esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discrete Wavelet Transform  
Discrete wavelet transform (DWT) is the most commonly used method in the filtering of time-frequency [34]. The reduction of additive noises by DWT provides good resolution in the frequency and time domains. Multi-resolution analysis is enabled by the DWT method by dividing the discrete signal x(n) into low- and high-frequency components. To determine DWT, an iterative Mallat algorithm can be applied [35; 36]. The low-frequency  
Figure 1: The simulation setup.  
Figure 2: The X-ray spectrum derived from TASMIC package.  
component \(a_{jk}\) (approximately) and the high-frequency component \(d_{jk}\) (exactly) (Figure 3) are the results of signal x(n) decomposition with multi-level filter banks:  
\[a_{jk}=\sum_{l}h(l-2k)a_{j-1,m} \tag{1}\]  
\[d_{jk}=\sum_{l}g(l-2k)a_{j-1,m} \tag{2}\]  
where \(j\) is a parameter that affects the DWT scaling. \(k\) is related to the translation at each level of the wavelet function. \(l\) is the number of levels and is an integer scale. \(h(l)\) and \(g(l)\) are low-pass and high-pass square filters, respectively. \(m\) is utilized in the scaling function as a translation of the \(j\) scale. Daubechies, Haar, etc., are several families of wavelet functions that exist in wavelet transform. In the present study, a Daubechies 2 (db2) wavelet was selected to analyze the signals received from the scintillation detector.  
The decomposition process of the signal obtained by the detector is illustrated in Figure 4. According to this figure, from the fifth stage onwards, no significant high-frequency information was obtained; thus, in this study, the signal decomposition only continued until the fifth stage. It should be noted that the details of the first stage had many fluctuations that show noisy behavior. The researchers believe that this signal is due to uncertainty problems in the MCNPX transport code. Therefore, these details were not considered for the next processing step. To provide suitable data for network inputs, the standard deviation (STD) features of \(a_{5,1}\), \(d_{5,1}\), \(d_{4,1}\), \(d_{3,1}\), and \(d_{2,1}\) have been calculated. It should be noted that this statistical characteristic has been introduced in previous studies as an efficient time characteristic [16].  
\[TD=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\left|x_{i}-\mu\right|^{2}} \tag{3}\]  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{4}\]  
where \(x_{i}\) is the primary data and \(N\) is the amount of data.  
Figure 3: The signal decomposition process using DWT; g(n) is the low-pass filter, h(n) is the high-pass filter. The operator () means down-sampling by two.
## 3 Results and Discussion
### Effect of Neutralized Citrate Buffer on Chlorella Growth  
Citrate buffer is a constituent of wood enzymatic hydrolysate, as this buffer is used to keep pH at a level (4.8) optimal for activity of cellulosic enzymes. The addition of neutralized (pH = 7) wood enzymatic hydrolysate into microalgal cultures also involves adding citrate buffer. Therefore, the effect of neutralized (pH = 7) citrate buffer on _Chlorella_ growth in TGP (1 g/L glucose) during heterotrophic and mixotrophic cultivation was evaluated. The results of our study show that neutralized citrate buffer can exert an inhibitory effect on _Chlorella_ growth (Figure 2). Inhibition of _Chlorella_ growth during heterotrophic cultivation increased with the increase in the neutralized citrate buffer loading from 0.5% to 10%, although further loading increases (15%, 20%) did not alter inhibition rate, which remained as for a 10% loading. Citrate buffer contains citric acid, which has been reported to be inhibitory to bacteria [20] and fungi [21]. Citric acid has also been reported to exert positive [22] or negative [23] effects on _Chlorella_ growth. Citric acid improved biomass production during _Chlorella vulgaris_ cultivation, because it was able to be utilized as a carbon source [22]. In contrast, _Chlorella ellipsoidea_ failed to grow in the presence of citric acid, and the inhibitory effect of citric acid was suggested to be due to feedback inhibition of citrate synthase activity or the blockage of DNA synthesis [23]. Besides citric acid, neutralized buffer also contains sodium ions, and it was reported that the occurrence of salt stress in _Chlorella_ cells was associated with the presence of Na\({}^{+}\) ions [24]. Therefore, the inhibitory effect of Na\({}^{+}\) ions accumulated in medium towards microalgae should not be excluded in the case of applying enzymatic hydrolysate at high loadings on industrial scale.  
