI Roadmap and PRIN aSTAR (2017 RKWTMY), by the Consiglio Nazionale delle Ricerche with the Joint Laboratory ATTOBIO.  
Not applicable.  
All data needed to support the conclusions of the paper are present in the text. Raw data are available upon request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Dynamic Bandwidth Allocation
### Dynamic Bandwidth Allocation Adjustment  
A VDC may have more than one flow on any link. Conventionally, bandwidth is allocated among VDCs in a best-effort way, and more flows indicate more bandwidth. Thus, the VDC with less flows will not be allocated with its fair share of bandwidth. To prevent such unfair allocation of bandwidth, the dynamic bandwidth allocation should satisfy:  
1. If all VDCs can fully use the bandwidth allocated to them, the redundant bandwidth of link should be allocated among these VDCs evenly. If all flows of a VDC can fully up the allocated bandwidth, the allocated bandwidth should be allocated among these flows evenly. As Figure 6d shows, one flow of VDC-A is allocated with half of the link bandwidth, while the two flows of VDC-B get another half in total. 2. If the bandwidth of any flow within a VDC is lower than its allocated bandwidth, the spare bandwidth should be allocated to other flows within the same VDC before being allocated to the flows in other VDCs. In the orange part, as shown in Figure 6e, within VDC-B, the flow  
Figure 6: The bandwidth allocation mechanism. (**a**) The VN demands; (**b**) Initial allocation; (**c**) Rate stipulation (1 flow); (**d**) Rate stipulation (3 flows); (**e**) Dynamic flow rate adjustment. from VM-3 to VM-4 is reduced to 10, as a result of which, the flow from VM-3 to VM-5 can grow to 40. This kind of bandwidth allocation within the same VDC is called the intra-VDC bandwidth allocation. 3. If all flows within a VDC fail to fully use the allocated bandwidth, the spare bandwidth can be allocated to other VDCs evenly, and these VDCs will further allocate their share of spare bandwidth among the flows within them. This kind of allocation is called the inter-VDC bandwidth allocation. As shown by the gray part in Figure 6e, if two flows of VDC-B only require a flow of 20 of the bandwidth in total, the rest of the bandwidth can be assigned to VDC-A. As a result, the flow from VM-1 to VM-2 becomes 80. By combining the situations discussed above, we present a hierarchical max-min fairness Algorithm 1 to ensure VDC-based fair allocation of redundant bandwidth. It mainly consists of two layers--the dynamic intra-VDC bandwidth allocation and the dynamic inter-VDC bandwidth allocation in a max-min fair way. It should be noted that intra-VDC bandwidth allocation takes precedence over the inter-VDC bandwidth allocation. In each stage, we collect the traffic of all VM-to-VM flows on each link first, and then calculate the traffic of the VM-to-VM flows of the same tenant and the number of tenants sharing this link, and we further get the available bandwidth that each tenant can be allocated with.
## 3 Results and Discussion
### Principal Component Analysis of VOCs  
PCA analysis was used to extract important information from VOCs found in 12 pear cultivars. The scores scatter plot of 12 pear cultivars and the corresponding loadings plot of VOCs are shown in Figure S3. The 12 pear cultivars could be divided into three groups on the basis of the relationships between cultivars (scores) and their VOCs (loadings). The first group included one cultivar, Dr. Guyot, which contained the most types and amounts of VOCs. The second group contained three sand pear cultivars (Caiyunhong, Mantianhong, and Meirensu), which were characterized by high relative contents of ethyl butyrate (1), ethyl hexanoate (9), ethyl tglate (10), and ethyl octanoate (18). The third group was composed of the other eight cultivars, which contained high relative levels of 1-hexanol (47), 3-hexen-1-ol (48), and hexanal (73). Among cultivars in the third group, Hanhong, Qiuyue, Yali, Yuluxiang, and Zaosu are Chinese white pears, Starkrimson is an occidental pear, Nanguo belongs to the Akiko pear group, and Yunhexue is a cross between a sand and a white pear.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Classification Results and Analysis
#### 4.4.4 Performance in the Original Images Test Dataset  
We also did an experiment with the original images test dataset, and the results are shown in Table 15.  
The results show that the proposed method could also achieve good classification performance with the original images test dataset.
