esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Results
### Normal External Disturbances  
Under the assumption of the aforementioned parameters and external disturbances, there are no other external disturbances.  
The fractional-order PMSM is obviously a nonlinear chaotic system. In Figure 2, the trajectory in the diagram is densely entangled in the three-dimensional space \((x1,x2,x3)\) and staggered without repeated routes. There is no periodic ring structure, the state diffuses in the phase space after adding external interference after 10 s, and the position of the limit cycle is shifted. It can be seen that the system is sensitive to disturbance, which will cause the trajectory to deviate from the original path quickly during operation. Therefore, Figure 2 depicts the phase trajectory of the nonlinear power of the system at the tenth second when a disturbance occurs, with an initial state of zero. The results demonstrate that the fractional-order PMSM exhibits aperiodic, irregular, and nonlinear oscillations, leading to an instability that significantly impacts unit operation safety and stability. The three-dimensional phase trajectory coordinates exhibit typical chaotic characteristics. Figure 3 displays the fuzzy of uncertain nonlinear functions \(g_{i}(x)\). Figure 4 shows that the sliding-mode surfaces \(s_{i}\) of each state function approach zero. Control inputs based on SMC with different switching functions are shown in Figure 5, from which we can discover that control inputs with sign switching function have dramatic positive and negative changes. At the same time, control inputs with exponential switching function have smoother changes after adding disturbances. There is a smooth and continuous control curve even after the addition of perturbations based on the fuzzy switching function in this paper. The control inputs in this paper are the mildest that best perform the tracking control and have minimum damage to the device.  
Figure 4: The sliding-mode surfaces \(s_{i}\) in this paper.  
Figure 3: The fuzzy function of the nonlinear terms \(g_{i}(x,t)\).  
Figure 2: Phase trajectories of the fractional-order PMSM.  
As shown in Figure 6d, all three states can follow the desired trajectory \(sin(t)\), while the tracking effects of the three methods are different in detail from the tracking error analysis of the three states in Figure 6a-c. In one second, all three ways can achieve good tracking effect, and the adjust time of the above three methods is less than 0.2 s, which is in a normal range. Although each error with fuzzy switching functions in this paper has a large adjust time compared with the other two, the error with fuzzy switching function has the minimum value, and after adding the perturbation, the error does not change. However, after adding the disturbances, errors with exponential switching function have a great influence, and the jitter is increased slightly. Generally speaking, the SMC in this paper gives the best tracking results.
## 3 Discussion
### _O. officinal_
Similarly, most proteins previously functionally identified from the angiosperm-specific TPS-b clade are monoterepense synthases. For example, six _A. thaliana_ TPS-b proteins clustered in the same branch that harbored AtTPS10, a monoterepense synthase that produces myrcene or ocimene [12]. These findings indicate that the TPS members share a similar functional feature within the same subfamily, however, further functional characterization is required. The expression analysis showed that _DoTPS_ genes were mainly expressed in floral organs of _D. officinal_, followed by root organs and stems (Figure 6), inferring a strict regulation of terpenoid production. Interestingly, 11 of 18 _DoTPS-b_ genes with high transcript levels in floral organs were monoterpene synthase genes (Figure 6). Four monoterpene synthase genes (_DoTPS10_, -19, -20, and -25) were highly expressed at the semi-flowering stage (Figure 9), in agreement with the content of geraniol and linalool (Figure S2). Among them, _DoTPS10_ showed the highest transcript level in floral organs (Figures 6 and 9), suggesting that it may be responsible for the biosynthesis of monoterpenes. In the present study, DoTPS10 was shown to be a single-product enzyme that could covert GPP to linalool (Figure 10), the predominant component of floral scents in orchids. TPS proteins that produce the same single product have also been found in a _Freesia_ hybrid [(21)], _Malus domestica_[(16)] and _Vitis vinifera_[(19)]. Furthermore, the majority of _DoTPS_ genes could be induced by MeJA treatment, resulting in the increased production of monoterpene volatiles such as geraniol and linalool (Figure 8). The reason why exogenous MeJA resulted in the upregulation of these _DoTPS_ genes may be due to the presence of G-boxes in their promoters (Table S13), which can interact with the existing CGTCA or MYC motif of the jasmonic acid signaling pathway [(37)], but it needs to be further explored. Therefore, activated expression of _DoTPS10_ by MeJA treatment offers a critical cue for further exploring the