esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Coal Permeability Dependence on Depth  
The coal permeability dependence on depth was estimated. Calculations were performed considering the experimentally determined cleat compressibility \(C_{f}\) = 0.0845 MPa\({}^{-1}\), the permeability of unloaded coal perpendicular to the butt cleat direction (74.6 mD), the 1.5 permeability anisotropy ratio, [PERSON]'s ratio of the tested coal \(\mu\) = 0.31, a lithostatic gradient appropriate for sedimentary basins (0.0231 MPa/m), and a hydrostatic gradient of the fluid pore pressure (0.01 MPa/m). For assumed pressure gradients, the vertical stress \((\sigma_{v})\) is (Equation (8))  
\[\sigma_{v}=(0.0231-0.01)\cdot d=0.0131\cdot d \tag{8}\]  
where \(\sigma_{v}\) is vertical stress, MPa, and \(d\) is the depth, m ([PERSON], 1992).  
For the model that describes the stress regime, defined by uniaxial strain and mean stress, the permeability was determined from Equation (6) ([PERSON], 1992). Considering the experimental data, the tested coal permeability depth dependencies perpendicular to the butt and face cleat directions were (Equations (9) and (10))  
\[k_{f}=74.6/\exp[0.00206\cdot d] \tag{9}\]  
\[k_{b}=50.4/\exp[0.00206\cdot d] \tag{10}\]  
Figure 9: Dependence of coking coal permeability (S. D. Tihov mine, Leninsky area, Kuzbass) (k) on the hydrostatic confining pressure (P) according to the [PERSON] et al. (1992) model and the microstructural analysis data: the blue solid line corresponds to the permeability perpendicular to the butt cleat direction; the red solid line corresponds to the permeability perpendicular to the face cleat direction.  
where \(k_{f}\) is the permeability of the face cleats, mD; \(k_{b}\) is the permeability of the butt cleats, mD; and \(d\) is depth, m.  
Assuming the [PERSON] (2004) model [20], in which the permeability is controlled by the horizontal stresses, the coal permeability depth dependence can be described by Equation (7).  
The corresponding coal permeability depth dependencies perpendicular to the butt and face cleat directions (Equations (11) and (12)) are, respectively,  
\[k_{f}=74.6/\exp[0.00142\cdot d] \tag{11}\]  
\[k_{b}=50.4/\exp[0.00142\cdot d] \tag{12}\]  
where \(k_{f}\) is the permeability of the face cleats, mD; \(k_{b}\) is the permeability of the butt cleats, mD; and \(d\) is depth, m.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 4 Numerical Results and Discussion
Figure 4: The average BER and outage probability performance versus transmission distance for various transmit powers: (**a**) the average BER performance; and (**b**) the outage probability performance. Figure 6 shows the average BER versus the signal frequency \(f\) for the WPCN-based UWAC systems under various transmission distances \(d\) and various spreading factors \(m\). Similar to Figure 5, it can be observed that there exists an optimal value of signal frequency (which is marked with a bold circle) for the systems to obtain the minimum average BER. Specifically, a smaller optimal signal frequency is preferred for a larger transmission distance for the WPCN-based UWAC systems, while the optimal signal frequency remains invariant when the value of the spreading factor changes. The reason is that both the path loss \(A(d,f)\) and noise \(N(f)\) are frequency-dependent, and when the signal frequency increases, the path loss \(A(d,f)\) increases whereas the noise \(N(f)\) decreases, and thus there exists an optimal frequency to obtain the maximum SNR and minimum average BER. According to Equation (3), the spreading factor only acts on the transmission distance and does not affect SNR when the signal frequency changes. To examine the impact of the Doppler shift \(f_{d}\) on the performance of the WPCN-based UWAC systems, we plot the BER against the Doppler shift for the systems with various  
Figure 5: Achievable rates versus time allocation factors: (**a**) impact of various transmit powers (\(d\) = 80 m); and (**b**) impact of various transmission distances (\(P_{d}\) = 170 dB re uPa). Figure 6: The average BER versus signal frequency (\(P_{d}\) = 200 dB re uPa): (**a**) impact of various transmission distances; and (**b**) impact of various spreading factors (\(d\) = 300 m). signal bandwidths in Figure 7. Not surprisingly, it can be observed that the average BER performance is affected by the Doppler shift and that the BER increases as the Doppler shift increases. It can also be observed that as the signal bandwidth increases, the interference coming from the Doppler shift becomes less severe. Specifically, when the Doppler shift increases from 0.2 Hz to 0.6 Hz, the average BER increases by 1.69% for the system with a signal bandwidth of 20 kHz, while the average BER increment is 35.21% when the signal bandwidth is 5 kHz. The reason is that, for a certain number of subcarriers, the subcarrier spacing increases as the bandwidth increases. Thus, the interference between adjacent subcarriers will be less severe.
