esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology of SSI-COV
### Implementation of SSI-COV
Figure 1: Methodological scheme of the SSI-COV application for structural health monitoring. To evaluate the stability of the modes, two scenarios with different tolerance settings were analyzed. In the first scenario, a tolerance of 0.02 was used for the frequency, 0.05 for damping, and 0.02 for the MAC, while in the second scenario, a stricter tolerance of 0.01 was adopted for all parameters, allowing for greater precision in the identification and confirmation of the system's dynamic modes. Table 1 shows the results of the modal analysis from the ETABS model, where the mode shapes and corresponding frequencies with significant modal participation are listed. UX and UY represent the percentage of modal participation in the X and Y directions, respectively, indicating the extent to which each mode contributes to the dynamic response of the structure along these principal axes. This distinction allows for a better understanding of how the structure responds to excitations in each direction, aiding in the interpretation of the vibrational behavior.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 2 Interference Management  
### Related Work  
The simplest scenario for interference management systems includes a SOI transmitter, an interference transmitter, and a receiver, all equipped with MIMO transmitting/receiving antennas (Figure 1), while is scalable to networks with a larger number of transmitting and receiving nodes. The left MIMO antennas receive \(x_{1}\) and \(x_{2}\), both of which are combinations of SOI \(s_{soil}\) and interference \(s_{int}\) in the same RF band:  
\[X=AS,\text{ or }\left[\begin{array}{cc}x_{1}\\ x_{2}\end{array}\right]=\left[\begin{array}{cc}a_{11}&a_{12}\\ a_{21}&a_{22}\end{array}\right]\left[\begin{array}{c}s_{soil}\\ s_{int}\end{array}\right] \tag{1}\]where \(A\) is the mixing matrix and depends on the channel coefficients. To separate the SOI from the interference is to find the inverse of the mixing matrix, \(S=A^{-1}X\).  
The experimental results include an algorithm for the digital edge device to perform principal component analysis (PCA) and independent component analysis (ICA) with under-sampled signal and the photonic circuit hardware that separates the wideband signals in real-time.
## 4 Systems Implementation and Evaluation
### Experimental Setup  
The method we proposed is implemented in the Python programming language. Our networks are trained on NVIDIA GTX 1080 in a 64-bit computer with Intel(R) Core(TM) i7-6700 CPU @3.4 GHz, 16 GB RAM and the Ubuntu 16.04 operating system.
## 3 Phase Sensitive Detection
### Signal Splitting Algorithm  
To use PSD to split the yaw angle signal in half, the calculation formula is as follows:  
\[\psi_{D}(i)=\left\{\begin{array}{l}\psi^{\prime}(i)\;\psi^{\prime}(i)<0\\ 0\;\;\psi^{\prime}(i)\geq 0\end{array}\right.,\;\psi_{U}(i)=\left\{\begin{array}{l} \psi^{\prime}(i)\;\psi^{\prime}(i)>0\\ 0\;\;\psi^{\prime}(i)\leq 0\end{array}\right.,\;i=1,2,3,\cdots,N_{0}, \tag{5}\]  
\(\Psi_{D}=\{\psi_{D}(1),\psi_{D}(2),\cdots,\psi_{D}(i),\cdots,\psi_{D}(N_{0})\}\) denotes the lower part after the yaw angle signal is decomposed by PSD and \(\Psi_{U}=\{\psi_{U}(1),\psi_{U}(2),\cdots,\psi_{U}(i),\cdots,\psi_{U}(N_{0})\}\) denotes the upper part.  
PSD decomposes \(\Psi_{0}\) into the upper and lower halves by its median, with the upper retaining the terms with \(\Psi^{\prime}>0\) in their amplitudes and filling the others with the median, while the lower half retains terms with \(\Psi^{\prime}<0\) and filling the others with the median. As will be seen later in this article, this signal-processing method can be applied to the layer-by-layer counting of S turns.  
The functions of PSD are shown in Figure 4.