mechanism of linalool biosynthesis in _D. officinal_. RT-qPCR data showed that distinct tempospatial expression profiles of _DoTPS_ genes could be affected when _D. officinal_ was exposed to cold or osmotic treatment (Figure 7). Similarly, previous work has emphasized the importance of terpen in defensive response to biotic attack and abiotic stresses [(1; 2; 3; 4; 5; 7; 9; 10)]. It is possible that the crassulacean acid metabolism plant _D. officinal_, which adheres tightly to the surface of tree bark or rocks in locations with limited soil, thus requires TPS proteins to quickly biosynthesize terpenes to circumvent adverse environments [(23; 25)]. To better understand the mechanisms of differential terpenoid production in _D. officinal_, more efforts should be made to integrate studies on _DoTPS_ expression patterns with those on profiling of terpenes.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Signal Processing
#### Pressure Point Analysis  
Pressure point analysis (PPA) is one of the more popular system diagnosis methods that are available [19]. The PPA method detects leaks by monitoring pipeline pressure at a single point along the line and comparing it with a running statistical trend constructed from previous pressure measurements. The combination of selective filtering and software thresholds determines whether the behavior of successive measurements contains evidence of a leak. The PPA method just requires pressure signals from one or several detection points, so the advantages of this method are cost-effectiveness and easy maintenance. Additionally, PPA can detect small leaks which cannot be detected by other methods. However, it is difficult for this method to localize leak points. Therefore, the application range of PPA is too narrow.
## 1 Introduction
Most existing research has been constrained to same-component-same-testbed [23; 24] and component-different-testbed [25; 26]. A few scholars have performed cross-domain experiments of different-component-same-testbed [27], but the model has not performed well in the new field, severely restricting its application in real-world working situations. Currently, most studies directly use 1D vibration signals for classification, while 2D images provide more information that allows the model to achieve higher recognition accuracy [28; 29]. Given that the fault classification and image recognition processes are extremely similar, it is highly feasible to introduce computational methods from the field of image processing into fault diagnosis [30]. For instance, [PERSON] et al. [31] suggested a capsule network made by switching the starting blocks and utilizing the short-time Fourier transform (STFT) to acquire time-frequency pictures as input. [PERSON] et al. [32] used cyclic spectral coherence to transform 1D signals into a 2D image that was integrated with CNN for fault diagnosis of bearings. A 2D convolutional structure derived from LeNet-5 was proposed by [PERSON] et al. [33], in which segmentation f  
Figure 1: Three scenarios of intelligent fault diagnosis. 2D image. [PERSON] et al. [34] performed automatic detection and damage evaluation of damaged bearing components by transforming vibration signals into a 2D grayscale image with time-frequency representation using a continuous wavelet transform. These research methods, however, are prescriptive and heavily rely on expert expertise. This paper proposes a novel method of bearing fault diagnosis based on 2D images and cross-domain few-shot learning, taking into account the aforementioned issues. Considering that the signals estimated using multiscale morphology contain more fault information and that no artificial parameter setup is required for full-scale calculation, the 1D bearing vibration data is transformed into a 2D image. In the meanwhile, the knowledge from the natural image dataset using MiniImageNet [35] is extended to the transformed bearing images. On this basis, the feature extractor is trained and then fine-tuned using a small number of samples from the target domain. In addition, a distance-based classifier [36] was utilized rather than a fully connected layer to improve the classification capacity of the few samples. Finally, using the experiment and production line datasets, the proposed method's effectiveness has been verified. The outcomes show that, in comparison to previous methods, our method offers more generality in addition to improved accuracy.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 5 QKD Market Perspectives
