esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 1 Introduction
In summary, how to be able to rapidly and accurately re-route and guide the traffic back to equilibrium after an emergency is a key issue facing traffic relief. The main contributions of this paper are as follows:  
1. A hybrid control architecture that combines network-wide traffic flow data (macro information) and travelers' real-time OD data (micro information) is proposed, which provides the system with emergency-compatible and more accurate real-time traffic load for dynamic road networks. 2. A spatio-temporal neural network-based traffic equilibrium assignment model (ST-ETA) is given, mapping the equilibrium assignment aiming at system optimization (SO) into a neural network. The problem of real-time path planning for emergency scenarios in large-scale real road networks is solved by learning the mapping relationship between the current traffic condition and the optimal equilibrium distribution, instead of an iterative approach. The beginning of this paper lists the current research related to path planning based on traffic assignment and traffic forecasting. The second part will briefly describe the principles of equilibrium traffic assignment and the theoretical basis for hybrid control path planning. The third section details the hybrid control architecture. In the fourth part, a series of experiments and result analysis are given to verify the model validity. In the last part of this paper, we conclude and put forward the future research work.
## 3 Packet Detection Algorithms
### Preamble-Based Algorithms
#### 3.2.1 Auto-Correlation Algorithm  
The auto-correlation algorithm uses the structure of the short preamble and takes advantage of its periodicity [14; 16; 17; 18; 19; 20; 21]. The 10 repetitive short symbols in the short preamble have good auto-correlation, which are not available in noise and other signals. The algorithm framework is shown in Figure 9. Although this method also uses two sliding windows, instead of energy calculation, window \(C\) is used to calculate the correlation between the received signal and a delayed version by Equation (6). When there is only noise, the correlation of noise samples is almost zero since noise does not have auto-correlation characteristics. When the packet is present, the correlation of the identical short preamble is high. The delay \(z^{-D}\) is equal to the period of one symbol length. For IEEE 802.11 ah, \(D\) = 16, which is the period of a short training symbol. Window \(P\) is used to calculate the signal energy during the correlation window to normalize \(m_{n}\) by Equation (7). The lengths of both windows are \(L\). Further, both \(c_{n}\) and \(p_{n}\) are squared to ensure that \(m_{  
Simulation. The simulation result is shown in Figure 10. The window size \(L\) is 16. \(m_{n}\) is high during the whole STF period from index 101 to 260 and remains low before the start of the packet since there is only noise. The difference in response from the double sliding window is caused by the \(c_{n}\) value. When the packet is present, the correlation result \(c_{n}\) of the identical short preamble is high, so \(m_{n}\) jumps to its maximum value. the auto-correlation algorithm only uses the periodicity of the preamble, and there is no need to know the specific content. However, if the structure of the preamble is available, the following method is another good choice.
# A Low-Phase-Noise 8 GHz Linear-Band Sub-Millimeter-Wave Phase-Locked Loop in 22 nm FD-SOI CMOS
## 2 System Architecture and Circuit Design
### I/Q Vco  
The circuit configuration of the wideband I/Q VCO is shown in Figure 2. The resonator, which determines the output frequency of the VCO, was designed using grounded coplanar waveguides TL1-TL2, TL7-TL8, and the parasitic capacitances of transistors M1-M8. Transistors M1-M4 form the core of the VCO, which consists of two cross-coupled oscillators. Quadrature signal generation is achieved by the connection of transistors M5-M8. Consequently, balanced in-phase (I) and quadrature (Q) signals are obtained at the output of the VCO. The VCO outputs are isolated from the subsequent stages using buffers built by transistors M9-M12 along with grounded coplanar waveguides (CPWGs) TL5-TL8. A DC current of I\({}_{0}\) = 5 mA is used to bias the VCO through a current mirror configuration. The size of the biasing transistors is small enough to keep the resonance frequency of the resonator unchanged and maximize the output voltage swing. As a result, an output differential peak voltage of 550 mV is obtained for a supply voltage VDD of 1.5 VDC. Due to the significance of the parasitic capacitances of MOSFETs at mm-wave frequencies, the