esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Novel Pre-Whitening Method and S Transform
### S Transform  
The S transform combines the separate strengths of the STFT and wavelet transforms [27] and has provided an alternative approach to process the non-stationary signals generated by mechanical systems [28].  
Suppose the short time Fourier transform (STFT) of signal \(y(t)\) is as below:  
\[\mathrm{STFT}(\tau,f)=\int\limits_{-\infty}^{\infty}y(t)g(\tau-t)e^{-j2\pi ft}dt \tag{9}\]  
where \(\tau\) and \(f\) denote the time of spectral localization and Fourier frequency, respectively, and \(g(t)\) denotes a window function.  
The S transform can be derived from (9) by replacing the window function \(g(t)\) with the Gaussian function, depicted as: \[g(t)=\frac{|f|}{\sqrt{2\pi}}e^{-\frac{t^{2}f^{2}}{2}}\] (10) Then, the S transform is defined as: \[S(\tau,f)=\int\limits_{-\infty}^{\infty}y(t)\frac{|f|}{\sqrt{2\pi}}e^{-\frac{ (\tau-t)^{2}f^{2}}{2}}e^{-j2\pi ft}dt\] (11) Actually, the S transform is a special case of STFT with the Gaussian window function. If the continuous wavelet transform (CWT) is: \[W(\tau,d)=\int\limits_{-\infty}^{\infty}y(t)\omega(t-\tau,d)dt\] (12) where \(d\) denotes the \"width\" of wavelet \(w(t,d)\), and thus, it controls the resolution; and \(w(t,d)\) denotes a scaled copy of the fundamental mother wavelet.  
Then, the S transform is a CWT with a specific mother wavelet multiplied by the phase factor: \[S(\tau,f)=e^{-j2\pi ft}W(\tau,d)\] (13) where the mother wavelet is: \[\omega(t,f)=\frac{|f|}{\sqrt{2\pi}}e^{-\frac{t^{2}f^{2}}{2}}e^{-j2\pi ft}\] (14) Note that the factor \(d\) is the inverse of the frequency \(f\). Compared with STFT and CWT, the S transform has the following merits: (1) The window length used in STFT is a fixed value. On the contrary, the window length used in the S transform is a function of time and frequency. In other words, the window length is adaptive. In the time domain, the window will be wider for low frequencies and will be narrower for high frequencies. This will provide better localization in the frequency domain for low frequencies and provide better localization in the time domain for higher frequencies. These characteristics are very similar to the wavelet transform. (2) The great difference of the S transform and the wavelet transform is the different presentation of the time-frequency characteristic. The S transform represents signal features in the time-frequency contour. However, the wavelet transform represents signal features in the time-scale contour. Because the S transform uses frequency as a variable, this allows it to have a direct connection and avoids the error in the frequency estimation.  
The most important merit of the S transform is the preservation of both the amplitude and phase information. However, wavelet coefficients only contain amplitude information. Because of these merits, the S transform is used for mechanical fault diagnosis combined with the novel deterministic component cancellation method.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 2 Literature Review and Related Work
### Network Traffic Issues with SDN Based IoT  
Billions of IoT devices depend on IoT services influenced by traffic management within the wireless and 5G mobile network environments. Although many SDN architectures are available for service handling, future SDN-based IoT systems will provide better services.  
