esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Modeling of Scholte Wave in Acoustic-Elastic Coupled TTI Anisotropic Media
## 4 Numerical Experiments
### Inhomogeneous Acoustic-Elastic Coupled Model  
The inhomogeneous TTI acoustic-elastic coupled media is shown in Figure 12, which is composed of the water layer and the inhomogeneous TTI media. The size of the model is \(9.21\) km \(\times\) 3.75 km. The water layer is \(0.75\) km thick. The grid spacing is \(\Delta x=\Delta z=12.5\) m. The density of inhomogeneous TTI media is \(\rho_{S}=1.8\) gcm\({}^{-3}\), the polar angle \(\theta\) is set to \(-30^{\circ}\), and the density of the fluid \(\rho_{f}=1.0\) gcm\({}^{-3}\). The temporal step for the staggered-grid finite-difference method is \(\Delta t=1\) ms. A point source with 20 Hz wavelet is located at x = 4.62 km and z = 0.7375 km (Figure 13).  
Figure 11: Arrival time of the Scholte wave at point A, B, C, and D with different polar angles. The blue, red, yellow, and purple lines are point A, B, C, and D, respectively. The Thomsen parameters are \(\varepsilon=0.2\), \(\delta=-0.05\), and \(\gamma=0.1\).  
Figure 12: Acoustic-elastic coupled Marmousi-2 model. Plots (**a–d**) are the P- and SV-wave vertical velocities and the Thomsen parameters \(\varepsilon\) and \(\delta\), respectively. The red line is the fluid-solid interface. The icon S represents the point source.  
Figure 13 shows the wavefield snapshots of the Marmousi-2 model. We can find that the snapshot of particle-velocity component \(v_{z}\) includes the Scholte wave and body waves. Beside there is no dispersion in the calculation results. This indicates that our algorithm has fine stability and is suitable for complex models.  
Without changing the transverse position, we gradually increase the distance between the source and the fluid-solid interface. The Scholte wave is clearly visible in the simulation results (Figure 14). It propagates along the horizontal fluid-solid interface, the time-distance curve is a straight line, and the velocity is lower than the minimum shear wave velocity of TTI media, which has strong propagation energy. When we reduce the distance between the source and the fluid-solid interface, the energy of the Scholte wave gradually decreases.  
In this section, we apply the algorithm to the inhomogeneous acoustic-elastic coupled model. Numerical dispersion did not appear. This indicates that our algorithm is stable and can be applied to complex models. Moreover, we find that the distance between the source and the fluid-solid interface is negatively correlated with the energy of the Scholte wave.  
Figure 14: _Cont._  
Figure 13: Snapshots of particle-velocity components (**a**) \(v_{x}\) and (**b**) \(v_{z}\) at \(t=1.1\) s. The red line denotes the fluid-solid interface.
###### Abstract  
The _ilvBNC_ operon in _Corynebacterium glutamicum_ encodes key enzymes for the biosynthesis of branched-chain amino acids (L-isoelucine, L-leucine, and L-valine). This operon has been studied for quite a long time, and it is assumed that three hairpin mRNA structures can be formed in its regulatory region; however, their functionality and role in the attenuation mechanism of the _ilvBNC_ operon are not completely clear. In the present work, we performed a mutational analysis of mRNA secondary structures in the regulatory region of the _C. glutamicum ilvBNC_ operon, which allowed us to propose a model of the regulation of its transcription involving three mRNA hairpins that essentially act as a transcription terminator, an antiterminator, and an antiantiterminator. In this work, we proved the existence of a transcription terminator in this operon and experimentally confirmed the effectiveness of its influence on the expression of the _ilvBNC_ operon, AHAS enzyme activity, and valine production. We demonstrated the unique functional features of this attenuator, which, due to the overlapping of the terminator and antiterminator hairpins, is capable of rapid low-energy transitions between them without the complete disruption of the hairpin structures.  
