esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Defects Using Spatial Variances of Guided-Wave Modes in Testing of Pipes
## 3 Methodology
### General Routine  
The first step in the general propagation routine of guided wave devices is to cancel the signal received from the backward direction. For doing so, the signals received from Ring 2 are initially subtracted from Ring 1 and Ring 3, and then the results are phase-delayed with respect to the tool set's ring distancing and the excitation wave mode dispersion curve (T(0, 1) in this case). The results are stored in the Signals matrix:  
\[\mathrm{Signals[i,\ n]}=\left\{\begin{array}{ll}\mathrm{PhaseDelayed(Ring1- Ring2),\ for\ i\ \leq\ NS/2}\\ \mathrm{PhaseDelayed(Ring3-Ring2),\ for\ i\ >\ NS/2}\end{array}\right. \tag{1}\]  
where n represents the sample number, i is the sensor number, and NS represents the total number of achieved signals, which is twice the number of transducers in each ring. This method will result in T(0, 1) components to be overlapped. For the signals received from the forward test direction, this overlap will be positive, and for the backward direction, the amplitudes are inverted, which allows the unidirectional inspection. The temporal domain signal, which is generally used for inspection, is achieved by taking the mean of the Signals:  
\[\mathrm{temporal[n]}=\frac{1}{\mathrm{NS}}\sum_{i\ =\ 1}^{\mathrm{NS}} \mathrm{Signals[i,\ n]} \tag{2}\]
# Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo
## 2 Simultaneous Quantile Regression
### Regression Quantile  
We begin with a fundamental definition of sample quantiles, a concept readily extended to the linear regression model without the typical need for an ordered set of sample observations. Consider the set \(y_{t}:t=1,\ldots,T\) of independent and identically distributed (i.i.d.) observations on a random variable \(Y\) governed by the distribution function \(F\). The \(\tau\)-th sample quantile, where \(0<\tau<1\), can then be characterized as any solution to the minimization problem:  
\[\min_{\beta}\sum_{t}\rho_{\tau}(y_{t}-\beta),\]where \(\rho_{\tau}\) is the check loss function, defined as  
\[\rho_{\tau}(u)=u(\tau-\mathds{1}(u<0)). \tag{3}\]  
Equivalently, we may write the minimization problem as  
\[\underset{\mathbf{\beta}\in\mathbb{R}}{\text{min}}[\sum_{t\in\{t:y_{t}:\beta\}} \tau|y_{t}-\mathbf{\beta}|+\sum_{t\in\{t:y_{t}<\mathbf{\beta}\}}(1-\tau)|y_{t}-\mathbf{ \beta}|].\]  
[PERSON] et al. [29] observed the difficulty in identifying outliers within the context of regression, emphasizing the inherent confusion in extending standard notions of sample quantiles to linear models based on the ordering of sample observations. This confusion is effectively addressed by a direct generalization of the earlier-described minimization problem. Consider a sequence of \(k\)-vectors from a known design matrix denoted by \(\{x_{t},t=1,\ldots,T\}\). Assume that \(\{y_{t},t=1,\ldots,T\}\) constitutes a random sample from the regression process, where \(u_{t}=y_{t}-\mathbf{x}_{t}^{\prime}\beta\) follows a distribution function \(F\) such that \(F(0)=\tau\). The \(\tau\)-th quantile of the linear regression model, with \(0<\tau<1\), is defined as any solution to the minimization problem:  
\[\underset{\mathbf{\beta}\in\mathbb{R}^{K}}{\text{min}}[\sum_{t\in\{t:y_{t}:\mathbf{ \beta}\}}\tau|y_{t}-\mathbf{x}_{t}^{\prime}\mathbf{\beta}|+\sum_{t\in\{t:y_{t}<\mathbf{ \beta}\}}(1-\tau)|y_{t}-\mathbf{x}_{t}^{\prime}\mathbf{\beta}|] \tag{4}\]
# Multi-Layer Feature Fusion-Based Community Evolution Prediction
## 3 Multi-Layer Feature Fusion-Based Community Evolution Prediction  
This section will introduce the MCEP algorithm in detail. As mentioned in the related work section, community evolution prediction is mainly divided into four steps. The main work of this paper is the last step, which obtains the corresponding features.  
The structure of this section is as follows. Section 3.1 will briefly introduce the features used in the MCEP. Section 3.2 will introduce the global and local-based community role-extraction algorithm proposed in this paper. Section 3.3 will introduce the algorithm for discovering the community mesoscopic features. Section 3.4 briefly introduces the prediction process of the MCEP algorithm.  