The IoT devices exchange the data for transmitting necessary information when different networks merge with the IoT environment. Although 5G IoT provides new challenges and IoT solutions as given in [11], IoT-based 5G networks are being developed to improve services with efficient data traffic management. Thus, modification to IoT applications within future IoT-based 5G networks is possible. Therefore, promising IoT applications must consider the available traffic managing schema to follow enhanced network resources by keeping the delay of services at the minimum rate. The massive amount of data gathered by IoT devices and sensors, etc., need an efficient management technique to improve the communication performance in future smart cities [12]. Despite the self-organizing technique for handover processing, the authors suggested intelligent traffic management. According to [13], relay selection procedures increase the data rate of communication in the IoT. As far as the traffic between the IoT terminals is concerned, traffic management depends on many problems, such as IoT terminals facing inter-cell interference. SDN provides programmability, controllability, flexibility, and management despite billions of objects in the IoT network. Here, the authors analyzed IoT applications  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Schemes**} & **Requirements for Analyzing Data** & **Overall Benefits for Improving Data** \\  & **Traffic of the Transportation System** & **Traffic** \\ \hline NOMA & Power allocations & High spectral efficiency \\  & Sparse coding schemes with & More diversity \\  & non-orthogonal codes & \\ PDMA & Specific pattern division schemes with & Efficient receiver with low complexity \\  & non-orthogonal codes & Better EE \\  & Legacy coding schemes with & Simple and low complex receiver \\  & non-orthogonal codes & Power and code domain \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benefits of 5G-based multiple accessing schemes.  
and generalized IoT-SDN solutions, which simplify the application of wireless capabilities and improve performance.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 3 Azimuth Bias
### Antenna Deviation
\begin{array}{c}\theta_{m,ant}\approx\theta+\theta_{0}-s_{ant}\tan(\varphi) \cos(\theta)+t_{ant}\tan(\varphi)\sin(\theta)\\ +\bigg{(}\frac{1}{2}\sin(\theta)\cos(\theta)-\frac{\sin(\theta) \cos(\theta)}{\cos^{2}(\varphi)}\bigg{)}s_{ant}^{2}-\bigg{(}\frac{1}{2}\sin( \theta)\cos(\theta)-\frac{\sin(\theta)\cos(\theta)}{\cos^{2}(\varphi)}\bigg{)} t_{ant}^{2}\\ +\bigg{(}\cos^{2}(\theta)-\frac{2\cos^{2}(\theta)}{\cos^{2}(\varphi)}+\frac{ 1}{\cos^{2}(\varphi)}\bigg{)}t_{ant}s_{ant}+\cdots\end{array}  \tag{9}\]  
where \(\varphi\) is the ideal elevation of the aircraft. The variable \(\theta\) may be particularized to zero in this case as the antenna only measures the aircraft that are into the antenna main lobe (\(\theta\approx 0\)). Assuming that \(\theta_{0},\ s_{ant}\) and \(t_{ant}\) are small values then the higher order terms may be assumed to be negligible, resulting in:  
\[\theta_{m,ant}\approx\theta_{0}-s_{ant}\tan(\varphi) \tag{10}\]  
The previous measure just contains the projection of the antenna deviation parameters in the azimuth component (\(\theta\approx 0\); noise and other bias terms are neglected for this calculation), resulting in the following additive azimuth bias term:  
\[\Delta\theta_{ant}=\theta_{0}-s_{ant}\tan(\varphi) \tag{11}\]
# Design and Implementation of a Multi-Modal Biometric System for Company Access Control +
###### Abstract  
This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman's method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user's face only or the serial acquisition of both the user's face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent \"response behaviors\" according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database.  
Keywords:data security; adaptive multi-modal biometric system; biometric identifiers; face recognition; iris recognition +
Footnote †: This paper is an extended version of our paper published in the 2 nd International Conference on Data Compression, Communication, Processing and Security (CCPS 2016).