Corynebacterium glutamicum; transcription regulation; attenuation; _ilvBNC_ operon; acetohydroy acid synthase (AHAS)
###### Abstract  
This paper proposes array antennas based on half Tyler (HT) distribution that can realize omnidirectional null-free (ONF) beams. Array 1 employs the usual edge feeding approach, working at 3.5 GHz with a bandwidth of 0.25 GHz, whereas array 2 operates at 26 GHz with a bandwidth of 3 GHz. We evaluated the far-field performance of linear arrays consisting of 6, 8, 12, and 16 elements. The simulation and measurement results show that the proposed HT approach can achieve a near-ONF pattern and cover a broad area of \(\pm 42^{\circ}\) on an eight-element linear array. The ONF beam reduces community power fluctuations and increases power by 20 dBm in surrounding areas of the base station (BS). The fluctuation treatment effect can be influenced by adjusting the weighting of sidelobe depression (dp): compared to the uniformly excited one, for example, the maximum ONF gain of an eight-element linear operating at 3.5 GHz is only reduced by 0.5 dB when dp = \(-\)30 and by 1-2 dB when dp = \(-\)60.  
power fluctuation; omnidirectional null-free (ONF) beam; half Tyler (HT) distribution; beam scanning; sidelobe depression (dp)
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 3 Signal Model
### Lte-V2x  
LTE-V2X supports 10 MHz and 20 MHz channels. Each channel is divided into subframes, Resource Blocks (RBs) and subchannels. A subframe is 1 ms long, as is the transmission time interval. It consists of 14 OFDM symbols with a normal cyclic prefix. Those with indices 3, 6, 9 and 12 are used for channel estimation and carry Demodulation Reference Signals (DMRS); the last symbol is used as a guard period for Tx-Rx timing adjustment, and the remaining are the actual data symbols [4].  
A resource block represents the smallest unit of frequency resources and is made up of 12 subcarriers of 15 kHz spacing (total of 180 kHz). A combination of RBs in the same subframe is referred to as a subchannel in LTE-V2X, and each subchannel may have a different number of RBs [27].  
Within the same subframe, a subchannel is used to transmit Transport Blocks (TB) over the physical sidelink shared channel, and Sidelink Control Informations (SCI) over the physical sidelink control channel. A TB contains user data information and must be transmitted with its associated SCI. An SCI carries information, including the modulation and coding scheme, which is crucial to decode user data. It is always sent using the Quadrature Phase Shift Keying (QPSK) modulation scheme, whereas TB can also support the 16-QAM modulation scheme [28].
## 3 Results
### Operating Limits of the Examined System  
The examined system is able to operate with different combinations of loads, as was shown in Section 3.3. However, it is not possible to operate with all the possible operating scenarios due to energy balance restrictions. Specifically, the ambient source heat input acts as the counterbalance energy rate in order to provide the proper heating loads. This heat input (\(Q_{in}\)) cannot be negative and this is a restriction of the present design.  
Figure 20 shows different operating lines of the system, with each corresponding to a different cooling load. Cooling loads of 25 kW, 50 kW, 75 kW and 100 kW are examined and in all cases, the operating lines are approximately vertical. In this analysis, \(T_{am}\) = 25 \({}^{\circ}\)C and \(T_{heat,h}\) = 115 \({}^{\circ}\)C, with this combination used in the majority of cases of this work.  
Moreover, Figure 20 proves that the linear correlation of each curve separately is very accurate with R\({}^{2}\) = 1. The next step is the development of an overall equation for determining the system operating limits. This equation is:  
\[Q_{heat,h}\geq 1.8176\cdot Q_{cool}-1.3573\cdot Q_{heat,m} \tag{32}\]  
The aforementioned equation has R\({}^{2}\) = 0.9999 and a mean absolute deviation error of 0.02%; thus, it is a very accurate formula. The equation has been developed for zero ambient source heat input. Therefore, it is a low limit because higher ambient heat input gives a greater possibility for heating production.  
Figure 19: System energy and exergy efficiencies for different high heating loads.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 2 Proposed Methodology
### Empirical Mode Decomposition (EMD)  
EMD is a data-driven technique used to analyze signals which are nonlinear and non-stationary in nature:  
* For transient analysis, EMD separates the signal into components of different resolutions.
* The signal is decomposed into various frequency components known as intrinsic mode functions (IMFs) and a residue.
* In a power system, the signals generated during transient conditions can be processed by using EMD to retrieve useful information from the signal. The developed components can be used in protection functions. The two rules applied to decompose a signal during transient conditions are given in [5; 14].
* Rule 1: Only a single extremum is to be considered by the IMF between two subsequent zero crossings. This means that the number of counted local minima and maxima must differ at most by one.
* Rule 2: The mean value provided by the respective IMFs should be zero. The sifting process can be summarized in the following algorithm.  