current tuning is more practical than varactor tuning in the mm-wave VCO design. The MOSFET gate-source capacitance is related to its drain-source voltage or drain current [12]. Therefore, a change in the bias current results in a change in the parasitic capacitances of M1-M12, which dominate the total capacitance of the resonator. Thus, a current-tuning network is constructed using the transistors M13-M16 and resistors R1-R2. A DC voltage is applied to the gates of the tuning transistors, producing drain currents that are added to the fixed bias current from the current mirror to feed the QVCO core transistors. The usage of the tuning transistors is crucial to isolate the VCO core from the tuning DC voltage source as well as to ensure the oscillation by reducing power loss trough tuning. Moreover, a differential tuning voltage is applied across VCTRL+ and VCTRL- to obtain higher linearity and a wider tuning frequency range. To obtain the positive tuning, for instance, VCTRL- is maintained at a fixed low DC voltage point and VCTRL+ is tuned between VCTRL- and VDD. Negative tuning is obtained by swapping the VCTRL+ and VCTRL- from the positive tuning scenario. As a result, a wide frequency range \(\Delta\omega\) = 10 GHz and a phase noise less than \(-\)100 dBc/Hz @ 100 KHz were obtained at the output of the I/Q VCO.
###### Abstract  
Gene families involved in specialised metabolism play a key role in a myriad of ecophysiological and biochemical functions. The _Vitis vinifera_ sesquiterpene synthases represent the largest subfamily of grapevine terpene synthase (_VviTPS_) genes and are important volatile metabolites for wine flavour and aroma, as well as ecophysiological interactions. The functional characterisation of _VviTPS_ genes is complicated by a reliance on a single reference genome that greatly underrepresents this large gene family, exacerbated by extensive duplications and paralogy. The recent release of multiple phased diploid grapevine genomes, as well as extensive whole-genome resequencing efforts, provide a wealth of new sequence information that can be utilised to overcome the limitations of the reference genome. A large cluster of sesquiterpene synthases, localised to chromosome 18, was explored by means of comparative sequence analyses using the publicly available grapevine reference genome, three PacBio phased diploid genomes and whole-genome resequencing data from multiple genotypes. Two genes, _VviTPS04_ and _-10_, were identified as putative paralogues and/or allelic variants. Subsequent gene isolation from multiple grapevine genotypes and characterisation by means of a heterologous in planta expression and volatile analysis resulted in the identification of genotype-specific structural variations and polymorphisms that impact the gene function. These results present novel insight into how grapevine domestication likely shaped the _VviTPS_ landscape to result in genotype-specific functions.  
g rappeine; terpene synthase; sesquiterpene; genotypic variation; gene structure Article
###### Abstract
* There are various statistical approaches utilised in research, such as the gamma process, hidden Markov model, regression-based model, relevance vector machine (RVM), and numerous more. Data-driven approaches rely on the analysis and utilization of historical run-to-failure data. These strategies are frequently employed to make estimations by relying on a pre-established threshold for failure. The use of a wavelet packet decomposition technique and hidden Markov models (HMMs) can be beneficial, as the incorporation of time-frequency information enables more accurate outcomes compared to solely considering temporal factors. Nevertheless, the methodologies that rely on historical data for asset lifespan prediction necessitate a comprehensive understanding of the asset's physical characteristics [9; 10]. This paper employs the N-CMAPSS dataset, which provides simulated run-to-failure data for aircraft engines under real flight conditions. This paper describes a new way to make predictions that combines bootstrap forest and hyperbolic tangent-boosted neural networks. It is meant to fill in the gaps in current methods for predicting when aircraft systems will need maintenance. Despite the success of traditional prognostic approaches, their capacity to manage complex, nonlinear deterioration patterns in aviation components remains restricted. The proposed method improves the accuracy and reliability of predictions by combining the collective power of bootstrap forests with the adaptive learning skills of hyperbolic tangent neural networks. This hybrid model greatly enhances the accuracy of predictions for the RUL in different operational