## 5 Discussion
### Limitations in Satisfying Security Requirements for Offline Payment Function  
As shown in Table 3, the methods introduced in the existing research can be categorized into five types to satisfy the security requirements of the digital payment method mentioned in Section 2. The five types of methods are as follows: 1. Blockchain technology, 2. Trusted third party involvement, 3. Supply of certificates to transactions members, 4. Tracking flow of money, 5. Cryptographic technique.
Many pathological voice detection systems have been published in the literature. However, to our knowledge, we are the first to use the cochlear simulation model to implement a pathological voice detection system. The voice samples are processed using a cochlear simulation model, and then the processed voice samples are applied to the input of a CNN for final classification. Cochlear implants are sensory prosthetic devices. They are capable of establishing the functional hearing of the listeners with severe hearing loss. This is achieved by establishing direct electrical stimulation to the auditory nerves for the people with damaged hair cells in the basilar membrane. These hair cells are tuned at different frequencies to aid hearing perception for people with no hearing impairment [25]. A typical cochlear implant system includes several signal processing steps: removal of the D.C. component, pre-emphasis, division of the signal into a set of channels, rectification, and lowpass filtering. Among these signal processing steps, the most critical one is dividing the signal into several channels using a filterbank. The center frequencies and the bandwidth of these filters are determined based on the human cochlear vibration patterns caused by the audio samples. In this work, we consider two models for the filterbank. One model uses a bank of bandpass filters, and the other uses gammatone filters. A bank of bandpass filters is commonly used in commercially available cochlear implants. However, recently, researchers are recommending using gammatone filters instead. The main advantages of the gammatone filters are that they: (a) provide an appropriate \"pseudo-resonant\" frequency transfer function, (b) demonstrate a simple impulse response, and (c) support efficient hardware implementation [26]. Finally, the processed audio features are applied to the input of a CNN for classification. The main contributions of this work are as follows:  
* It develops a novel, non-invasive pathological voice detection algorithm based on speech signal processing that mimics the biological process of speech perception and a deep learning approach. * It extracts audio information using gammatone filters and conventional bandpass filters to examine their efficacy for pathological voice identification. * It eliminates the necessity of choosing the suitable features from speech samples to aid the classification mechanism. * It achieves a reasonably high classification accuracy without overwhelming the computation burden on the system. * It provides a detailed performance analysis of the proposed system in terms of _accuracy_, _precision_, _recall_, _NPV_, and _F1 score_. * It compares the performances of the proposed system with other related works to demonstrate its effectiveness. The rest of the paper is organized as follows: some related works are presented in Section 2, the materials and methods are explained in Section 3, the results are analyzed in Section 4, and the paper is concluded with Section 5.
## 3 Results and Discussion
### Machine Learning Classification  
#### 3.2.1 Pre-Processing  
The EEG data are collected using a DSI-24 EEG headset. These data are then transformed into a two-dimensional brain heatmap, offering a visual representation of the subject's brain activity. To optimize the heatmap for real-time analysis, the resolution is adjusted, and the color palette is standardized. Data augmentation techniques are applied to the heatmaps to simulate varying brain states, including different levels of cognitive stress and relaxation. This process results in a comprehensive dataset suitable for training machine learning models, with each heatmap undergoing normalization to a uniform size for consistency in feature extraction.  
Figure 6: Comparative brain heatmaps: (**a**) normal brain activity, (**b**) brain activity under stress with active frontal lobe. The generated heatmaps were calculated from the time domain root mean square (RMS) value of the EEG signal over a 1-s window.
## 3 Experiments and Analyses  
The proposed multi-focus image fusion method is applied to standard multi-focus images from public website [29]. All standard multi-focus images used in this paper are free to use for research purposes. The images from the image fusion library have the size of 256 \(\times\) 256 pixels or 320 \(\times\) 240 pixels. The fused images are evaluated by comparing them to the fused images of other existing methods. In this paper, four pairs of images are used as a case study to simulate the proposed multi-focus image fusion method. To simulate a real world environment, four pairs of images have two scenes. One is an outdoor scene, such as a hot-air balloon and leopard shown in Figure 3a,b respectively. The other is an indoor scene, such as a lab and bottle shown in Figure 3c,d respectively. These four pairs of original images are from the same sensor modality. Since each image focuses on a different object, there are two images for each scene. The out-of-focus regions in the original images are blurred.