Before introducing MCEP, we first briefly introduce the related symbols used in this paper.  
In this paper, the dynamic network is defined as \(G=(V_{t},E_{t})\), where \(V_{t}\) is the node in the network at time \(t\), \(E_{t}\) is the edge in the network at time \(t\), \(t=1\), \(2\),..., \(T\), and \(T\) is the number of the snapshot in the network. \(N_{t}=|V_{t}|\) represents the number of nodes in the network at time \(t\). \(C_{t}\) represents the community structure at time \(t\), \(C_{(t,l)}\) represents the \(i\)-th community at time \(t\), and \(Role_{C}\) represents the division of roles in community \(C\). The main symbols used in this article are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(G_{t}\) & The network of snapshot \(t\) \\ \(C_{t}\) & Community set at snapshot \(t\) \\ \(C_{t,l}\) & Community j at snapshot \(t\) \\ \(\xi_{C}\in R^{|C_{t}|\times M}\) & The global feature matrix of community \(C\) \\ \(\ell_{C}\in R^{|C_{t}|\times M}\) & The local feature matrix of community \(C\) \\ \(F_{C}\in R^{|C|\times L}\) & The mesoscopic feature of community \(C\) \\ \(W_{C}\in R^{K\times M}\) & The global feature contribution matrix \\ \(H_{C}\in R^{K\times M}\) & The local feature contribution matrix \\ \(\beta_{C}\in R^{|C_{t}|\times K}\
## 2 System Model
At \(U_{1}\): \(U_{1}\) directly decodes its own signal \(x_{1}\), and the signal \(x_{2}\) and \(x_{3}\) are regarded as interference, the SINR of \(U_{1}\) can be given as \[\gamma_{U_{1}} = \frac{\alpha_{1}P_{s}|h_{U_{1}}|^{2}}{(\alpha_{2}+\alpha_{3})P_{s} |h_{U_{1}}|^{2}+\sigma^{2}}\] (4)
2. At \(U_{2}\): In order to decode \(x_{2}\), \(U_{2}\) first needs to decode \(x_{1}\), then eliminate the interference caused by \(x_{1}\) with the help of SIC technology, and finally decode \(x_{2}\) with \(x_{3}\) as the interference signal. To facilitate the analysis of secrecy outage performance, we posit that \(U_{2}\) exhibits effective decoding \(x_{1}\), thus the SINR of \(U_{2}\) can be given as  
Figure 1: Network model. \[\gamma_{U_{2}} = \frac{\alpha_{2}P_{S}\big{|}h_{U_{2}}\big{|}^{2}}{\alpha_{3}P_{S} \big{|}h_{U_{2}}\big{|}^{2}+\sigma^{2}} \tag{5}\]
3. At \(U_{3}\): Similar to the decoding process of \(U_{2}\), \(U_{3}\) needs to decode \(x_{1}\) and \(x_{2}\) successfully eliminate interference before decoding \(x_{3}\), we assume that \(U_{3}\) can successfully decode \(x_{1}\) and \(x_{2}\), and the SNR of \(U_{3}\) can be given as \[\gamma_{U_{3}} = \frac{\alpha_{3}P_{S}\big{|}h_{U_{3}}\big{|}^{2}}{\sigma^{2}}\] (6)
4. At E: We assume that E has SIC capabilities similar to \(U_{2}\) and \(U_{3}\) to detect multi-user data. \(x_{2}\) and \(x_{3}\) are considered as interference noise when they eavesdrop on \(U_{1}\). \(x_{1}\) is successfully eliminated through SIC, and \(x_{3}\) is regarded as interference noise when it eavesdrops on \(U_{2}\). \(x_{1}\) and \(x_{2}\) are successfully eliminated when they eavesdrop on \(U_{3}\). Thus, we have \[\gamma_{E} = \left\{ \begin{array}{ll}\frac{\alpha_{1}P_{S}\big{|}h_{U_{2}} \big{|}^{2}}{(\alpha_{2}+\alpha_{3})P_{S}\big{|}h_{U_{2}}\big{|}^{2}+\sigma^{2 }},&j=1\\ \frac{\alpha_{2}P_{S}\big{|}h_{U_{2}}\big{|}^{2}}{\alpha_{3}P_{S}\big{|}h_{U_{ 2}}\big{|}^{2}+\sigma^{2}},&j=2\\ \frac{\alpha_{3}P_{S}\big{|}h_{U_{2}}\big{|}^{2}}{\sigma^{2}},&j=3\end{array} \right.\] (7)
# A Systematic Review of Radio Frequency Threats in IoMT
## 3 Results and Discussion
### Cyber Impacts and Layered Attacks  
Criminality is uprooted from cybercrime, regardless of the attacking layer. By understanding cyber impacts at the human and OSI layers, we gained insight into the causes and were able to deploy countermeasure strategies against powerful attacks. Prior to reviewing the physical layer attacks, we will examine layered attacks and cybersecurity frameworks.  