settings. This is a crucial improvement compared to previous models, which often face difficulties in dealing with the changing characteristics of aircraft system data. It offers a more precise and dependable approach to forecasting the RUL of crucial components. This facilitates the implementation of improved maintenance methods and helps to ensure s wafer and more efficient aircraft operations. This work specifically focuses on modelling the deterioration of selected fault types, which flow and efficiency modulation can accurately represent. It acknowledges that this limitation is important to ensure the precision and application of the model. The structure of the current paper is as follows: the first chapter offers a comprehensive review of the prognostic methods currently employed in the field of RUL prediction. Chapter two provides an overview of the suggested data-driven prognostic approaches and their underlying assumptions. Chapter three presents a comparative examination of a hyperbolic tangent integrated neural network, which uses datasets from aviation engines and other data-driven techniques. Chapter four provides an overview of the RUL prediction's results. The conclusion is outlined in chapter five.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 3 Cost Model
### Cost Components in a Cloud
#### 3.3.2 Energy and Cooling  
The OPEX also includes the power utilized by servers and switches as well as the power utilized to cool the heat generated by the servers and switches. For this purpose, a power usage efficiency (PUE) of 1.8 is utilized as in [34].  
\[\text{Annual Electricity \& Cooling}=\text{PUE}\times\text{E}\times 24\times 365 \times(N_{S}\times P_{S}+N_{SW}\times P_{SW}) \tag{7}\]  
This helps estimate the power consumed by the equipment as well as the power required for cooling and the contribution to the annual OPEX. Electricity prices for medium scale industry customers have stayed roughly steady in Finland over the past decade, and a fixed price of 0.1 EUR/kWh is used [32].
## 5 Comparison of Different Decomposition Methods
### Selection Criteria  
Quantum computers are currently limited by the error rates and decoherence of qubits [4], and the longer the circuit, the higher the chance of errors will become. Therefore, the selection is based on circuit length, although the decomposition algorithm is only tested with perfect qubits on a simulator for now. In accordance with the motivations laid out in Section 2, only exact decomposition algorithms are considered.  
For all decomposition methods, the number of gates resulting from the decomposition is only dependent on the number of qubits affected by the unitary gate. Thus, for generic \(n\)-qubit unitary gates, the resulting circuit length can be calculated from the size of the input matrix.  
To measure the length of the resulting circuit, the number of CNOT gates will be used. There are several reasons for that. The first is that not all papers distinguish between generic one-qubit gates and rotation gates. The decomposition of a generic one-qubit gate takes three rotation gates (see Section 5.3), so the comparison might be a factor of three off if one-qubit gates are used to judge circuit length. The CNOT gate is used as the result for all decomposition methods and always has the same definition. This makes it a good metric for the total circuit length.  
Secondly, each CNOT can generate entangled states between qubits [14], and for execution of the circuit on (near-term) quantum devices, each CNOT between non-neighboring qubits might introduce additional mapping operations [5]. Thus, to reduce mapping in the future, a circuit with as few CNOTs as possible is desired.  
Thirdly, the error-rates for two-qubit gates are currently considerably higher than for one-qubit gates [4]. Thus, the chance that an error occurs in a circuit becomes much  
Figure 4: Decomposition of an \(F_{4}^{3}(R_{a})\)-gate.  
bigger with more CNOTs. Thus, to make the decomposition feasible for near-term quantum applications, it is not only important to keep the circuit-length low but especially the CNOT count.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 7 Design Steps of QWL Antenna  
The design of the QWL antenna can be initially guided by the following steps:  
1. Determine the operating frequency range of the antenna.
2. Set \(\frac{Z_{0}}{2}=50\)\(\Omega\) in Equation (1) to define the fundamental parameters of the antenna.
3. Design the three-dipole array director according to the design principles discussed in Section 4. Following Yagi antenna design guidelines, ensure that the length of each metal rod in the directors does not exceed half of the wavelength at the lowest operating frequency.