## 4 Discussion
These patients could benefit most from an accurate non-invasive prediction of disease progression to target a preventive more aggressive medical treatment. Our results further support the argument that predictive models, including non-invasive evidence of low ESS, high concentration of LDL, and specific baseline morphological characteristics improve the prediction of plaque progression over traditional risk factors. There is growing evidence that CCTA virtual FFR can be used as a substitute of invasive FFR for the functional assessment of coronary obstructive disease [21, 22, 23]. Our study demonstrates that non-invasive FFR can also be used as a predictor of disease progression and not only for diagnostic purposes. To our knowledge, this is the first time that non-invasive FFR is calculated in a serial patient database for prediction purposes, though the association of higher values of SmartFFR with plaque progression was not significant at multivariate analysis, a finding which possibly requires further investigation. Finally, a novelty of this study is the inclusion of the patients' plasma lipidomics into the development of the predictive models. Previously, it was shown that several circulating lipid species are associated with the diagnosis of CAD and some species were also identified as biomarkers of disease progression [24]. Our work confirms that lipidomics can be used to predict plaque progression. Its combination with the morphological and computational CCTA parameters increased to 88% the predictive accuracy of the final model. In our work,45.5% of the patients were under statins treatment. These patients were either dyslipidemic or obstructive CAD patients. Statin use was associated with an increased progression of calcified coronary plaque and a reduced progression of non-calcified coronary plaque as already reported for the whole SMARTool population [11]. Therefore, overall plaque area, plaque burden and lumen area changes were not significantly affected by statin use. This work has some main limitations. First, about half of the population was under treatment with statins and all patients under antianginal and/or risk factor modifying medication during the follow-up period. Treatment is expected to variably affect plaque progression beyond baseline morphological and functional coronary features. Another limitation is that the threshold to define progression was selected arbitrary. In this study, we set the threshold considering the distribution of disease progression among the patients as well as previously selected cut-off values (6). Further work is needed with a reverse engineering analysis approach to identify the optimal threshold, which enables the prediction of disease progression with greater accuracy and reliability.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 1 Introduction
Accordingly, this study contributes to the literature by providing a comprehensive statistical analysis of the historical series of vessel transits through this maritime route, thereby helping to expand the limited body of scientific research on vessel traffic through the Panama Canal. Furthermore, it lays the groundwork for future research in this area. The results show that both the SARIMA and NNAR models exhibit superior predictive performance and better statistical fit compared to the Holt-Winters model for forecasting vessel traffic through the Panama Canal.
# Charge Carrier Distribution in Low-Voltage Dual-Gate Organic Thin-Film Transistors  
[PERSON]  
1 Research Center for Organic Electronics (ROEL), Yamagata University, 4-3-16 Jonan, Yonezawa, Yamagata 992-8510, Japan; [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (Y.T.)  
[PERSON]  
1 Research Center for Organic Electronics (ROEL), Yamagata University, 4-3-16 Jonan, Yonezawa, Yamagata 992-8510, Japan; [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (Y.T.)  
[PERSON]  
1 Research Center for Organic Electronics (ROEL), Yamagata University, 4-3-16 Jonan, Yonezawa, Yamagata 992-8510, Japan; [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (Y.T.)  
[PERSON]  
1 Research Center for Organic Electronics (ROEL), Yamagata University, 4-3-16 Jonan, Yonezawa, Yamagata 992-8510, Japan; [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (Y.T.)  
[PERSON]  
2 Yokohama R&D Center, Mitsubishi Chemical Corporation, 1000 Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa 227-8502, Japan; murase.tomohide.mv@m-chemical.  
contact resistance which is associated with gate-voltage [16]. The electrical behaviors of dual-gate organic transistors (DGOTFTs) have been studied, including its analytical model [17; 18], contact resistance [19], charge transport and back-gate-bias effects [20]. However, these studies are based on OTFTs that were operated at high-voltage (>10 V): the operation analysis in low-voltage (<5 V) DGOTFTs is never reported. This possible reason is that fabrication of low-voltage DGOTFTs is generally challenging because vertical multi-layer-stacking of thin gate dielectric layers would be required.  
In this study, charge carrier distribution in low-voltage OTFTs with BG, TG, and DG architecture was reported. Low-voltage DGOTFTs were successfully fabricated by employing 140-nm-thick parylene film as gate dielectrics formed by chemical vapor deposition (CVD). Numerical simulation of the carrier distribution was carried out based on a finite element method (FEM). The charge carrier distributions were successfully imaged. The simulation results indicated that DG system produces a dual-channel and has excellent control of charge carrier density in the organic semiconducting layer, which leads to the better switching characteristics of the OTFTs than the single-gate devices.