## 5 System Modeling and Simulation
### System Simulation
\begin{table}
\begin{tabular}{l l l c c} \hline \hline
**Parts** & **Physical Meaning** & **Character** & **Value** & **Units** \\ \hline Asynchronous motor & Driving torque & Potential Source & – & – \\ \hline \multirow{2}{*}{Pully} & The moment of inertia & Inertia Element & \(1.04\times 10^{-3}\) & kg\(\cdot\)m\({}^{2}\) \\  & Rotational damping & Resistive Element & \(8.2\times 10^{-4}\) & N\(\cdot\)m\(\cdot\)s\(/\)rad \\ \hline \multirow{3}{*}{Synchronous belt} & Rotational motion to straight motion & Converter & \(1/48.51\times 10^{-3}\) & rad/m \\  & Elastic coefficient & Capacitive Element & \(1/15,200\) & m/N \\  & Straight motion to rotational motion & Converter & \(48.51\times 10^{-3}\) & m/rad \\ \hline \multirow{3}{*}{Brake and Reducer} & The moment of inertia & Inertia Element & \(3.865\times 10^{-3}\) & kg\(\cdot\)m\({}^{2}\) \\  & Rotational damping & Resistive Element & \(3.8\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\(/\)rad \\  & The reduction ratio of reducer & Converter & \(40\) & – \\ \hline \multirow{3}{*}{Ball screw} & The moment of inertia & Inertia Element & \(2.295\times 10^{-2}\) & kg\(\cdot\)m\({}^{2}\) \\  & Rotational damping & Resistive Element & \(2.5\times 10^{-3}\) & N\(\cdot\)m\(\cdot\)s\(/\)rad \\  & Rotational motion to straight motion & Converter & \(2\pi/(20\times 10^{-3})\) & rad/m \\ \hline \multirow{3}{*}{Movable pulley} & Mass & Inertia Element & \(35.753\) & kg \\  & Movement damping & Resistive Element & \(40\) & N\(\cdot\)s\(/\)m \\  & Chain transmission ratio & Converter & \(1/2\) & – \\ \hline \multirow{3}{*}{Chain and Claw} & The elastic coefficient of chain & Capacitive Element & \(0.5\times 10^{-7}\) & m/N \\  & Mass & Inertia Element & \(44.328\) & kg \\  & Movement damping & Resistive Element & \(130\) & N\(\cdot\)s\(/\)m \\ \hline \multirow{2}{*}{Helicopter} & Motion resistance & Potential Source & – & – \\  & Mass & Inertia Element & \(10^{4}\) & kg \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of ERSD transmission system. * Capture simulation: This test is used to verify whether the capture time meets the requirements and whether the final capture speed can be significantly reduced. * Traction simulation: The purpose of this test is to verify whether EASIST can adjust the moving speed of the claw in real time according to the system input when performing the traction task.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 5 Conclusions  
Three TSPA-based Bayesian filtering MBPU methods have been proposed. These proposed methods transplant the EKF, CKF, and UIF to the MBPU methods, respectively. More meaningfully, this paper proposed a TSPA-based Bayesian filtering MBPU framework. We proposed a gradient  
Figure 6: Robustness analysis using simulated data of Figure 4b,c with different methods.  
estimation method based on the first step of TSPA, which can eliminate the limitation of phase gradients by the PCA. Then, the corresponding PU model is determined according to different Bayesian filtering methods. Finally, the TSPA-based Bayesian filtering MBPU method unwarps the pixels, one by one, using an effective quality map based on heapsort. This proposed MBPU framework provides a basis for the research of MBPU methods with filtering functions.  
From the results of the simulated data, the TSPA-based Bayesian filtering MBPU methods not only ignore the PCA, but also have good noise robustness. It is further seen through the robustness analysis experiments that TSPA is easily affected by noise. However, TSPA-based Bayesian filtering MBPU methods have great noise robustness. Experiments with a set of TanDEM-X data with topography exhibit severe undulations. In addition, ICESat data points are used to evaluate the accuracy of the DEMs generated by the different methods. The results of the experiments demonstrate that the proposed TSPA-based Bayesian filtering MBPU methods can obtain better results, and are more efficient than the traditional TSPA.  
Conceptualization, Y.G.; methodology, Y.G.; formal analysis, X.T., T.L., Q.C., X.Z., S.L., and J.L.; resources, T.L.; writing--review and editing, X.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2017 YFB0502700, State Key Program of National Natural Science Foundation of China, grant number 41774026, National Natural Science Foundation of China, grant number 61801136 and 41901303, and Civilian Space Program of China, grant number D010102.  