4. Design the W-shaped reflector according to the design principles discussed in Section 4.
5. Design the voltage balun using the method described in [27].
6. If implementing a series-fed array antenna, design the series feeding according to the method outlined in Section 6.
7. Design the low-common-mode-current connector based on the method provided in Section 5.
8. Finally, optimize the antenna's design based on the specific application requirements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Bandwidth (\%)** & **Efficiency (\%)** & **Size (\(\lambda_{e}^{3}\))** & **Process** & **Wind Load** & **Gain (dBi)** \\ \hline
[38] & 32.7 & 90–92 & 0.88 \(\times\) 0.88 \(\times\) 0.29 & PCB + Metal & high & 8.0–9.0 \\ \hline
[39] & 35.3 & NG & 0.71 \(\times\) 0.62 \(\times\) 0.29 & PCB & low & 6.5 \\ \hline
[40] & 26.7 & NG & 0.73 \(\times\) 0.44 \(\times\) 0.10 & PCB + Metal & high & 5.1–8.0 \\ \hline
[41] & 55.3 & NG & 0.93 \(\times\) 0.93 \(\times\) 0.25 & PCB & high & 8.2–9.1 \\ \hline
[42] & 31.6 & 87–92 & 1.05 \(\times\) 0.66 \(\times\) 0.22 & Metal + Plastic & high & 9.8 \\ \hline
[43] & 5.4 & NG & 1.83 \(\times\) 1.83 \(\times\) 0.54 & All-metal & low & 15.1 \\ \hline
[44] & 14.4 & NG & 2.48 \(\times\) 0.67 \(\times\) 0.23 & All-metal & low & 12.1 \\ \hline
[45] & 25.1 & NG & 1.63 \(\times\) 0.69 \(\times\) 0.23 & All-metal & low & 10.1 \\ \hline This antenna & 19.9 & 93–100 & 1.37 \(\times\) 1.22 \(\times\) 0.56 & All-metal & low & 14.2 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the series-fed QWL antenna array with other antennas.  
Figure 13: Simulated and measured patterns of series-fed WHEMS array antenna at 750 MHz. (**a**) E-plane. (**b**) H-plane.
## 2 Results and Discussion
### Fingerprinting of Cinnamon NMR Dataset  
NMR-based metabolomics study is recognized as a direct analytical platform and always imparts more credibility to identification results owing to its strong elucidation power aside from being quantitative (qNMR) providing a readout out of metabolite abundance in extracts [20; 43]. A total of 16 metabolites were identified based on 1D- and 2D-NMR analyses. The peak assignment is summarized in Table S2, most of which were previously identified in _Cinnamomum_ sp. from our previous report [44]. In the present study, \({}^{1}\)H-NMR analyses were carried out to characterize and quantify major metabolites in authenticated cinnamon species, i.e., CV, CA, and CI, and commercial products. Signal assignments were further confirmed using 2D-NMR experiments, including \({}^{1}\)H-\({}^{1}\)H COSY (correlation spectroscopy), \({}^{1}\)H-\({}^{13}\)C HSQC (heteronuclear single quantum coherence spectroscopy), and \({}^{1}\)H-\({}^{13}\)C HMPC (heteronuclear multiple bond correlation). Two main regions appeared in the \({}^{1}\)H-NMR spectra (Figure 4): an up-field region (\(\delta_{\rm H}\) 0.5 to 5.5 ppm) belonging mostly to primary metabolites, namely, fatty acids (N1), glycerol (N2), and sugars (N3-N6) and a down-field region (\(\delta_{\rm H}\) 5.5-9.5 ppm) assigned to secondary metabolites, i.e., cinnamic acids (N7-N8), cinnamaldehydes (N9-N11), protocatechuic acid (N12) and coumarin (N13).  