## 2 Ikeda Map Used as a Basis for a Pseudo-Random Generator
### Description of the Pseudo-Random Byte Algorithm  
The pseudo-random byte generator includes five steps [16]:  
1. The initial values and parameters forming the key set for the two Ikeda functions were obtained--\(x_{1,0}\), \(y_{1,0}\), \(x_{2,0}\), \(y_{2,0}\), \(u_{1}\) and \(u_{2}\);
2. More than 100 iterations of the Ikeda maps were made without retrieving any results;
3. The work of the algorithm continues with more iterations of the Ikeda maps to calculate and post-process the four real values--\(x_{1,i}\), \(y_{1,i}\), \(x_{2,i}\), \(y_{2,i}\). They are calculated as follows: \[a=mod(abs(integer(x_{1,i}\times 10^{10}))),256)\] (4) \[b=mod(abs(integer(y_{1,i}\times 10^{10}))),256)\] (5) \[c=mod(abs(integer(x_{2,i}\times 10^{10}))),256)\] (6) \[d=mod(abs(integer(y_{2,i}\times 10^{10}))),256)\] (7) where \(integer(z)\) returns the integer part of \(z\)--truncating the value at the decimal point--\(abs(z)\) returns the absolute value of \(z\), and \(mod(z,w)\) returns the reminder after division;
4. Output byte \(s_{i}\) is generated when XOR operations are performed between \(a,b,c\), and \(d\): \(s_{i}=a\oplus b\oplus c\oplus d\);
5. Perform Step 3 until the output stream is reached.  
Figure 3: Lyapunov exponent of the Ikeda map.  
Theoretical and statistical tests were performed to determine the cryptographic security of the proposed generator and can be found in [16].
## 3 Results
### Isolation and Structure Elucidation
The remaining assignments for aromatic carbons C(2), C(2\({}^{\prime}\)), C(6), and C(6\({}^{\prime}\)), as well as for carbonyl carbons C(7) and C(7\({}^{\prime}\)), were deduced by applying the known substituent parameters and basic additive rules [22] for predicting the chemical shifts of atomic nuclei in organic compounds. Eventually, compound **1** was identified as 3,3\({}^{\prime}\)-trimethoxy-4-sulphoxyellagic acid potassium salt (Figure 1). The molecular formula of compound **2** was established to be C\({}_{17}\)H\({}_{11}\)O\({}_{11}\)S by HR-ESI-TOF-MS in the negative-ion mode (observed 423.0009 [M-K]\({}^{\prime}\), calculated 423.0022). The NMR data of **2** were closely similar to that of **1** apart from the presence of additional three-proton (\(\delta_{H}\) 3.9) and carbon (\(\delta_{C}\) 56.7) signals correlated with each other in HMQC spectrum. Based on the HMBC spectrum it was established that the signals correspond to the methoxy group in C(4\({}^{\prime}\)) position (Table 1). Therefore, the  
Figure 1: The structure of isolated compounds. structure of compound **2** was recognised as 3,3',4'-dimethoxy-4-sulphoxyellagic acid potassium salt (Figure 1). The present work is the third report on the occurrence of both sulphate derivatives in the plant kingdom, and in the case of compound **2**, the first report on its full NMR data. In the previous studies, compound **2** was isolated from the root of _Potentilla candicans_, Rosaceae [9] and the aerial parts of _Euphoria sororia_, Euphoriaceae [13], while compound **1** was obtained from the latter tissue and the wood of _Tamarix tetragyna_, Tamaricaceae [10].