The research [26] examines threats, vulnerabilities, and attacks. Literature reviews, surveys, articles, repositories, attacks, incidents, and more demonstrated how cybersecurity harnesses multiple dimensions of the corporate ecosystem. Cyber harness themes were examined from adversaries' perspectives. According to the research, the taxonomy will enable companies to distinguish between high-value and low-value assets and how they are directly and indirectly associated with cyber-related harms. To improve their cybersecurity management program, the paper analyzed the VERIS community database (VCDB) [27].
## 4 Discussion  
Results presented in Section 3 show the relevance of embedding AI into hyper-constrained devices, highlighting how this is still mostly an open issue. Currently, it is basically impossible to actually make state-of-the-art NNs work on hyper-constrained devices as they are. However, SOTA NNs are thought to be run on powerful machines and tackle complex general tasks. Many, possibly most, tasks to be tackled on embedded devices do not actually need to bear the complexity burden of general tasks. Our result in fact show that it is possible to embed simpler versions of state-of-the-art NNs and custom-built architectures.  
The selected NNs show acceptable performance over a simple classification task that mirrors faithfully realistic scenarios. However, this represents only a partial success. Indeed, the performance of the embedded architectures is still far from ideal, as they are characterised by slow speed, hindering AI applicability. Out of the architectures presented, only three models were capable of producing a single prediction in less than 1 s. That value represents a strong limitation to any real-time application, which may require running multiple inferences in shorter time spans. Indeed, such NNs are reliably applicable only on those applications that require classification to be made one-off. Real-time applications requiring AI techniques should therefore consider less constrained devices, as the ones presented in Table 2. Those limitations represent an open issue of AI techniques, and much research effort is needed to tackle them.  
Results also show that architectures obtained from state-of-the-art NNs are more stable to small, but relevant, variations of the input images. Those architectures benefit from the pretraining of state-of-the-art models, bearing more general information. When considering real scenarios, this feature is desirable. In fact, real-world inputs may differ from the ones available in the training and testing phase. Therefore, we would suggest to consider NN models derived from state-of-the-art, or at least models showing generalisation abilities, whenever it is possible. However, results point out the existence of a tradeoff between generalisation capability and inference speed. Indeed, SOTA-based model are the most capable of generalisation, while being also the slowest--compared to custom-built models. This stresses once more how the application of AI to embedded devices is still an open research issue. The trade-off between performance and speed is not always acceptable, and a simple-yet-powerful NN architecture capable of running on constrained devices is desirable and worth of research efforts.
# Detection of Sub-Aroma Threshold Concentrations of Wine Methoxyprazines by Multidimensional GCMS
## 2 Materials and Methods
### Method Optimization
#### 2.3.3 Single Dimension GC-FID (Flame Ionization Detector) Analysis  
To optimize the first dimension and establish the heart-cutting regions a SH-Rtx-wax (polyethylene glycol) 60 m \(\times\) 0.25 mm ID \(\times\) 0.25 um film thickness (Shimadzu NZ Ltd., Auckland, New Zealand) installed in a Shimadzu 2010 GC-FID (Shimadzu NZ Ltd. Auckland, New Zealand) was used to separate the MPs: IPMP, SBMP and IBMP. Sample vials were stirred and incubated at 50 \({}^{\circ}\)C for 10 min prior to static sampling with the SPME fiber at the same temperature for 40 min. The SPME fiber was desorbed in the GC injection port fitted with a 0.75 mm I.D. SPME liner (SGE Analytical Science Pty. Ltd., Ringwood, Vic, Australia) set to a temperature of 270 \({}^{\circ}\)C for 5 min in splitless mode at an initial flow rate (helium carrier gas, BOC Gas, Christchurch, New Zealand) of 1.46 mL/min, and a linear velocity of 19.9 cm/s. The column oven was initially held at 35 \({}^{\circ}\)C for 3 min and then ramped up to 101 \({}^{\circ}\)C at 7.33 \({}^{\circ}\)C/min, gradually increased to 148 \({}^{\circ}\)C at 1.50 \({}^{\circ}\)C/min before increasing to 250 \({}^{\circ}\)C at 40 \({}^{\circ}\)C/min and held for 16.11 min for a total runtime of 62 min. During the oven ramp the column flow rate was controlled using a pressure program where the pressure was increased from an initial pressure of 218.7 kPa to a final pressure of 328.1 kPa. This pressure program was the total pressure of the system and controlled not only the column flow in the first dimension column, but the resulting column flow in the second dimension column (GCMS) and the switching valve pressure whose optimization was needed to ensure 100% of the first dimension column outflow could be heart-cut onto the second dimension column. The end of the first dimension GC column was connected to a Dean's style switch (Shimadzu NZ, Ltd. Auckland, New Zealand) in the GC oven and, when closed, was connected to the FID detector held at a constant temperature of 250 \({}^{\circ}\)C. The three MPs eluted at 31.14 min (IPMP), 35.66 min (SBMP) and 37.20 min (IBMP), respectively. The two deuterated internal standards eluted at 31.08 min (d\({}_{3}\)-IPMP) and 37.14 min (d\({}_{3}\)-IBMP).