Figure 4: \({}^{1}\)H-NMR spectra of cinnamon samples showing characteristic signals for primary and secondary metabolites in spectral region (\(\delta_{\rm H}\) 0–10 ppm). Signal numbers correspond to those listed in Table S2 for metabolite identification using \({}^{1}\)H-NMR.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 4 TBD Algorithms Based Deep Learning
### TBD Algorithm Based on Deep Learning Object Tracking
#### 4.2.3 SORT Tracking vs. LSTM Tracking  
This section compares the SORT-based tracking module and the LSTM-based tracking module. In order to maintain fairness, we use the general (public) object detector Faster R-CNN to analyze and compare the results of the MOT17 test set as shown in Table 4, the performance of the deep learning-based visual multi-object tracking algorithm is explored and verified as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & **Methods** & **Detection** & **Year.** & **HOTA** & **MOTA** & **IDF1** & **IDs** & **FPs** \\ \hline
[50] & SORT & Public & 2016 & 34.0 & 43.1 & 39.8 & 4842 & 143.3 \\
[51] & DeepSORT & Private & 2017 & 54.4 & 60.3 & 61.2 & 2821 & 20.8 \\
[57] & eHAF17 & Public & 2018 & — & 51.8 & 54.7 & 1843 & 0.7 \\
[69] & OCSORT & Public & 2021 & 52.4 & 58.2 & 65.1 & 784 & 28.6 \\
[59] & StrongSORT & Private & 2022 & 64.4 & 79.6 & 79.5 & 1194 & 7.1 \\
[65] & MHT\_bLSTM & Public & 2018 & 41.0 & 47.5 & 51.9 & 2069 & 1.9 \\
[68] & BLSTM\_MTP & Public & 2021 & 41.3 & 51.5 & 51.9 & 2566 & 20.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The tracking results of the algorithm based on the TBD framework on the MOT17 test set [25].  
The LSTM-based tracker and SORT-based tracker clearly differ from one another, as seen in the above table. While the LSTM tracker performs better when handling occlusion issues, the SORT tracker has an overall edge in tracking speed. Additionally, when the network model's level of detail increases, researchers include various sub-modules with particular roles in the tracker. For instance, StrongSORT augments the DeepSORT tracker with the AFlink and GSI modules, significantly enhancing object tracking's accuracy and precision. However, because of the bloated network structure, the algorithm's processing speed has significantly decreased--to just 7.5 Hz--making it challenging for it to satisfy the demands of real-time applications such as autonomous driving. The detection-based tracking framework is still the most widely used method today, however the goal of research should be to achieve real-time precise tracking of objects without compromising tracking accuracy and precision.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 3 Chromatography
### Gas Chromatography  
Aflatoxins are semi-volatile compounds permitting analysis by GC. Unfortunately, GC-based aflatoxin analysis has never achieved wide attention. When capillary columns were not available, one could not use GC to analyze aflat due to insufficient separation of aflatoxin B1, B2, G1 and G2 on pack columns. The advent of capillary columns, however, encouraged researchers to explore this chromatographic tool for aflatoxin analysis. In 1981, [PERSON] [297] developed a GC-MS method for the determination of aflatoxin B1, in which aflatoxin B1 was introduced into the GC via on-column injection, followed by the separation on a fused silica capillary column coated with a nonpolar phase (SE-30) and detection using MS. Without chemical derivatization, experiments required an on-column mass of 10 ng for detection and no food samples were analyzed. Two similar studies were reported by [PERSON] et al. [251] and [PERSON] et al. [298], in which aflat were determined by the MS detector coupled with the GC but separated on different GC capillary columns. [PERSON] et al. [299; 300] reported the analysis of aflat using GC-FID. In these studies, GC-MS and GC-FID were manually evaluated as a qualitative tool for the confirmation of aflat in suspected samples. Due to the lack the simplicity of TLC and the sensitivity of LC-FLD, GC-based aflatoxin analysis has not been extensively evaluated compared to TLC and LC-FLD. Currently, given the increasing use of LC-MS for mycotoxin analysis, it is difficult to see that there will be a resurgent interest in GC as there are already many LC-FLD and LC-MS methods developed for the determination of aflat in various food matrices.