Following the above rules, a reconstructed data set \(u(t)\) decomposes into N number of IMFs \(C_{i}(t)\) and a residue \(r(t)\):  
\[u(t)=\sum C_{i}(t)-r(t) \tag{1}\]  
When \(r(t)\) satisfies the stop criteria, that will be taken out as the final residual, and the process of EMD will be completed.
## 4 Experiments and Results
### Evaluation Metrics  
The performance of the proposed GLSNet is assessed using three widely used metrics: overall accuracy (OA), \(F_{1}\) score, and mean intersection over union (mIoU) for each class. The OA is a metric that evaluates the accuracy of pixel classification by determining the ratio of accurately classified pixels to the overall number of pixels. The F1 score can be calculated for every category:  
\[F_{1}=(1+\beta^{2})\cdot\frac{precision\cdot recall}{\beta^{2}\cdot precision+ recall},\beta=1 \tag{6}\]  
Additionally, the mean F1 score is determined by averaging all of the F1 scores. If TP is defined as the number of true positives. The numbers for false positives and false negatives are represented as FP and FN, respectively. The IoU formula can be written as follows:  
\[IoU=\frac{TP}{FN+FP+TP} \tag{7}\]  
Next, we calculate the mean intersection over union (mIoU) by averaging the IoU values across all semantic categories to facilitate comparison.
# Optimization of a Coherent Dual-Beam Array Feed Network for Aperiodic Concentric Ring Antennas
## 3 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Antenna System** \\ **(Array + CBFN)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Beam** \\ \end{tabular} & \begin{tabular}{c} **Main Beam** \\ **Direction** \\ \end{tabular} & \begin{tabular}{c} **SLL [dB]** \\ \end{tabular} & \begin{tabular}{c} **\(D\) [dB]** \\ \end{tabular} & \begin{tabular}{c} **ACRA + CBFN Improvement** \\ **SLL [dB]** \\ \end{tabular} & \
\begin{tabular}{c} **\(D\) [dB]** \\ \end{tabular} \\ \hline Uniform & 1 and 2 & \(0\leq\varphi_{0}\leq 360\) & \(-14.49\) & \(15.35\) & \(-\) & \(-\) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Multi\(-\)beam (Opt. \(\overline{\tau}\)) \\ \end{tabular} } & \multirow{2}{*}{1} & \(45^{\circ}\) & \(-19.67\) & \(17.89\) & \(-6.2\) & \(3.2\) \\  & & \(95^{\circ}\) & \(-17.40\) & \(17.78\) & \(-4.7\) & \(2.5\) \\  & & \(245^{\circ}\) & \(-20.46\) & \(18.88\) & \(-6.1\) & \(3.5\) \\ \cline{2-6}  & \multirow{2}{*}{2} & \(345^{\circ}\) & \(-17.59\) & \(16.12\) & \(-3.0\) & \(0.9\) \\  & & \(145^{\circ}\) & \(-16.74\) & \(16.65\) & \(-2.2\) & \(1.3\) \\  & & \(295^{\circ}\) & \(-16.86\) & \(17.27\) & \(-2.3\) & \(1.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the conducted experiments and mean values of the non-uniformity improvement on the proposed antenna system in terms of sidelobe level (SLL) and \(D\). This novel two-beam configuration approach in the proposed feeding system is not exclusive for aperiodic CCRAs. In fact, a variety of C-BFN setup configurations can be designed by properly modifying the layers or the input/output ports based on the number of beams and the overall performance. Thus, this proposed dual-beam antenna system found on C-BFN with a non-uniform circular concentric aperture is a candidate for different antenna applications considering its isophoric feeding and reduced complexity, as well as its multi-beam shaping performance along with the scanning range.
## 5 Model Training and Result Discussion
### Experimental Results  
This experiment trained the CRNN model on the four video frame numbers of 5, 10, 25, and 50 frames, as well as evaluated the effect on the test set training loss curves, as shown in Figure 11. In the initial training, the loss of the model in the four experiments showed a rapid downward trend. After about 900 iterations, the model gradually began to converge and showed a stable trajectory. Figure 12 shows the performance evaluation results, where the model performed best when the number of video frames for a single sample was 10. On the validation set, its accuracy, mAP, and F1 score were 98.18%, 99.46%, and 0.9817, respectively; on the test set, the comprehensive effect of the dataset with 10 frames per sample was also excellent, with an accuracy, mAP, and F1 score of 98.52%, 99.34%, and 0.9849.  