## 4 Visual-Inertial Odometry
### Inertial Measurement and Initialization  
The frequency of IMU detection data is usually higher than that of the camera. In order to avoid the repetition of IMU integration, the VIO system still adopts preintegration method. The acceleration value \(a_{ij}\) and angular velocity value \(\omega_{ij}\) measured by the IMU are added between two image frames \(s_{i}\) and \(s_{j}\). The visual observations and inertial measurements are combined to further estimate the pose of the most nearest frame. According to the non-linear dynamic model [35], establish the error term of speed and linear acceleration, obtaining the prediction \(\hat{s}_{j}\) and covariance \(\sum_{s,j}^{\wedge}\). The inertial error function is:  
\[E_{inertial}\big{(}s_{i},s_{j}\big{)}:=\big{(}s_{j}\boxdot s_{j}\big{)}^{T}s \sum_{s,j}^{\sim-1}\big{(}s_{j}\boxdot s_{j}\big{)} \tag{19}\]  
Accurate initial values are crucial for guiding monocular systems. Monocular cameras cannot recover environmental scale information, but can use inertial data to obtain metric scale and gravity direction. However, there is inevitably bias in the IMU, so these quantities need to be properly initialized before processing in a tightly-coupled system. To address the issue that monocular visual-inertial systems cannot be immediately initialized during constant velocity or zero-acceleration motion, the scale and gravity direction are considered as parameters of the system, and optimizes them together with pose to achieve initialization at any scale. The parameters that need to be initialized include the rough pose estimate between two frames, the average depth of nearby points, the initial gravity direction, velocity, scale, and IMU bias. Before initialization, the visual-inertial frame needs to be converted to the metric frame, with a transformation matrix of \(T_{m,d}\in\left\{T\in SIM(3)\left|translation(T)=0\right\}\right.\), where \(\xi_{m,d}=\log(T_{m,d})\in sim(3)\). The average depths are all normalized to 1, the gravity direction is calculated using acceleration measurements, the velocity and IMU bias are 0, and the scale is 1. Then, all variables and parameters at the initial frame are transformed into the world frame, and these values based on real scale will continue to be used in the VIO system.
## 3 Experimental Results and Discussion
### Dataset
#### 3.1.1 Data Set Division  
According to the size of the PCB defective circuit board data set, the division ratio of the training set, validation set, and test set is set to be about 8:1:1. A total of 8640 pictures are set as the training set, 961 pictures as the validation set, and 1067 pictures as the test set. To ensure the reliability and fairness of the experiment, by randomly disrupting the dataset while dividing the dataset, the sequentiality and correlation of the data can be eliminated, and the model is less affected by the order in which the data are arranged.
## 4 Experimental Process and Discussion
### Detection Limit and Precision  
To test the minimum detection limit (MDL), 50 ppmv C\({}_{2}\)H\({}_{2}\) is introduced into the gas chamber. The 2f signal detected by the lock-in amplifier is shown in Figure 7a. When the C\({}_{2}\)H\({}_{2}\) concentration is 0 ppmv, the 2f signal is as shown in Figure 7b. The 2f SA is 1.206 V when the C\({}_{2}\)H\({}_{2}\) concentration is  
Figure 6: **(a)** Relationship between 2f signal’s amplitude (SA) and concentration C\({}_{2}\)H\({}_{2}\) (60 samples per group), and detection data recorded every 5 s. **(b)** Fitted curve of the 2f SA and concentration C\({}_{2}\)H\({}_{2}\).  
50 ppmv, and the system noise's amplitude (NA) of 0 ppmv is 0.007 V. The SNR is 44.725 dB, and the MDL is as follows: 50 ppmv \(\times\) NA / SA \(\approx\) 0.3 ppmv.  