# Steady-Suction-Based Flow Control of Flutter of Long-Span Bridge
###### Abstract
Steady-suction-based flow control, inspired by the aerospace industry, is regarded as a promising open-loop active flow control technique. It is a classical method that improves the aerodynamic performance of the bluff body by changing its aerodynamic shape instead of its physical shape. The suction applied to the surface can cause a global modification of the flow field around the bluff body through interaction with the boundary layer. Thus, the separation of the flow and vortex shedding can be inhibited, and the aerodynamic loading can be greatly reduced. Evidently, modifying the aerodynamic shape is much easier and more economical than modifying the structural shape. Thus, many researchers have conducted further investigations to improve the aerodynamic shape through various methods. Although extensive studies on the suction flow control (mainly for aeronautical applications) have been conducted in recent decades, relatively low effort has been devoted toward exploiting this method to enhance the wind resistance capacity of bridges. [PERSON] et al. [14] successfully controlled the vortex-induced vibration (VIV) of stay cables of cable-stayed bridges by adopting the suction control method. Furthermore, [PERSON] and [PERSON] [15] first proposed the application of the suction control method to mitigate the wind-induced vibrations of a bridge girder. In a follow-up study, they verified the effectiveness of the steady-suction method regarding the improvement of the flutter performance of long bridges based on numerical simulations [16]. However, the necessary experimental verification has not been conducted, and the effect of the installed suction device on the bridge girder is unknown. Therefore, the objective of the present study was to design a steady-suction control system applicable to bridge section models and to investigate the control effect on structural flutter performance through wind tunnel tests. The suction rate of all slots along the span were constant and equal. Weconsidered three experimental variables: the suction slot position, suction interval, and suction rate. The critical flutter speeds of the bridge model with or without a control system were compared to examine the effectiveness and applicability of the self-designed control system. In addition, the measured flutter derivatives (FDs), which are essential for flutter analysis, were compared and analyzed for all test cases to thoroughly examine the control mechanism of the steady-suction flow control.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.8 Chlorophyll Derivatives  
Cluster K (see Figure S17) was identified as that of chlorophylls based on GNPS spectral library matching. The node with precursor ion [M + H]\({}^{+}\) at \(m/z\) 593.277 was annotated as pheophorbide-a (**53**), which has been reported in _C. vespertilionis_[4]. This compound displayed key fragment ions at \(m/z\) 533, 459, and 447. The ion at \(m/z\) 533 was derived from the losses of carbomethoxy group at C-13\({}^{2}\) and an additional proton (CO\({}_{2}\)CH\({}_{3}\)+H), whereas the ion at \(m/z\) 459 indicated the losses of both substituents at C-17 and C-13\({}^{2}\) (C\({}_{5}\)H\({}_{10}\)O\({}_{4}\)) [34]. Furthermore, through careful analysis of its MS/MS fragmentation, the simultaneous losses of both C-17 (C\({}_{4}\)H\({}_{6}\)O\({}_{2}\)) and C-18 (C\({}_{2}\)H\({}_{4}\)) portions of the molecule, along with the additional losses of the methoxy group and the proton at C-13\({}^{2}\) (OCH\({}_{3}\)+H), gave rise to the fragment ion at \(m/z\) 447. With a mass difference of 14 Da (CH\({}_{2}\)) higher than that of **53**, the neighboring node with a precursor ion [M + H]\({}^{+}\) at \(m/z\) 607.292 was elucidated as an \(O\)-methylated derivative of **53**. This compound exhibited significant characteristic fragment ions at \(m/z\) 575, 487, and 447, resulting from the losses of both C-17 and C-18 portions of the molecule, together with the losses of the methoxy group and the proton at C-13\({}^{2}\), which were found compatible with the structure of pheophorbide-amethyl ester (**56**). The MS/MS spectrum of **56** and its proposed fragmentation pathway are presented in Figure S18 and Figure S19, respectively.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 5 Potential Supplements to Enhance Antimicrobial Properties of Bioceramics  
### Peptides  
Antimicrobial peptides have recently received considerable attention for possible use in a number of dental therapeutic applications against oral biofilms [79]. Many studies have identified antimicrobial peptides as the potential next-generation alternative to traditional antimicrobial therapy in the oral cavity [80]. Peptide 1018 and DJK-5 are examples of antimicrobial peptides effective against oral biofilms (Figure 4) [81; 82]. These two peptides were reported to have strong performance in inhibiting biofilm development and eradicating pre-formed oral biofilms [82; 83] not only on an open biofilm platform [84], but also for single- and multi-species biofilms grown in dental tubules [80]. The mechanism of the antimicrobial effect by Peptide 1018 and DJK-5 peptides was by binding to and stimulating degradation the second messenger nucleotide (p)ppGpp that is involved in biofilm formation and maintenance [81; 82]. Both peptides were able to bind onto the hydroxyapatite surfaces [83]. In addition, DJK-5 was also protease-resistant with enhanced antib biofilm activity [83]. Regular endodontic solutions (e.g., sodium hypochlorite, chlorhexidine, and EDTA) were used in combination with these peptides without reported side effects [85; 86]. However, peptides are difficult and expensive to manufacture in large quantities due to the complex processes of their extraction, isolation, and purification [87]. The cost of using peptides as disinfectants would be impractical in the everyday clinical practice. A modified antimicrobial peptide 1018 has been reported to be able to bind to hydroxyapatite with high affinity [88]. Such types of peptides exhibited high antimicrobial and antibiofilm activity against plaque biofilm with significant biofilm volume reduction [88]. Bioceramic materials are calcium-silicate and calcium-phosphate based materials, which are biocompatible and with similar properties to dental minerals. Given the previous evidence of successful application of antimicrobial peptides on biofilms grown on hydroxyapatite and dentin, peptides have great potential to be used as supplements to be incorporated in endodontic bioceramic materials to enhance their antibiofilm properties.