Lastly, a barrier that cannot be neglected while considering the spreading of a technology is the cost. For most of the market segments, commercial QKD systems are seen as prohibitive and not competitive considering the maturity of the technology. This is true also for critical infrastructure, where the cost may be considered more marginal than security. Considering the analysis proposed in [139], they estimated that a small network with 5-10 nodes would cost more than 500,000 euro for equipment only, considering actual available technologies. For satellite systems, where the installationof fibers and switching or routing devices is not needed, the cost is expected to be higher than 500,000 euro. They envisioned a reduction to less than 200,000 euro by 2022 by reaching the expected volumes of production, while reducing this value below 50,000 euro may also enable QKD for smaller (Cubesat) satellites, which would open a new, larger market segment. In the telecommunications sector, the dominant cost is in the sources and detectors, which currently cost around 100,000 euro and is expected to decrease to less than 10,000 euro by 2022. However, it is estimated that most of the market share in the telecommunications sector will be dominated by QRNG systems, enabling many applications for IoT, for which the costs are already relatively low. For QRNGs, the cost can be thousands of euros for the most efficient ones, while for those scenarios where high performance is not required, the price is expected to decrease to tens of euros within a couple of years, approaching the cost level of the IoT sector. A reliable assessment of strengths and weaknesses of QKD systems requires a thorough understanding of both the QKD protocol itself and its practical implementation, as reflected in European Telecommunications Standards (ETSI) reference documents on security proofs [140] and module security specifications [141].
## 1 Introduction
In the present article, it is expected that the ML APAs and the vertical centre of mass braking mechanisms are programmed according to foot strike patterns (FFS or RFS). Specifically, it is reasoned that the need to actively brake the centre of mass fall during the step-swing phase becomes less imperative in FFS if the FFS pattern is more effective than the RFS pattern to attenuate the collision forces, as repeatedly shown in the literature [16; 19; 20; 21; 22]. Because it has an energetic and attentional cost [12], the CNS may consequently choose to attenuate this active braking for economical purposes. If so, the duration of the swing phase of gait initiation can be expected to be shortened for the FFS pattern, since the centre of mass would fall more rapidly. Of particular interest, recent studies reported that the amplitude of the ML APAs associated with gait initiation over an obstacle was programmed according to this temporal parameter (i.e., the duration of the swing phase), and was lower when this duration decreased with a lower obstacle [10; 11]. In the present study, changes in ML APAs amplitude can therefore be expected because of the changes in the swing-foot strike pattern. Hence, the present study aimed to investigate the effect of changing the swing-foot strike pattern (FFS vs. RFS) on the postural organisation of gait initiation over an obstacle. The hypothesis of an interdependent relationship between balance control mechanisms for optimal stability control was raised. Specifically, and for the reasons detailed above, it was expected that both active braking of the centre of mass fall and the ML APAs associated with gait initiation over an obstacle to be cleared would be attenuated in the FFS compared to the RFS pattern, but with no consequent alteration in ML stability or increase in collision forces.
## 3 Materials and Methods
### Synthesis
#### 3.1.2 Copolymerization of VDPE, DVB and Styrene  
The synthesized VDPE monomer was subjected to the bulk copolymerization with DVB (used as a cross-linking monomer) and styrene (another hydrophobic vinyl monomer). In each synthesis, 1 g of VDPE was used, to which an appropriate amount of styrene and DVB were introduced to obtain an assumed molar ratio of VDPE:S:DVB. The content of VDPE varied in the range from 10 to 90 mol% (at 10 mol% step), while the amount of DVB changed in the range from 1 to 11 mol% (at 2 mol% step). Styrene was always the balance to 100%. The monomers were mixed with toluene (0.5 mL) and benzoyl peroxide (BPO, 2 mol% in relation to other monomers) as an initiator. Before the copolymerization reaction, the mixture was degassed by bubbling with Ar for 5 min. The vial was sealed and placed in an oil bath kept at 80 \({}^{\circ}\)C for 24 h. The obtained copolymer was crushed, washed with toluene and CH\({}_{2}\)Cl\({}_{2}\), and dried in a vacuum oven at 50 \({}^{\circ}\)C. Subsequently, the material was suspended in the mixture of NaOH (3.2 g, 80 mmol), water (20 mL), and dioxane (20 mL), and refluxed for 48 h. After cooling, it was washed with 1M HCl, dioxane, CH\({}_{2}\)Cl\({}_{2}\) and Et\({}_{2}\)O, followed by drying in a vacuum oven. Next, the copolymer was dispersed in acetic anhydride (20 mL) and mixed with dioxane (20 mL), refluxed for 24 h, again washed with dioxane, CH\({}_{2}\)Cl\({}_{2}\) and Et\({}_{2}\)O, and dried in a vacuum oven. Finally, the product was dispersed in a mixture of pyridine (30 mL) and dioxane (10 mL). To the prepared solution, hydroxylammonium chloride (0.514 g, 7.4 mmol) was added, and the obtained suspension was refluxed for 24 h. The resulting solid was washed with dioxane, H\({}_{2}\)O/DMF, DMF, toluene, EtOAc, CH\({}_{2}\)Cl\({}_{2}\), and Et\({}_{2}\)O, and dried in a vacuum oven.  