He, [PERSON] et al. used a 1.534-\(\mu\)m tunable diode laser and a miniature gas chamber to achieve C\({}_{2}\)H\({}_{2}\) detection in Reference [19], and the minimum detectable absorption is \(2.66\times 10^{-3}\). In this paper, different concentrations of C\({}_{2}\)H\({}_{2}\) were introduced into the gas chamber, and the minimum detectable concentration is 0.3 ppmv. Therefore, the minimum detectable absorption is \(3.01\times 10^{-3}\).  
To test the precision of the detection system, nine C\({}_{2}\)H\({}_{2}\) samples at 10, 15, 25, 35, 45, 55, 75, 95, and 100 ppmv were prepared. The detection system calculated the concentration values according to the fitting formula. The error analysis of the detection results is shown in Figure 8. The blue line indicates the absolute error of the experimental results, and the red line reflects the relative error. The maximum absolute error is 0.3 ppmv, and the maximum relative error is within \(\pm\)2%. Therefore, the precision is 2% in the detection range from 10 to 100 ppmv.
# A Review of Oil Spill Remote Sensing
## 3 Use of Active Sensors for Oil Spill Detection and Mapping
### Ship-Borne Radar Oil Spill Detection  
Ship-borne radar was in existence for many years and only recently has been modified to include oil spill detection and mapping. Ship-borne radar has a range of 8-30 km, as a function of antenna height [56]. Ship-borne radars are becoming a dependable method of detecting and mapping slicks close to the recovery vessels and fulfilling a tactical need for imagery directly on recovery ships.
## 6 Calibration in a Stochastic Environment Under Sensor Nonlinearities
### Gain Calibration
#### 6.3.2 Measurement Noise  
In the case of gain calibration, measurement noise has a qualitatively different impact on the calibration results compared with the communication noise. Namely, the recursion for \(\hat{g}_{t}\) now contains a term resulting from the multiplication of the identical noise terms in \(\varepsilon_{t}^{A,ij}\Delta y_{t}^{i}\) and \(\Delta y_{t}^{i}\), implying a nonzero parameter estimation bias and lack of convergence to consensus [33; 34; 36; 45; 47; 49; 61]. In order to overcome this problem, the _instrumental variable_ methodology has been proposed, leading to the following alternative of (16):  
\[\hat{a}_{t+1}^{i}=\hat{a}_{t}^{i}+\delta_{t}^{i}\sum_{j\in\mathcal{N}_{t}^{ in}}\gamma_{ij}\varepsilon_{t}^{A,ij}Z_{t}^{i} \tag{33}\]  
where \(Z_{t}^{i}\) is the instrumental variable, which should be correlated with \(\Delta x_{t}^{i}\) and uncorrelated with \(\hat{\varsigma}_{t}^{i}\)[49]. It has been shown that the choice \(Z_{t}^{i}=\Delta y_{t-\tau}^{i}\) for \(\tau\geq 2\) is surprisingly simple and effective (see, e.g., the simulation results below). A compact form of the recursion for \(\hat{g}_{t}\) is then  
\[\hat{g}_{t+\tau}=(I+\delta_{t}\Delta x_{t-\tau}\alpha^{2}\Gamma)\hat{g}_{t}+ \delta_{t}m_{t}, \tag{34}\]  
where \(\tau\geq 2\), \(m_{t}=[m_{t}^{1}\cdots m_{t}^{n}]^{T}\) with \(m_{t}^{i}=\delta_{t}\sum_{j}\gamma_{ij}\) (\(\hat{a}_{t}^{j}\Delta\zeta_{t}^{j}-\hat{a}_{t}^{i}\Delta\zeta_{t}^{i}\)) (\(\alpha^{i}\Delta x_{t-\tau}+\Delta\zeta_{t-\tau}^{i}\)). It is not difficult to verify that \(\{m_{t}\}\) is a zero-mean independent random sequence, as a consequence of the introduction of the instrumental variables. Consequently, we have the following important result:  
**Theorem 7**.: _Let A2-A7 hold and let (1) hold with \(v_{t}^{i}=0\), \(i=1,\ldots,n\); let the measurement and communication noises be present. Then, algorithm (34) converges to consensus in the mean square sense._  
The proof formally follows the proof of Theorem 5.  