## 4 Experiments and Results
### Results
#### 4.4.2 Ablation Experiment  
We conducted ablation experiments using the DA-CBAM, ILM, and APL functions to better demonstrate how each module affected the classification model outcomes. This paper's experiments used the cross-entropy loss function instead of the APL function, as illustrated in Table 4. The first row shows the outcome of using only the backbone model InceptionResnetV2, and the evaluation indexes like ACC and F1-score were slightly improved after adding the DA-CBAM. However, the results were not significantly improved because the problem of dataset imbalance had not been resolved yet, and it also failed to capture the interclass feature connection. A substantial 1-6% improvement was seen for all the measures when APL was added to the DA-CBAM. All the measures did not significantly improve when the ILM was added to the DA-CBAM, indicating that proper data balancing improved the experimental findings. The results of the experiment with the ILM but without the DA-CBAM were lower because the ILM and DA-CBAM had to collaborate to enhance the system's performance. The system's overall performance suffered when the ILM was utilized alone. The confusion matrix in Figure 8 for the experiment, including an APL, clearly showed that the classification results for the category with fewer samples greatly improved. Additionally, the results greatly improved when the DA-CBAM and ILM were put together. However, when the backbone model was introduced with the ILM alone, the results marginally declined. This was because by complementing each other's shortcomings and offering extra information, the two modules increased overall system performance and substantiated the need for concurrently extracting intra- and interclass features.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ACC** & **AUC** & **F1\_Score** & **Final\_Score** & \(\kappa\) & **HL** \\ \hline InceptionResnet-V2 & 91.29 & 93.62 & 77.62 & 77.59 & 55.52 & 8.70 \\ InceptionResnet-V2 + DA-CBAM & 91.40 & 93.16 & 77.89 & 75.70 & 56.06 & 8.60 \\ InceptionResnet-V2 + ILM & 90.42 & 92.70 & 74.28 & 72.00 & 49.05 & 9.58 \\ InceptionResnet-V2 + APL & 92.90 & 94.61 & 84.10 & 82.31 & 68.22 & 7.10 \\ InceptionResnet-V2 + DA-CBAM + APL & 92.81 & 94.54 & 83.25 & 81.44 & 66.54 & 7.19 \\ InceptionResnet-V2 + ILM + APL & 90.53 & 92.70 & 74.59 & 72.30 & 49.66 & 9.5 \\ InceptionResnet-V2 + DA-CBAM + ILM & 91.77 & 93.68 & 79.18 & 77.14 & 58.56 & 8.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ODIR ablation experiments.  