In our study, heterotrophic growth of _Chlorella_ was partially inhibited, but the inhibitory effect of neutralized citrate buffer was much more pronounced during mixotrophic cultivation. Figure 2 shows that light-driven growth of mixotrophic conditions is almost completely inhibited in the presence of neutralized citrate buffer, with a growth rate for TGP-Cit10 (L-Light) much smaller than for TGP (L-Light), and nearly at the same level as for TGP-Cit10 (D-Dark).  
Based on the facts that the obtained wood enzymatic hydrolysate contains \(\sim\)10 g/L glucose, and that neutralized citrate buffer seems to block the photosynthetic part of mixotrophic growth, a 10% loading of neutralized wood hydrolysate (TGP-Enz10) was used to obtain the 1 g/L glucose sufficient for _Chlorella_ growth during heterotrophic cultivation, to avoid inhibition of light-based growth.  
Figure 1: Glucose release (line) and saccharification rate (bars) during 72 h enzymatic hydrolysis of alkaline (NaOH) pretreated beech wood solids. Number of independent experiments: 3. Standard error: 5%.
## 3 Performance Metrics and Application Scenarios in PLS
### Quality of Service (QoS)-Related Metrics  
Compared to existing wireless communication networks, 5G and B5G networks have very high data rates and higher coverage rates with significantly improved QoS. One of the main demerits of employing traditional secrecy outage probability-based metrics for 5G networks is that they are not directly linked to the QoS requirements for different service applications. Without loss of generality, the QoS-related metrics that are used in this survey include SINR-based, PER-based and BER-based metrics. The SINR-based metrics are the most widely used metrics for describing the performance difference between the capacities of Bob and Eve channels. The theoretical basis of the SINR-based metrics can be characterized as the quantitative relationship between the noise powers of the legitimate channel and the Eve channel [29; 30]. Additionally, the performance of the main transmission and wiretap channels is also described by the QoS. Thus, the SINR-based metrics are instantly linked to the QoS and this can help with the optimization and modeling of secure transmission strategies. A maximum SINR performance of communication from the source Alice to Eve and a minimum SINR performance from Alice to Bob imply that the system performance for receiving signals is good enough in terms of the reliability and security aspects. This includes the method for efficient secure communication that is capable of obtaining the required maximum and minimum error levels for Eve and Bob, respectively. In order to enhance the capacity of a transmission link, it is important to adopt PLS techniques, such as beamforming. An example of this definition is the packet error rate (PER) [84] and bit error rate (BER) [85], which can be analogously related to secrecy throughput [86] and, therefore, the SINR. For the foundation of a transmission channel, the BER requirement must be greater than the minimum desired level. In general, the physical layer techniques exploit optimization problems to enhance the security of a transmission strategy by reducing the BER of eavesdroppers. As a result, the BER can be utilized to estimate the performance of a secure transmission strategy, as well as the QoS requirements.  
Furthermore, the authors of [87] provided an alternative design for a secure precoding scheme, which was based on the QoS and SINR metrics. This design contains an optimal power allocation and standardization framework schemes, which also realize the trade-off between minimizing SINR at the eavesdropper and maximizing SINR at the legitimate receiver. Despite the existing knowledge-rich approaches, secure and reliable transmission strategies are still a predominantly open problem within physical layer design and optimization systems. In these cases, the appropriate metrics are commonly used during security design to estimate the attainable performance of transmission systems. Specifically, the secrecy metrics that are listed in Table 3 are mostly considered to be the design criteria and optimization constraints that are necessary for efficient secure communication strategies.
# Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
## 2 Unveiling the Inner Workings: Exploring the Principles and Model of an EDS System
### The Research Gap
The primary challenge lies in determining the mutual inductances between any two coils, which depends on their relative positions, which continuously change due to train displacement. All physical quantities are derived from changes in magnetic energies stored in the system, dictated by the mutual inductances between onboard and offboard coils. Therefore, prior calculation of mutual inductance tables, obtained through numerical calculations, as proposed in this work, offers a means to significantly expedite the analysis of EDS systems. Harmonic analysis employing Fourier transforms, coupled with numerical methods, is used to ascertain lift and drag forces on continuous sheet guideways, as demonstrated by [PERSON] and [PERSON] [PERSON] and [PERSON] (1973) when investigating the motion of a rectangular coil on a conducting sheet. This technique is typically limited to two-dimensional steady-state analyses,assuming infinite guideway conductor width. It may also apply to guideways with discrete coils [37]. [PERSON] et al. [38] provided the most cited experimental data on induced currents and generated forces in the EDS system of the Yamanashi test line. The values obtained from this paper are compared with those derived from the method presented herein. Experimental analysis remains a significant challenge in studying EDS systems, due to the high cost of SC coils and their requisite cooling systems. Consequently, most experiments utilize permanent magnets or Halbach arrangements as substitutes for SC coils. Additionally, linear test tracks are often substituted with rotational test benches to replicate high speeds, as observed in the study by [PERSON] et al. [39]. The main contribution of this work lies in developing a novel approach to calculating forces in null-flux EDS systems, offering a more time-efficient solution. This approach streamlines the investigation of EDS phenomena by simplifying the research process and eliminating the need for complex algorithms. Furthermore, it provides a straightforward method to study non-cross-connected null-flux systems, enabling rapid computation of induced currents and generation of forces using an electrical circuit simulator. By introducing this innovative approach, this work contributes to advancing the field of EDS and offers valuable insights into the behavior and performance of EDS systems.
# Exact Relationship between Black Phosphorus Thickness and Behaviors of Field-Effect Transistors
## 3 Results
The reason for the rapid increase can also be attributed to the fact that when the BP thickness is less than 21.7 nm, the band gap of BP is modulated by the thickness, thus affecting the mobility. In order to verify the above conclusions, we used plasma etching technology to etch the BP device with a thickness of 50 nm. Under specific power and current, after controlling the etching rate for 30 s, a BP-FET with a thickness of about 30 nm was obtained. Figure 6a shows its output characteristics. In the three \(I_{ds}\)-\(V_{g}\) curves, the source drain voltage scanning range is from \(-\)100 mV to 100 mV, and the gate voltages are set at 30 V, 0 V, and \(-\)30 V, respectively. Figure 6b shows the transfer characteristics. The \(V_{g}\) scanning range is from \(-\)30 V to 30 V, and the source drain voltage is set at 100 mV. Compared with the output characteristics of the BP FET in Figure 5c, the current of the etched BP FET is close to that of the 30 nm BP FET and greatly reduced compared with that of the 50 nm BP FET, which confirms that in the electrical characteristics of the BP FET is directly related to the BP thickness. Under the same \(V_{g}\), the greater the BP thickness, the greater the source drain current. However, the \(I\)-\(V\) curve in Figure 6a has kink points. The reason for this phenomenon is that the plasma etching method often introduces different levels of  
Figure 5: **(a–f)** The \(I_{ds}\)-\(V_{ds}\) curves at different \(V_{g}\), BP thickness of FET are 50 nm, 40 nm, 30 nm, 20 nm, and 6 nm, respectively. The \(V_{g}\) is from \(-\)40 V to 40 V.  
structural damage and impurity elements, causing damage to the lattice structure of the sample materials. From the transfer characteristics in Figure 6b, it can be concluded that the thickness of the material will affect the modulation of the electric field. By comparing the transfer characteristic curve of the BP FET with 30 nm BP in Figure 4a, it can be seen that the electric field modulation effect is more obvious in the device after plasma etching. Since the device after plasma etching has better conductivity when the negative gate voltage is higher, most carriers are holes, which is consistent with the above conclusion.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 5 Simulation Results and Discussions  
A new OWT maintenance model is developed in this Section by assembling the above four types of PN models based on the following assumptions:  
* The CMS has no hardware reliability issues;
* The subsystem faults and failure caused by natural disasters (e.g., earthquakes, tsunamis, etc.) are not considered;
* All detected faults are repairable, and all failures can be recovered through maintenance;
* The health state of a subsystem after maintenance is regarded as good as new.  