# An FPGA Implementation of a Convolutional Auto-Encoder
## 2 FPGA Implementation of the CAE
### Hardware Framework  
As shown in Figure 2, the encoder part of the proposed CAE framework contains five main modules: zero padding, a channel distributor, convolutional encoding, a channel arbitrator, and an output controller. The detailed explanation is as follows:  
1. Zero Padding Module  
To prevent losing the boundary information during the convolution operation, the zero padding module is employed to fill zeros around the input matrix. The obtained matrix is defined as the zero-padding matrix in this paper.
2. Channel Distributor Module  
The zero-padding matrix is sent to the channel distributor module, which contains a channel judger and four first-in, first-out (FIFO) memories. The channel judger interleaves every two single-channel data points and then sends them to the FIFOs in sequence. The FIFOs buffer the interleaved data, and then divide them into eight channels.
3. Convolutional Encoding Module  
The work process of this module is summarized as follows: First, the convolution layers in this module execute the convolution calculation in eight channels at the same time. The convolution kernels, obtained by training CAE, are stored directly in the internal LUT resources. By this means, the frequent data interaction between FPGA and external memory is avoided, and the transmission latency is reduced significantly. Then, in order to introduce nonlinear factors after the convolution calculation, activation functions are integrated into the convolution layers. Specifically, the ReLU function is employed for fast convergence and high accuracy. Finally, the maximum pooling is chosen to retain the main features. The first set of eight-channel data is transformed to the three-channel data after the pooling operation, while the subsequent sets of eight-channel data are transformed to the four-channel data.  
Figure 2: The encoder of the proposed CAE hardware framework. The different colors marked in the zero padding module indicate different filling methods: the first and the last row are filled all zeroes, while the intermediate matrix is padded zeroes only at the beginning and the end of each row. The different colors marked in the channel arbitrator module represent the corresponding four channels for packaging.  
4 Channel Arbiter Module  
The output data of the convolutional encoding module are orderly packaged as the single-channel data, and then sent to the next module by the channel arbiter module for judgment.
5 Output Controller Module  
This module is used to judge whether the data stream is processed completely. The above modules process continuously until the ending data of the zero-padding matrix are handled.
## 2 Materials and Methods
### Synthesis and Characterization
#### 2.2.4 5,5'-Bis(5-(2-ethylhexyl)-thieno[3,4-c]pyrrole-4,6-dione)-2,2'-bithiophene (**8**)  
A 20-mL Biotage microwave vial was charged with 2 (282.3 mg, 0.82 mmol), 5,5'-bis(trubitlyIstannyl)-2,2'-bithiophene (297.7 mg, 0.4 mmol), Pd(PPh34) (46 mg, 0.04 mmol), and toluene (12 mL). The vial was then sealed with a Teflon cap under a nitrogen atmosphere and placed in a Biotage microwave reactor (Biotage, Uppsala, Sweden). Next, the reaction mixture was heated to 170 \({}^{\circ}\)C for 1 h. Once cooled, the mixture was poured into 200 mL of methanol and the precipitate formed was filtered and dissolved in a small amount of dichloromethane. The solution was loaded onto silica gel and purified by flash chromatography using a dichloromethane/hexane gradient. Finally, the fraction was collected and dried, resulting in a red solid. Recovered yield: 200 mg (72%). 1H-NMR (400 MHz, chloroform-d, _s_): 7.95 (d, 2H, CH), 7.66 (s, 2H, CH), 7.29 (d, 2H, CH), 2.76 (t, 4H, CH2), 1.86-1.78 (br m, 2H, CH), 1.42-1.23 (br m, 16H, CH2), and 0.98-0.83 (br m, 12H, CH3). LRMS (FD) m/z, calculated for C36H40N2O2S4 (M+): 692; found: 692.  