Assumptions for achieving B.I.B.O. stability do not impose any significant restriction in practice (pay attention to Assumptions A1-A3). Notice, however, that the B.I.B.O. stability does not presuppose, by definition, convergence to zero of the input variables. According to Assumption A2, we assume only the boundedness of the nonlinear term in a very general sense. We do not believe that, after proving the above general Theorems, any additional quantitative analysis of the error bounds has evident sense without specifying eventual specific aspects, including the characteristics of the sensor, the measured signal, and the environment.
## 3 Results and Discussion
### Flavonols
#### 3.5.2 Isoflavonoids  
Based on MS/MS data, seven isoflavonoid compounds (from **24-30**) were discovered in this investigation in the negative mode of ionization. Compounds **24** and **25** showed [M\(-\)H]\({}^{-}\) at \(m/z\) 417.1191 and 271.0964 were putatively identified as equal 4\({}^{\prime}\)-\(O\)-glucuron and 3\({}^{\prime}\)-\(O\)-methylquol, respectively, in mint leave only. Compound **26** (6\({}^{\prime\prime}\)-\(O\)-acetylglycitin, \(m/z\) 487.1278) was identified in mint and fenugreek leaves. A precursor ion of compounds **27**, **29**, **30** (\(m/z\) 285.0418, 271.0611, 315.0866) was identified in mint leaves as 3\({}^{\prime}\)-hydroxygenistein, 3\({}^{\prime}\)/4\({}^{\prime}\)-7-trihydroxyisoflavanone and violanone, respectively, in negative mode. A previous study also reported about this compound as violanone has been found to have specific properties that make it potentially valuable for medicines, for instance, its anti-inflammatory and antimicrobial effects. Its chemical structure also allows it to readily bind to another molecule, which may have implications for potential use in drug development. Compound **28**, having chemical formula C\({}_{15}\)H\({}_{10}\)O\({}_{5}\), was identified at \(m/z\) 269.0450 in the negative mode, which was tentatively categorized as 3\({}^{\prime}\)-hydroxydiadzein in fenugreek seeds and basil plus mint leaves.
## 2 Materials and Methods  
### Subjects and Data Acquisition  
Resting-state fMRI data were collected from 20 subjects who were licensed taxi drivers for more than 1 year and 20 nondrivers (i.e., controls). Considering that the demographic variables such as gender, age and education level would affect the intrinsic functional architecture of brain, we conducted statistical analysis on the demographic parameters (i.e., age, sex and education) of these subjects, showing the group mean \(\pm\) standard deviation, as well as the significance level (i.e., \(p\) value) in Table 1. Two-sample \(t\)-test and Pearson's Chi-squared test were correspondingly applied for group comparison. It can be seen that there is no significant differences in demographic parameters between the two groups (see Table 1); therefore, the subsequent results are unlikely to be influenced by the between-group differences of these uninteresting variables. All subjects signed written informed consent forms, and this study was approved by the Institutional Review Board of the Southwest University. No subjects had mass lesions (including tumors, vascular malformations or malformations of cortical development), brain trauma or history of alcohol or drug dependence.  
Resting-state fMRI scans were acquired at a 3-T SIEMENS MRI scanner. All subjects were instructed to stay awake and keep their eyes closed; no other task instruction was provided. The functional images were collected using a gradient-echo echo-planar pulse sequence sensitive to blood oxygen level-dependent (BOLD) contrast with the following parameters: TR/TE = 2000/30 ms, thickness/gap = 3.0/0 mm, FOV = 200 \(\times\) 200 mm\({}^{2}\), FA = 90\({}^{\circ}\), matrix = 64 \(\times\) 64, slices = 32, time of scanning = 8 min, and volumes = 240.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variable** & **Drivers** & **Controls** & \(p\)**Value** \\ \hline Number of subjects & 20 & 20 & \\ Age (years) & 39.5 \(\pm\) 5.8 & 41.1 \(\pm\) 5.0 & 0.34  \\ Sex (male/female) & 20/0 & 18/2 & 0.15  \\ Education (years) & 9.5 \(\pm\) 1.8 & 9.0 \(\pm\) 1.4 & 0.37  \\ Duration of taxi driving (years) & 4.9 \(\pm\) 3.5 & & \\ Duration of total driving (years) & 11.7 \(\pm\) 4.9 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The demographic parameters of the subjects.