Then, based on the data listed in Tables 1 and 2, the simulation calculation is carried out by using the following steps:  
Step 1: Define the parameters of the PNs:  
1. Initialise the places and transitions, and the arcs between them, as well as the conditions for the condition places and the terminate places;
2. Import the fixed transitions times and compute the variable ones (e.g., time to failure, time for chartering a vessel, etc.) by randomly sampling with the corresponding distributions as in [29];
3. Define the conditional probability of any conditional arcs.  
Step 2: Identify and fire the enabled transition that has the minimum switching time in the complete model;  
Step 3: Recompute the times of any transitions that are still enabled;  
Step 4: Repeat Steps 2 and 3 until a token is produced in one of the condition places or terminate places;  
Step 5: Activate the conditions predefined for the condition places or terminate place containing a token;  
Step 6: If a token is produced in a terminate place, the present iteration will be ended. Otherwise, repeat Steps 2 to 5.  
Step 7: Iterate the above simulation until the predefined iteration time is reached.  
To validate the convergence of the model, the average number of drivetrain failures within the lifetime of the OWT is calculated. The calculation results obtained when the time intervals of \"basic service\" and \"advanced service\" are six months and one year respectively are shown in Figure 8.  
From Figure 8, it is seen that after the simulation starts, the calculation result of the average number of drivetrain failures fluctuates at the beginning. Then, it becomes stable gradually with the increasing number of simulations and finally converges to a stable value  
Figure 8: The average number of drivetrain failures versus the number of simulations.  
after the number of simulations is over 50,000. In the following calculations, to ensure the reliability of the calculation results, 100,000 simulations will be performed to predict the target parameters.
## 4 Discussion
In terms of their injury mechanism, DAI as well as SDH are connected to these components of acceleration, which are present during a direct cranial impact scenario and cause a relative movement of brain to skull, white to grey matter respectively. In case of SDH, relative motion between skull and brain causes tearing or failure of the bridging veins of the subdural space. The DAI may also arise out of relative motion but due to relative motion of white and grey matter. This relative motion affects the integrity of neuronal axons, which are connecting both parts of the brain tissue. The axons might get swollen due to this event, which leads consequently to the secondary severe effects of DAI. Nonetheless, these injuries may also arise below the HIC-threshold of safety criteria. Thus, the aspects of head movement are gaining importance in the assessment of the overall VRU safety during an impact scenario [90]. Moreover, the biomechanical response of the skull structures during traffic accidents is not fully understood. In order to construct head protection systems and criteria for damage to not only the head structures but also the other human body parts, the research need to be intensified. Therefore, the author proposed to include numerical modelling as the further step to assess and improve VRU's safety. \begin{table}
\begin{tabular}{c c c} \hline
**Head Injury** & **Criteria** & **Threshold** \\ \hline Bone (skull) fracture & Maximum principal stress (diploe layer) & 20 MPa \\  & Maximum principal strain (cortical layer) & 0.42\% \\ \hline Bone (facial/nose) fracture & Effective plastic strain (cortical layer) & 1.2\% \\  & Effective plastic strain (rheocular layer) & 4.5\% \\ \hline Acute Subdural Hematoma (ASDH) & Strain in bridging veins. ASDH due to bridging vein rupture. & 25\% \\ \hline  & Intracranial pressure in region of interest (Coup) & 237 kPa \\ Cerebral contusion & Intracranial pressure in region of interest (Contrecup) & –104 kPa \\ \hline  & Diffuse axonal injury & Average maximum principal strain in ROI & 48\% \\ \hline \end{tabular}
\end{table} 
Table 3: Head injury criteria, adapted from [78, 82].