2.5 5,5'-Bis[3-(5'-hexyl-2,2'-bithiophene-5-yl)-5-(2-ethylhexyl)]-thieno[3,4-c]pyrrole-4,6-dione]-2,2'-bithiophene (**M1**)  
A 5-mL Biotage microwave vial was charged with 3 (173.4 mg, 0.25 mmol), 5-bromo-5'-hexyl-2,2'-bithiophene (181.1 mg, 0.55 mmol), trans-di(u-acetato)bis[o-(di-o-tolylphosphino)benzylldipalladium(II) (4.71 mg, 4 mol %), tris(o-methoxyphenylyl)phosphine (7.04 mg, 8 mol %), cesium carbonate (162.9 mg, 0.5 mmol), and tetrahydrofuran (2 mL). The vial was then sealed with a Teflon cap under nitrogen atmosphere and placed in an oil bath. Next, the reaction mixture was heated to 120 \({}^{\circ}\)C for 24 h with vigorous stirring. Once cooled, the mixture was poured into 200 mL of methanol and the precipitate was filtered and loaded onto silica gel for purification by flash chromatography using a dichloromethane/hexane gradient. Finally, the fraction was collected and dried, resulting in a dark purple solid. Recovered yield: 152 mg (51%). 1H-NMR (600 MHz, 1,1,2,2-tetrachloroethane-d2, 8): 7.86 (d, 4H, CH), 7.14 (d, 2H, CH), 7.05 (d, 2H, CH), 7.00 (d, 2H, CH), 6.70 (d, 2H, CH), 3.54 (d, 4H, CH2), 2.78 (t, 4H, CH2) 1.87 (br m, 2H, CH), 1.71-1.64 (br m, 4H, CH2), 1.42-1.29 (br m, 28H, CH2), and 0.97-0.89 (br m, 18H, CH3). 13C-NMR (125 MHz, 1,1,2,2-tetrachloroethane-d2, 8 ppm): 162.20, 146.82, 141.14, 138.93, 135.72, 134.33, 133.37, 131.92, 130.91, 130.43, 129.80, 128.29, 127.56, 124.86, 124.42, 123.47, 42.45, 37.97, 31.46, 31.21, 30.46, 30.06, 29.62, 28.80, 28.46, 23.72, 23.12, 22.53, 14.11, and 10.44. Elemental Analysis: Calculated (mass %) for C64H72N2O4S8: C, 63.64; H, 7.51; N, 2.32; O, 5.30; S, 21.24. Found: C, 64.4; H, 6.18; and N, 2.51. LRMS (FD) m/z, calculated for C64H72N2O4S8 (M+): 1188; found: 1188.
## 2 Correlation Model of Monitoring Data
### Influence of Monitoring Data Quantity on Reconstruction Error  
In order to discuss the influences of the selection of response variables, two additional cases are discussed. The original stress monitoring data is collected from 7 May 2014 to 10 May 2014 to calculate the correlation coefficients between measurements from location 3-1-4 and the other locations. The stress monitoring data of two additional cases are from 7 May 2014 to 12 May 2014 and from 7 May 2014 to 14 May 2014, respectively.  
The correlation model using stress monitoring data from 7 May 2014 to 12 May 2014 is:  
\[\mathbf{y_{4}}(i)=0.89(\mathbf{x}(i))_{1}+0.09(\mathbf{x}(i))_{2}-0.20( \mathbf{x}(i))_{3}+0.04(\mathbf{x}(i))_{4}-33.62 \tag{49}\]  
The contrast curves between reconstructed values and actual values are shown in Figure 7. The average absolute error is 0.65 MPa and the relative error is 1.99%.  
The correlation model using stress monitoring data from 7 May 2014 to 14 May 2014 is:  
\[\mathbf{y_{5}}(i)=0.35(\mathbf{x}(i))_{1}-0.16(\mathbf{x}(i))_{2}-0.11( \mathbf{x}(i))_{3}+0.03(\mathbf{x}(i))_{4}-39.45 \tag{50}\]  
The contrast curves between the reconstructed values and the actual values are shown in Figure 8. The average absolute error is 0.22 MPa and the relative error is 0.68%.  
The errors of the reconstructed information using stress monitoring data from different time periods are summarized in Table 3.  
It can be seen from Table 3 that the errors become smaller when the quantity of stress monitoring data become larger, in which the average absolute error and relative error are a minimum when the stress monitoring data from 7 May 2014 to 12 May 2014 is chosen to build the correlation model. This indicated that the larger quantity of stress monitoring data should be used, which is better for reconstruction of sensor measurements. In theory, the number of measurements larger than the unknown coefficients is the basic requirement for regression function identifi  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Response Variables** & \(\boldsymbol{e_{a}}\) **(MPa)** & \(\boldsymbol{e_{b}}\) \\ \hline \(\mathbf{X}_{P1}\) & 0.89 & 2.71\% \\ \(\mathbf{X}_{P2}\) & 0.97 & 2.97\% \\ \(\mathbf{X}_{P3}\) & 1.03 & 3.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The summarized errors using different response variables.  