Figure 8: Ablation experiment confusion matrix of OIA-ODIR.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 4 Algorithm Design
### Algorithm Design Based on Greedy Strategy  
Greedy strategy is a simple and rapid method to solve the problem. The characteristics of the greedy algorithm are step by step. The greedy algorithm does not consider the global optimal solution, but is only based on the current situation, and uses a certain measure to optimize the selection. Although the greedy algorithm cannot obtain the optimal solution in most cases, the greedy algorithm can obtain a 'fairly good' solution with a lower time complexity. For this problem, the most critical step is to select a fixed standard in each step of optimization selection. By selecting a fixed standard, it is necessary to be able to obtain a 'not too bad' solution at the end. The steps of using the greedy algorithm to solve this problem are as follows:  
Step 1: Initialize the parameters, including the number of nodes \(I\), the node set \(nbr(i)\), the export node \(i_{e}\), the number of data packets \(J\), the base station \(B_{j}\) to which the data packet \(j\) belongs, the arrival time of the data packet \(j\)\(A_{j}\), the deadline \(D_{j}\), and the maximum buffer queue length of the base station \(Q_{i}\);  
Step 2: Sorting the data packets according to the absolute deadline in ascending order and selecting the first packet after sorting to start processing;  
Step 3: If the current packet does not reach the export node \(i_{e}\), save the current situation to the stack. Otherwise, skip to step 8;  
Step 4: Select the base station that is adjacent to the current base station and has not been visited, and add it to the set to be selected. If the set to be selected is empty, the current base station is marked as visited, and the situation is recovered from the stack and jumped to step 2; if the set to be selected is not empty, the base station with the smallest load is selected as the next-hop base station;  
Step 5: The arrival time is obtained based on the current load of the base station. If the buffer of the next-hop base station at the arrival time is full, the arrival time + 1, jump to step 5;  
Step 6: Update \(c[T][I]\) and \(s[J][T][I]\) according to the base station number and arrival time of the next hop, and jump to step 3;  
Step 7: Select a packet after the current packet; jump to step 3;  
Step 8: Output results.
## 2 Laser Processing Method and Control System Overall Scheme for MCR Frequency Modulation
### Overall Structure of Laser Processing Control System for MCR Frequency Modulation  
The laser processing process for frequency modulation is briefly depicted in Figure 1. The MCR to be processed is placed on the working platform. The computer control system is used to control the pulse laser to produce the laser with appropriate energy density. After adjusting the scanning mirror and focusing mirror, the laser beam acts on the silver layer on the electrode surface of the MCR so that the silver layer is gasified and the mass of the MCR is changed, changing the resonant frequency of the MCR.  
The layout of the laser processing system for MCR frequency modulation is depicted in Figure 2. The carrier platform transports the MCR wafer from the feeding and discharging position to the processing position. The frequency detection of the MCR is carried out by the frequency detection probe. According to the frequency detection results, the laser output is controlled and the frequency of the MCR is continuously adjusted. After the frequency modulation of the current row of the MCR is completed, the laser processing system automatically enters the next row of frequency modulation operation until the MCR frequency modulation of the whole wafer is completed, and the carrier platform returns to the reclaiming and discharging position.  
YanXiang IPC-620H is selected as the industrial computer of the motion control system. YanHua PCI-1245E series motion control card is selected as the motion control card of the motion control system, which uses DSP and FPGA as the control unit and can communicate with the industrial computer through PCI bus. X-shaft and Y-shaft linear modules use Jacobs dgl-200. The Z-shaft uses a stepper motor and ball screw, and its model is LEX25 RC-50 MG-SAN1. GaoChuang servo driver CDHD-4D5 is selected for a linear motor. The  
Figure 1: Schematic diagram of laser processing for frequency modulation.  
Figure 2: Layout of laser processing system for MCR frequency modulation.  
control driver of Z-shaft lift motor is IAI servo driver DS-S-C1. The GLPN-M fiber laser of IPG laser company is used as the laser processing device. The power, frequency and scanning galvanometer of the laser are controlled by LMCFIBER control card. The linewidth of the laser is within 20 \(\upmu\)m. The pulse width is 1.5 ns. The laser wavelength is 532 nm. The pulse energy is 16 \(\upmu\)J. The beam quality is less 1.2 M\({}^{2}\). The maximum output power of the laser is 15 W. The pulse frequency of the laser is adjustable from 100 KHz to 1200 KHz.