###### Abstract  
For the analysis of volatile bacterial compounds, solid phase microextraction (SPME) is currently the most widely used metabolite concentration technique. Recently, the potential of stir bar sorptive extraction (SBSE) for this use has been demonstrated. These two approaches were therefore used in combination with gas-chromotography coupled with mass-spectrometry (GC-MS) for the analysis of volatile and semi-volatile bacterial compounds produced by _Staphylococcus aureus_. In both cases, SPME and SBSE/headspace sorptive extraction (HSSE) enrichment was carried out in two coating phases. A whole analytical and statistical process was developed to differentiate the metabolites produced from the metabolites consumed. The results obtained with SBSE/HSSE and SPME were compared and showed the recovery of 90% of the compounds by SBSE/HSSE. In addition, we were able to detect the production of 12 volatile/semi-volatile compounds by _S. aureus_, six of which had never been reported before. The extraction by SBSE/HSSE showed higher concentration capacities and greater sensitivity than SPME concerning bacterial compounds, suggesting that this technique may therefore become the new preferred option for bacterial volatile and semi-volatile compound analysis.  
gas-chromotography-mass-spectrometry; stir bar sorptive extraction; solid phase microextraction; _Staphylococcus aureus_; bacterial metabolite analysis +
Footnote †: journal: molecules
## 4 Evaluation
### Performance Evaluation Metrics
#### 4.3.3 Dispersion of Routing Load  
Dispersion of routing load indicates the evenness of load distribution in a network. A large dispersion indicates that the load in the network is not balanced. A relatively single and fixed route will produce this result, which will lead to congestion at nodes and even network crash.  
\[Dispersion=\frac{\sum_{i=0}^{N}\left(\beta_{i}-\overline{\beta}\right)^{2}}{N} \tag{17}\]
# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
## 4 Methodology
### Double Attention-Based Word Embedding Model  
To encode the semantics of sememes into words through the \"word-sense-sememe\" structure, a DAWE model is developed, as shown in Figure 5.  
In the DAWE model, a \"double attention\" architecture is adopted: (1) Sense-level attention is to capture senses weight changes with context. A word may have different meanings in different contexts, but those meanings are not isolated. We argue that the meaning of a word in a specific context should be a fusion of different senses. As the context changes, the fusion weight of the senses also changes accordingly. (2) Sememe-level attention is to capture the weight change of sememes with context. In the SE-WRL model, each weight of sememes that constitutes the senses is thought as equivalent. Actually, when a sense presents different meanings, the weights of sememes under a sense should be different.  
As shown in Figure 5, DAWE is a word embedding model based on the word-sense-sememe structure, as well as a word sense disambiguation (WSD) model. In DAWE, sememes constitute the different senses of a word, and then different senses reconstitute into word meanings that are relevant to the textual context.  
Figure 5: DAWE model.  
The purpose of word embedding is to keep semantic relevance of words while words are embedded into a unified semantic space. However, word embedding has the semantic confusion defect of representing all the meanings of a word in the same vector. To remedy such deficiencies, the different meanings of words need to be modeled separately to overcome the chaos of word embedding. The research suggests that better decomposition of word meanings combined with context leads to better representations of word meanings. WSD is to distinguish the different senses of words in different contexts, which can be roughly divided into unsupervised methods and knowledge-based methods. DAWE uses a knowledge-based approach to disambiguate the different senses of words in context using weighted sememes for the presenting of senses under a word. As a word embedding model based on knowledge, the objective of DAWE is the same as conventional approaches based on knowledge, which is to have words with the same semantics close to each other and words with different semantics away from each other [29].  
According to the location of the object of the \"attention\", DAWE models can be extended to double attention over context model (DAC) and double attention over target model (DAT). Figures 6 and 7 illustrate the relationships and differences between the two models.