# Widely-Tunable Ring-Resonator Semiconductor Lasers
## 5 A Performance Review of Widely-Tunable Semiconductor Lasers
Heterogeneous approaches, where III/V and Si are bonded prior to processing of III/V, offer an advantage of manufacturability. Such approaches are naturally more scalable if modified complementary metal oxide semiconductor (CMOS) processes are used, and such lasers can be integrated with many other components on a single chip to realize advanced photonic integrated circuits, e.g., fully-integrated beam steerers [2], microwave signal generators, and tracking generators [47]. A laser providing 57 nm of tuning, >45 dB SMSR, and a (integrated) linewidth below 100 kHz in full tuning range, with best results down to 50 kHz and 10 dBm output power (without booster SOA), has been demonstrated [49].
# Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Presurized Hydrogen
## 4 Conclusions  
1. The yield of pyrolysis oil in a hydrogen atmosphere is 59.23%, which is higher than that (40.88%) in a nitrogen atmosphere, and the solid product is reduced from 26.02% in a nitrogen atmosphere to 20.41% in a hydrogen atmosphere. The carbon content of pyrolysis bio-oil obtained under hydrogen is basically unchanged. Oxygen decreased from 21.34% to 17.16%, hydrogen increased from 5.17% to 7.06%, the H/C ratio increased from 0.072 to 0.098, and the heating value increased from 27.75 MJ/kg to 31.40 MJ/kg;
2. In the hydrogenated pyrolysis of bio-oil, the content of compounds with a boiling point of 0-200 \({}^{\circ}\)C is 63.21%, while in a nitrogen atmosphere, the content of components within the boiling point range is 55.11%. Therefore, the low boiling point fraction of  
Figure 7: Comparative analysis of components of pyrolysis bio-oil under different atmospheres.  
pyrolysis bio-oil can be improved in a hydrogen atmosphere, which is conducive to improving the combustion characteristics of pyrolysis oil;
3. By comparison, the biomass pyrolysis oil changed obviously in the hydrogen atmosphere. Ethyl hexadecanoate (peak area percentage 19.1%) and methyl octadecanoate (peak area percentage 15.42%) were obtained. The number of phenols decreased from 33.66% to 28.38%. Pyrolysis in a hydrogen atmosphere inhibited its aromatization and reduced the side chain reaction of the benzene ring. The content of esters increased significantly in the hydrogen atmosphere, and acids increased from 0.45% in the nitrogen atmosphere to 1.65% in the hydrogen atmosphere. It is mainly hexadecanoic acid, nineteenth acid, ethanol, and methanol. Therefore, esterification occurred in the pyrolysis process, resulting in the formation of long-chain esters and reducing the acidity of pyrolysis bio-oil.  
J.W.: Investigation, visualization, original draft preparation; S.W.: Data curation, reviewing and editing; J.L.: Methodology, reviewing and editing; M.Y.: Project administration, reviewing and editing; Y.W.: Supervision, conceptualization, reviewing and editing, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Research Project of Shanxi Provincial Department of Science and Technology (No. 202003D311001), National Natural Science Foundation of China (No. 21838006) and the National Key R&D Program of China (No. 2018 YFC1902101).  
Not applicable.  