In the supervised learning field of machine learning, classification results and actual measurement results are often compared through confusion matrices. The confusion matrix is a visualization tool that can present the accuracy of the classification results and depict the true positives, true negatives, false positives, and false negatives of each category. Figure 13 shows the confusion matrix of the dataset of 10 frames per sample on the test set. The test set is automatically extracted from the dataset according to the algorithm. There are more than 200 corresponding test samples for each leakage level. Figure 13a shows the difference between the predicted results and the actual labels of the various leakage levels. In Figure 13b, the values are in percentile, and it is determined that the CRNN has a correct rate of more than 90% for each leakage level. The best performance is achieved for leakage levels of 5.0 L/min and 15 L/min, achieving 100% correct recognition. The results prove that the developed method has strong detection capabilities. It can meet the needs of industrial detection.  
Figure 11: Changes in the training loss for the different data samples during training.  
Figure 12: Results of the different video frames on the validation set and test set.
## 5 Proposed Method  
### Preliminaries  
#### 5.1.1 Decentralized Partially Observable Markov Decision Process  
The cooperative searching and tracking problem for multiple UAVs can be modeled as a decentralized partially observable Markov decision process (Dec-POMDP). At every timestep \(t\), each agent in the system make its decision based on the local observation (including perception and communication). And then the environment execute all agents' joint action to update the immediate rewards and the next global state.  
Assuming there are \(n\) agents, the Dec-POMDP can be described as follows (the time subscript \(t\) is omitted for convenience and the joint variables over all agents are bold) [42]:  
\[(N,S,\mathbf{A},T,\mathbf{R},\mathbf{O},Z,\gamma), \tag{24}\]  
where:  
* \(N\) denotes the set of \(n\) agents;
* \(S\) denotes the global state space and \(s\in S\) denotes the current and specific state;
* \(\mathbf{A}:\{A^{(1)},A^{(2)},\cdots A^{(n)}\}\) denotes all agents' joint action space and \(a^{(i)}\in A^{(i)}\) is the action of the \(i\)-th agent;
* \(T:P(s^{\prime}|s,\mathbf{a})\rightarrow[0,1]\) is the state trasition probability function from state \(s\) to next state \(s^{\prime}\) given joint action \(\mathbf{a}:\{a^{(1)},a^{(2)},\cdots a^{(n)}\}\);
* \(\mathbf{R}(s,\mathbf{a}):\{r^{(1)},r^{(2)},\cdots,r^{(n)}\}\) denotes the joint reward function by executing the joint action \(\mathbf{a}\) given state \(s\);
* \(\mathbf{O}:\{O^{(1)},O^{(2)},\cdots O^{(n)}\}\) represents all agents' joint observation space, and \(o^{(i)}\in O^{(i)}\) denotes the local observation of the \(i\)-th agent;* \(Z:o^{(i)}=Z(s,i)\) is the local observation function of the \(i\)-th agent given the global state \(s\);
* \(\gamma\in[0,1]\) denotes the constant discount factor.
## 3 Results and Discussion  
The effect of fermentation parameters on glycerol bioconversion in fed-batch cultures of _C. freundii_ AD119 was investigated. The strain and the medium were described in previous reports [21; 22]. The strain was selected from a pool of 3800 isolates, which included microorganisms belonging to the genera _Klebsiella_, _Citrobacter_, _Hafnia_[21]. As no improvement was observed when glycerol was supplemented with co-substrates [22], the medium used in this study contained glycerol as the sole carbon source. The goal of this study was to establish optimal conditions for the process of 1,3-PD production from crude glycerin obtained from an industrial biodiesel production site. At the same time, the accumulation of 3-HPA, a toxic metabolic intermediate, was monitored, and the impact of its concentration on the performance of the fermentation was studied.  
The choice of an industrial by-product as the carbon source was not accidental. Such a substrate contains various impurities which introduce the risk of a negative impact on the microorganisms used for fermentation, such as methanol, mineral salts, heavy metals, mono- and diglycerides of fatty acids, free fatty acids, soaps [25]. Nonetheless, it is considered an attractive substrate for bioconversion, and, as such, it was used in this study. Reports exist which indicate that the substitution of pure glycerol with crude glycerin leads to decreased yields of biomass and 1,3-PD and affects the profile of metabolites [26; 27; 28]. On the other hands, [PERSON] et al. [29] reported improved bioconversion of glycerol to 1,3-PDwhen crude glycerin was used instead of the pure substance. In the same paper, it was also shown that bacteria belonging to the genus _Klebsiella_ show greater resistance to the impurities of the industrial by-product than _Clostridium_ spp. Nevertheless, our previous findings indicate no interferences from the chosen substrate on glycerol bioconversion to 1,3-PD in the case of the strain used here [22]. Moreover, as no improvement was observed when glycerol was supplemented with co-substrates, the medium used in this study contained glycerol as the sole carbon source.