The finally obtained copolymer catalysts were denoted as aNHPI_bDVB, where a and b mean the content of the functional monomer and DVB, respectively, used in the copolymerization process, in relation to styrene. For example, the symbol 10%NHPI_1%DVB refers to the copolymer synthesized in the mixture containing 10 mol% VDPE, 1 mol% DVB, and 89 mol% styrene.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Securing Authentication for Mobile Networks
#### 2.7.1 Schemes for Authentication in 4G and 5G Cellular Networks
In a similar vein to the IDM3G protocol, authors in [43] introduced the BIO3G protocol for safe and privacy-preserving biometric authentication in 3G mobile contexts. In comparison to the IDM3G protocol, the BIO3G protocol cannot withstand DoS attacks and does not consider location or identity privacy [41]. 5. Deniable authentication with privacy  
Deniable authentication differs from standard authentication in that a third party cannot be persuaded by the receiver [44]. Authors in [45] suggested a non-interactive authentication methodology to accomplish deniable authentication. The protocol in [45] is based on the shared session secret and the ElGamal signature scheme, and it not only considers the security issues proposed by [46], such as forgery, impersonation, deniability, and completeness, but it can also maintain security when the session secret has already been compromised. As a result, in cellular networks, the employment of message authentication codes (MACs) between two parties can provide deniable authentication. Authors in [47] defined an experimental protocol for the Internet community named EAP-PSK under RFC 4764, which provides less scalability and security. RFC 3748 [48] and RFC 2284 specify the Extensible Authentication Protocol (EAP), which is widely used in wireless networks. Figure 6: Mutual authentication with privacy techniques classified.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 2 Background  
The Background section provides an overview of the key concepts and technologies related to IoT security and communication. It introduces the NDP, IPv6 in IoT, congestion  
\begin{table}
\begin{tabular}{c c} \hline \hline
**IoT Communication** & **Features** \\
**Protocols** & **Features** \\ \hline IPv6 & Expanded address space, enhanced security, simplified packet handling \\ \hline
6 LoWPAN & Adapts IPv6 for low-power IoT networks \\ \hline RPL & Routing protocol optimized for constrained IoT devices \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key communication protocols used in IoT networks.  
management, and modeling approaches that provide the groundwork for comprehending the Enhanced SCAFFOLD protocol.  
NDP is a core protocol in IPv6 that facilitates communication between nodes, routers, and hosts within the same network segment. It relies on the Internet Control Message Protocol version 6 (ICMPv6) [13] to achieve its functionality. The key message types used in NDP are as follows:  
* NS: Used to determine the link-layer address of a neighbor, verify the uniqueness of an address during the DAD process, or check the reachability of a neighbor.
* NA: Used to announce changes to a host's MAC and IP addresses or respond to NS message requests.
* RS: Sent by hosts to locate routers on the local link network and prompt an immediate response from the router.
* RA: Periodically sent by routers or in response to RS messages to announce their presence on the network and provide system parameters such as MTU, network prefix, and hop count.
* Redirect Message (RM): Used to redirect traffic from one router to another.