Figure 7: The reconstructed value and the actual value using data from 7 May 2014 to 12 May 2014: (a) the curve of reconstructed value and the actual value; and (b) the curve of reconstruction error.  
noises and temperature effects included in the measurements, the measurements in at least one full day are better.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 1 Introduction
Two small chip antennas are embedded in the smartwatch, with a subwavelength spacing and, therefore, degraded antenna isolation. In the conventional MIMO system in Figure 1a, \(2\times 2\) MIMO channels are established between the two chip antennas and two AP antennas. On the other hand, in the Augmented MIMO system shown in Figure 1b, the dominant multipath MIMO channels are between the two shoulder-mounted antennas and two AP antennas. At the opposite end of the signal paths on the clothing, near the smartwatch, the signal is transferred between two pairs of proximity-coupled chip antennas. Each pair consists of one antenna belonging to the watch and the other to the clothing. Note that the chip antennas in the smartwatch are identical in both Figure 1a,b. Therefore, Augmented MIMO only adds the antenna elements and signal paths with better isolation on the clothing, without disabling anything in the original antenna system. A critical argument that arises with the Augmented MIMO system is whether MIMO performance is indeed improved, even with the closely spaced original MIMO antennas remaining in the signal paths. These antennas may reduce the rank of the MIMO channel, similar to a keyhole effect [13], which degrades the MIMO channel capacity due to a rank-1 signal propagation section. The main contributions of this paper are (1) to introduce the idea that wearable antennas can be used to improve the MIMO performance of small wearable devices; (2) to present specific experimental results that support the validity of the idea, even if the device itself is not directly embedded/attached to clothing and is originally designed to operate in a standalone manner; and (3) to point out the factors that influence its effectiveness as a guide for future research. In this paper, we empirically demonstrate the improvement in MIMO performance with a prototype of Augmented MIMO. In addition, several factors that influence the effectiveness of Augmented MIMO are discussed. The discussion will provide insight into the extensive work that can be conducted in the future on Augmented MIMO.
## 2 Methods
### Coherent Beam Combining
In addition, the simulation shows that the square array beam can also be used for beam combination, and the final beam combination spot is still square, which provides convenience for our subsequent image encryption. Next, we will introduce the theory of equal modulus decomposition (EMD) [21] in detail. Generally speaking, a complex number can be expressed as a position vector in the two-dimensional Cartesian coordinate system space. It consists of a horizontal real part (Re) component and a vertical imaginary part (Im) component, as shown in Figure 3. According to this, the EMD is produced through an asymmetric mathematical transformation. Here, we use the simplest one-dimensional symbol to describe the process. Assume that the intensity of the original image is \(O(x)\) and the phase added to the image is \(P\), the new function is constructed as follows  
\[I(x)=\sqrt{O(x)}\cdot P \tag{9}\]  
In addition, \(I(x)\) can be divided into two complex numbers \(M_{1}(x)\) and \(M_{2}(x)\) with equal modulus, as shown in \[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]0 The expression of \(\theta(x)\) is  
\[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]1  
\[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]2 (**a,b**) The beam splitting system diagram and beam combining system diagram of 2 \(\times\) 2 DG, respectively. DG, Dammann grating; L, Fourier lens; PP, phase plate; CDG, conjugate Dammann grating. \[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]3 (**a,b**) Simulation diagram of the intensity and phase of the rear focal plane of the lens L in beam splitting respectively; (**c,d**) simulation diagram of the intensity and phase of the front focal plane of the lens L in beam combining, respectively; (**e**) the beam intensity distribution map of the far field in beam combining. where \(rand(x)\) is a function that can generate random numbers in the range of [0, 1]. Moreover, the expressions of \(M_{1}(x)\) and \(M_{2}(x)\) are  
\[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]4  
\[E(x,y)=\exp\left(-\frac{x^{2}+y^{2}}{\omega_{0}^{2}}\right) \tag{2}\]5  
where \(A(x)=|I(x)|\) and \(\varphi(x)=\arg\{I(x)\}\). Finally, an image information \(I(x)\) is successfully hidden into two complex numbers \(M_{1}(x)\) and \(M_{2}(x)\) by using the EMD principle. In this process, \(I(x)\) can only be obtained from \(M_{1}(x)\) when \(M_{2}(x)\) is known. Therefore, \(M_{2}(x)\) can be regarded as the private key. \(M_{1}(x)\) is the ciphertext, and \(\theta(x)\) is the public key for image encryption.