The authors would like to thank the German Aerospace Center (DLR) for providing the TanDEM-X data (No: CAL_VAL6993). The authors also thank the anonymous reviewers for their constructive comments and suggestions.  
The authors declare no conflict of interest.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 5 Experimental Evaluation
### Test Design  
To test our system, a new protocol was defined following the same structure as in [20; 48] but with the addition of the SC-FDE modulation. The frequency was fixed at 5.4 MHz.  
A total of 50 frames were transmitted, each of which composed of a group of four packets. All frames started with an initial 6 th Pseudorandom Noise (PN) sequence with resampling of 8 and having a length of 5 ms. This was used to do the packet synchronization.  
Each packet was then composed by grouping a 60 ms tone, another 5 ms PN sequence, and one of the four modulations. The tone included in each packet allowed the Doppler shift of each modulation to be corrected. This effect is due to the Red Pitaya's clocks, which can provoke deviations of up to 10 Hz by side, so a total of 20 Hz of Doppler shift and the PN allows for the identification each modulation. Finally, this was repeated four times (once for each modulation) to form the frame.  
IoT devices are usually remote, and battery usage requires low power consumption; therefore, transmission power must be minimized [52]. Knowing that the OFDM studied in [20] had sufficient performance around 12 W, our SC-FDE system was evaluated for 6, 12, and 25 W.  
After this first approach, a second test rig was designed to perform the consumption optimization. A sweep of clipping values from 3 dB to 8 dB has been performed.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 4 Simulation Results and Analysis
### Controller Design
#### 4.1.3 The Magnitude Compensator \(S\left(z\right)\)  
The filter \(S\left(z\right)\) is a very important component of the RC, which will directly influence the performance of the controller. Its function can be mainly expressed in two aspects. Firstly, \(S\left(z\right)\) corrects the gain of control object \(P_{0}\left(z\right)\) to remain almost uniform before the cut-off frequency, which facilitates the gain \(k_{r}\), unifying the adjustment range. In other words, \(S\left(z\right)\) needs to suppress the resonant peak of \(P_{0}\left(z\right)\). Furthermore, \(S\left(z\right)\) should also improve the system stability and immunity to high-frequency interference by attenuating high frequency signals. In theory, the compensator \(S\left(z\right)\) is preferably designed as the inverse of \(P_{0}\left(z\right)\)[19]. This design method requires high accuracy of the control model. However, the actual plant cannot be completely accurate due to the uncertainty of the model parameters. Hence, according to the frequency characteristic of \(P_{0}\left(z\right)\), a suitable second-order low-pass filter \(S_{1}\left(z\right)\) and a notch filter \(S_{2}\left(z\right)\) are combined to form a magnitude compensator \(S\left(z\right)\) in this paper, where the second-order low-pass filter is used to attenuate the high frequency signal, and its general expression in the s-domain can be written as:  
\[S_{1}\left(z\right)=\frac{\omega_{n}^{2}}{s^{2}+2\xi\omega_{n}s+\omega_{n}^{2}} \tag{31}\]  
The cut-off frequency of the filter is generally set as \(1/10\) of the switching frequency, which is about 2000 Hz. The damping ratio \(\xi=0.707\) is selected in this paper. Hence, the second-order low-pass filter \(S_{1}\left(z\right)\) is designed as:  
\[S_{1}\left(z\right)=\frac{0.107859+0.14535z}{0.411296-1.15809z+z^{2}} \tag{32}\]  
Then, as for the elimination of the resonant peak, the notch filter is finally chosen as:  
\[S_{2}\left(z\right)=\frac{z^{2}+2+z^{-2}}{4} \tag{33}\]  
Figure 11: Bode plot of the internal model of RC with different \(Q\left(z\right)\).  
Figure 12 shows the Bode plot of \(P_{0}\left(z\right)\) after amplitude compensation. As can be seen from the magnitude plot, under the action of the second-order low-pass filter \(S_{1}\left(z\right)\), the attenuation of the high frequency signal is increased obviously, while the low and medium frequency characteristics are basically unaffected. The resonant peak has been suppressed effectively after the notch filter \(S_{2}\left(z\right)\) was employed. Under the complete compensation of the magnitude compensator \(S\left(z\right)\), the gain of \(P_{0}\left(z\right)\) remained almost uniform before its cut-off frequency. Therefore, the system stability and high frequency harmonic suppression capability can be enhanced greatly.