## 4 Experimental Results
Figure 15: Measured power factor and current total-harmonic distortion (THD) of the presented LED tube lamp driver under different input utility-line voltages. The comparisons between the existing T8-Type LED tube lamp drivers and the proposed one are shown in Table 3. A flyback-type AC-DC converter as an LED tube lamp driver is presented in [18], and a buck-type AC-DC converter as an LED tube lamp driver is presented in [19]. In addition, these existing LED tube lamp drivers ([18; 19]) operate with universal input voltages while the proposed version operates with American utility-line voltages. Although the circuit components counts in the proposed driver are higher than these existing ones, this table illustrates that the proposed single-stage LED tube lamp driver has smaller input current THD and achieves soft-switching on the power switches to increase circuit efficiency over the two existing versions.
## 5 EI-RNN Optimization and Analysis
### EI-RNN Training
#### 5.1.1 Initial Settings  
From a set of cleaned and structured datasets and architectures, we initiated a training procedure with a simple set of hyperparameters: a number of hidden units of the RNN cell of 8, a number of layers of 1, and the number of input features to be processed at every time step \(t\). The time steps of the RNN cell (125 for the DSA dataset) was given by the dimension of the input features (5625)/number of features to be processed (45). Furthermore, we set the number of epochs (300), batch size (128), optimizer (Adam/RMS Prop), and learning rate (0.01) based on references. The activation functions of each RNN are given in Section 2. We used the random normal initialization of the RNN weight matrices. We adopted the cross-entropy loss function since the problem was a classification problem and the output function was a softmax nonlinearity function.
## 3 Construction and Validation of Coupled Methods
[PERSON] [28] adopted the adaptive mesh method with local region refinement to model the flow field around a vehicle, with flow field boundary conditions and grid division depicted in Figure 5. The grid division employs block grid technology to generate high-quality adaptive grids for the external flow field, with grid refinement occurring in the region of spatial motion trajectory where the towed cable is located. The minimum grid resolution is set at \(1/10\) of the cable diameter, with spatial distribution range of the grid updated with time steps. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Physical Parameters of Towed Cable**} & \multicolumn{2}{c}{**Physical Parameters of Towed Body**} \\ \hline Length & 0.5 m/6 m & Mass & 0.0001 kg \\ Diameter & 0.01 m & Volume & 0.00015 m\({}^{3}\) \\ Weight of per unit length & 0.0628 kg/m & Drag coefficient & C\({}_{\text{dx}}\) = 1.0, C\({}_{\text{dy}}\) = 1.0, C\({}_{\text{dz}}\) = 1.0 \\ Bending/Torsional stiffness & 0 kN-m\({}^{2}\) & Towed speeds & 4 m/s \\ Axial stiffness & 110 kN & Fluid density & 1024 kg/m\({}^{3}\) \\ Poisson ratio & 0.28 & Kinematic viscosity & 1.05 \(\times\) 10\({}^{-3}\) Pa-s \\ C\({}_{\text{n}}\)/C\({}_{\text{t}}\) & 1.8/0.025 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the towed system. Figure 5: Computational domain mesh. Referring to the calculation and verification of the flow field around SUBOFF [29; 30], the velocity profile of the wake field, pressure coefficient distribution, and velocity vector distribution map behind the enclosure were compared with experimental data. The total number of 3D meshes used was 780,320. The region where SUBOFF's tail wake emanates is densely discretized. The y+ is judiciously assigned values, resulting in a fine grid node division at the main hull bow, around the hull, and in the self-propelled wake drag cable path area, where local grid refinement is employed to satisfy computational accuracy requirements.