## 3 Acsa-Olta
### The Measurement Calculation Strategy
Taking the coherent integration length of 300 ms as an example, if the \(\uptau_{b}\) of DIS is 0.1 chip and DET has the optimal grid size shown in Figure 5, when \(C/N_{0}\) is larger than 23 dBHz which is the standard deviation cross point of the measurements derived from the two methods, the correlators needed to search the area of 1 chip and the corresponding measurement error standard deviation are listed in Table 1.  
In the table, the measurement accuracy is calculated under the assumption that the peak detection is correct. Since the optimal grid size is smaller than 0.1 chip when \(C/N_{0}\) is larger than 23 dBHz, the false peak detection probability of the open loop with the optimal grid size is a little larger than that with the grid size of 0.1 chip so that the code phase measuring error is more coincident. As a result, the measurement accuracy of DET and DIS in these cases are more or less the same which is further verified in Section 4.1. In consideration that DIS can hugely decrease the correlators needed, it is preferred when \(C/N_{0}\) is high. When \(C/N_{0}\) reduces, the measurement accuracy of DIS is quickly decreased due to the noise and it is much poorer than the DET with the optimal grid size. For the more, as \(C/N_{0}\) decreases, with the grid size of 0.1 chip, the false detection probability become large and the accuracy will be further distorted. In fact, when \(C/N_{0}\) is lower than 23 dBHz, the measurement accuracy of DIS will quickly degrade which is validated by the simulation results in Section 4.1. Therefore, we propose the measurement calculation strategy that switches the two code phase calculation methods at the \(C/N_{0}\) of 23 dBHz, namely the standard deviation cross point of the measurements computed with the two methods. In the strategy, when \(C/N_{0}\) is higher than 23 dBHz, the discriminator is used and the grid size is 0.1 chip. When \(C/N_{0}\) is lower than 23 dBHz, measurement is calculated directly based on the parameters of grid with the optimal size. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{\(C/N_{0}\) (dBHz)} & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** \\ \hline \multirow{2}{*}{DET} & correlator number & 24.3 & 26.2 & 28.5 & 30.9 & 34.7 & 38.6 & 44.0 & 50.3 \\  & standard deviation (chip) & 0.0119 & 0.0110 & 0.0101 & 0.0094 & 0.0083 & 0.0075 & 0.0066 & 0.0057 \\ \hline \multirow{2}{*}{DIS} & correlator number & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 \\  & standard deviation (chip) & 0.0292 & 0.0260 & 0.0231 & 0.0206 & 0.0183 & 0.0163 & 0.0145 & 0.0129 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistic of the correlator needed and the mean accuracy. Figure 6: The code phase error standard deviation of the two measurement calculation methods without consideration of false peak detection.
## 3 Results  
### Classification Performance Results  
In this study, the ABIDE I pre-processed dataset was utilized within an FS and classification framework aiming for the automatic classification of ASD and TD subjects, while investigating potential discriminative biomarkers.  
The optimal performance was obtained when employing 136 features yielding 76.63% accuracy, 78.63% sensitivity, 74.27% specificity, and 82.74% AUC. The discrimination ability of the classification model is presented by the receiver operating characteristic (ROC) curve in Figure 2. The small number of discriminative features (comparatively to the number of instances) included in the produced feature subset also suggests that overfitting was avoided. Considering the 4:1 ratio among females and males, gender was also investigated as a potential biomarker; however, it resulted in inferior performance compared to the framework proposed in this study.  
Figure 2: The receiver operating characteristic (ROC) displaying the classification performance of the proposed framework.  
From the selected 136 features, the majority included dFC characteristics (47 and 45 out of the 136 total features for mean dFC and variance dFC, respectively), with sFC incorporating 43 (out of the 136) features (Figure 3). Interestingly, from the non-biological attributes, only one was selected, namely the echo time (TE), while demographics, motion, and the rest of the acquisition features (FOV, voxel size, and repetition time) were excluded from the optimal feature subset. In this regard, an additional analysis of FS excluding the aforementioned features took place (the results and implications of which are presented in the Supplementary Material), although it failed to produce any increment to the overall performance.