The authors declare no conflict of interest.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Differentiator Noise Amplification  
The differentiator circuit exhibits inherent high-frequency noise amplification due to its frequency-proportional gain characteristic. Simulation results demonstrate significant noise amplification, with measured values of 196.52 dB for a 5 Hz input signal. The differentiator output SNR degrades to \(-\)13.06 dB, indicating substantial noise injection into the signal path. An examination of various signal frequencies demonstrates performance degradation that depends on frequency. At 1 Hz, noise amplification reaches 26.80 dB, whereas at 50 Hz, it decreases to \(-\)7.24 dB. This dependency on frequency significantly  
Figure 24: PPM modulating results: (**a**) Traditional technique, and (**b**) Novel proposed approach.  
affects the suitability of the differentiator for different message signal bandwidths, with lower frequencies undergoing more pronounced noise amplification.
## 3 Modelling of Three-Phase Orbital Manoeuvring Method  
### Initial Orbital Model  
For the better functioning of the observation satellite, the low Earth orbit (LEO) was chosen as the initial orbit. The orbital parameters were downloaded from the UCS Satellite Database. The selection of constraints is based on the observation target area to ensure that the orbit covers the latitude region of _+/_ - 20 degrees in the South China Sea, using the orbital parameters given in Table 1.  
Figure 1: Target motion diagram in three-dimensional coordinate system.  
During orbital motion, there are many perturbations in LEO of which the J2 term perturbation of the Earth's oblateness is one of the most dominant long-term perturbations [16]. It directly causes long-term changes in the satellite's Right Ascension of the Ascending Node (RAAN), Argument of Latitude (AOL), perigee radial angle, and flat perigee angle [17; 18; 19]. For more accurate modelling of orbital motion, the J2 effect should be considered, while the remaining latitudinal harmonics are more than 100 times smaller than the J2 perturbation [20]. Therefore, they are not considered in this paper. Other perturbations include atmospheric drag, third body effects, and solar radiation pressure. Atmospheric resistance is the most important [21].  
Set the preliminary orbit as \(\hat{\Omega}_{1}\), argument of perigee as \(\dot{\omega}_{1}\), average rate of abnormal change as M\({}_{1}\), and average angular velocity of the satellite as \(n_{1}\). The period of the initial orbit is established as follows:  
\[T_{p_{1}}=\frac{2\pi}{n_{1}+\dot{\mathrm{M}}_{1}+\dot{\omega}_{1}} \tag{10}\]  
\(\hat{\Omega}_{1}\), \(\dot{\omega}_{1}\), and \(\dot{\mathrm{M}}_{1}\) are expressed as the following:  
\[\dot{\omega}_{1}=\xi n_{1}\Big{(}4-5\sin^{2}i\Big{)} \tag{11}\]  
\[\hat{\Omega}_{1}=-2\xi n_{1}\cos i \tag{12}\]  
\[\dot{\mathrm{M}}_{1}=-\xi n_{1}\Big{(}3\sin^{2}i-2\Big{)} \tag{13}\]  
among which \(\xi=\frac{3R_{e}^{2}J_{2}}{4r_{1}^{2}}\), \(R_{e}\) represents the mean radius of the Earth, and \(J_{2}=1.082626\)\(\times 10^{-3}\)[22].
## 2 Physical Model and Control Equation
### Mesh Setting and Mesh Independence Verification  
In order to enhance the calculation accuracy, mesh refinement has been applied to the outlet area. The naming selection is shown in Figure 2. Assuming that the front end of the pipe constitutes a blockage, it is designated as the \"wall\". The maximum and minimum values of the mesh element quality are in the range of 0-1, and the average value is 0.83924. The maximum aspect ratio is 8.7097, the minimum value is 1.1579, and the average is 1.841. The minimum value of skewness approaches 0, the maximum is 0.67254, and the average value is 0.22574. The above data show that the mesh division quality is good and in compliance with the mesh standard.  
To accurately identify small features in the model, local grid refinement technology is applied at the outlet of the nozzle where the velocity gradient changes significantly, whereas a sparse grid is employed at the outlet of the nozzle where the velocity gradient changes minimally. To validate the grid independence of varying densities and compare the velocity variations at the same position and under the same scheme for different grid numbers, this study examines the velocity at points with coordinates (70, 0, 0) (Figure 2) located at the outlet of the front nozzle, as illustrated in Figure 3. The grid numbers are 340,000, 650,000, 950,000, and 1.77 million, respectively. The results indicate that, after reaching 950,000 grids, there is no notable change in the outcomes with further increments in grid numbers. Consequently, to expedite the numerical calculations, a grid value of 950,000 is utilized for the simulation.  
Figure 2: Mesh division and boundary condition naming.