# Health State Prediction Method Based on Multi-Featured Parameter Information Fusion
## 2 Methods  
### Bi-LSTM  
LSTM is a variant of RNN and can solve the problem of gradient explosion in RNN processing of long-term time sequences. The structure is shown in Figure 2.  
It converts ordinary hidden nodes into storage units, which overcomes the difficulties in training models for RNN. The key to LSTM is the cell state and various gate structures, including forgetting gates, input gates, and output gates. LSTM achieves the function of filtering out redundant information by adding and deleting the information of the cell-like \(C_{t}\) through the structure of \"gates\". The process of information transfer and update is as follows:  
\[i_{t}=\sigma(W_{i}\cdot[y_{t-1},x_{t}]+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[y_{t-1},x_{t}]+b_{f}\Big{)} \tag{2}\]  
\[o_{t}=\sigma(W_{o}\cdot[y_{t-1},x_{t}]+b_{o}) \tag{3}\]  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[y_{t-1},x_{t}]+b_{C}) \tag{4}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{5}\]  
\[y_{t}=o_{t}tanh(C_{t}) \tag{6}\]  
In the above equation, \(i_{t}\) is the input gate at time \(t\), and \(\sigma\) is the nonlinear activation function. \(W_{i}\) is the weight matrix of the input gate, and \(b_{i}\) is the bias vector of the input gate. \(y_{t-1}\) is the hidden state of the previous time step, which carries the information of the previous time step. \(x_{t}\) is the input data for the current time step. Similarly, \(f_{t}\), \(o_{t}\), represent the forget gate and output gate at time t. Matrices \(W\) and \(b\) represent the respective gate weight matrix parameters and bias terms, respectively; \(\widetilde{C}_{t}\) is the candidate cell state, \(C_{t}\) denotes the new cell state, and \(y_{t}\) is the new hidden state. LSTM networks, although they can deal with time series and achieve predictive effects, have a unidirectional structure and therefore can only deal with unidirectional time-series data, and such a structure is a clear limitation for prediction because effective prediction usually involves mining information from both historical and future data to infer future trends. The unidirectional LSTM cannot obtain any information from future inputs. In contrast, the bidirectional long and short-term memory network (BiLSTM), which can consider both past and future information, better captures the interdependencies in the data, with the structure shown in Figure 3.  
It includes an output layer, a forward LSTM layer, a backward LSTM layer, a hidden layer, and an input layer. The forward layer is responsible for parsing the dynamic pattern of forward development in the sequence, while the backward layer analyzes the reverse trend of the sequence. BILSTM synthesize the data from both temporal directions at any given point. When information from these two directions is fused at the output layer, BILSTM provides a more comprehensive and in-depth contextual understanding by synthesizing past and future information to make decisions.  
Figure 2: Structure of the LSTM network.
## 2 Material and Methods
### Fna  
US-guided FNA was performed in all cases by an endocrinologist in our department, who has more than 20 years of experience. The patient was positioned in the supine position and a pillow was placed beneath their shoulders, in order to get the best possible hyperextension of the neck. Firstly, a thorough disinfection of the skin was performed, followed by local anesthesia using topical application of 2 g of 5% prilocaine/lidocaine anesthetic cream on the skin at the side of the nodule. The transducer probe had a sterile covering to protect the patient when locating the lesion. Very fine sterile needles (25 gauge) attached to 10 cc syringes were used. After locating the nodule on US, the needle was introduced parallel to the probe, using the anterior technique. US guidance was utilized to visualize the needle and confirm that it had entered the nodule. \"Coring\" movements were executed with the needle within the lesion for the cellular material to be gathered. At least two punctures per nodule were made, with the obtained material spread on glass slides and fixed in 95% ethyl alcohol (minimum 5 slides per nodule). The quality of the obtained material was not assessed at collection. An experienced cytologist in the field reviewed the specimens and reported the findings according to the Bethesda System for Reporting Thyroid Cytopathology. All samples were reliably assigned to one of the five categories. The indeterminate categories were considered Bethesda III and IV. Findings in this group were included in the study.