esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The crystal structure of the HRas-CRaf-RBD_CRD complex presented here with our analysis of the dimer model based on MD simulations supported by a recently published structure of the KRas-CRaf-RBD_CRD in which the dimer is present provide a key missing link in our understanding of Ras-mediated Raf activation. They show a continuous surface for Ras-Raf interaction that links the Ras active site and dimerization interface through switch I, loop 3, and \(\alpha\)5. In the context of the dimer, the CRD also contacts loop 8 and residues in two highly conserved nucleotide-binding motifs, centrally positioned to access regulatory and functional regions of Ras. This is consistent with the observation that the Raf-CRD is required for Raf activation in vivo. Our analysis of these structures in the context of other recent structural breakthroughs in the literature provides an emerging model of Ras activation of Raf, where formation of the Ras-Raf complex is linked to both dimerization and release of Raf autoinhibition to form a platform for amplified synchronized signaling. This model builds upon our recent proposal of higher-order Ras-Raf-Galectin signaling assemblies, paving forward a new research direction paramount to our understanding of signaling through the Ras-Raf-MEK-ERK pathway [8]. Because the affinity between Ras and the Raf-CRD alone is relatively weak, the disruption of its interactions with Ras, either through the interswitch region involving loop 3 or through loop 8, holds strong potential as a novel approach for targeting Ras- and Raf-related cancers. The structural analysis presented here, with release of the requirement for insertion of the CRD in the membrane, provides an alternate framework, consistent with available data on Ras dimerization and nanoclustering, from which to explore the effects of oncogenic mutants on signaling through the MAPK pathway.
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 5 Experiments
### Gtzan
#### 5.3.1 Result Comparison  
In Table 4, we compare our work with other methods on the GTZAN dataset. We report both the single clip and the voted whole excerpt results. The LSR method reduces the nnet1 error rate by 1.15% and fail to improve nnet2. Tf-KD and our method LSR-OS achieve comparable improvement, while LSR-OS is easier to tune. LSR-OS with teacher correction improves both networks further.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Model** & **Size** & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**LSR [19]**} & \multicolumn{2}{c}{**Tf-KD [27]**} & \multicolumn{2}{c}{**LSR-OS**} & \multicolumn{2}{c}{**LSR-OS-TC**} \\  & & **Clip** & **Song** & **Clip** & **Song** & **Clip** & **Song** & **Clip** & **Song** & **Clip** & **Song** \\ \hline nnet1 [42] & 0.46M & 23.58 & 15.97 & 22.76 & 14.82 & 22.13 & 14.16 & 22.21 & 14.21 & **21.41 (2.17)** & **13.45 (2.52)** \\ nnet2 [42] & 1.25M & 21.53 & 13.42 & 21.59 & 13.56 & 21.14 & 13.16 & 21.25 & 13.34 & **21.09 (0.44)** & **12.93 (0.49)** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test set error rate comparison on the GTZAN dataset. The column head “Clip” indicates the 3-second fragment results and under “Song” is the averaged total excerpt results.  
Figure 7: The three-seconds music clips from the GTZAN dataset. Left: the raw waveform data. Right: the corresponding STFT spectrogram in the log scale y-axis. The music genre from top to bottom: blues, classical, country, and disco.
## 3 Results and Discussion
### Mechanical Impedance Analysis  
In Figure 9, the curves of the scaled mechanical impedance signals \((Z(\omega)/\gamma(\omega))\) obtained from the relation \((V_{in}(\omega)/\gamma_{out}(\omega))\) are consolidated, as detailed in Equations (14)-(18). These signals were also calculated for the same measured points P1 and P2, where Figure 9a,c belong to the window 10-100 Hz and Figure 9b,d belong to 100-1000 Hz. The signals between 10 and 100 Hz have a similar structure for all the tested fruits where two initial characteristic peaks are reflected between 24 and 40 Hz and two final peaks are presented between 55 and 1000 Hz with higher amplitude. We can observe that in the range of 24 to 40 Hz, only semi-ripe and ripe fruits present these resonances, which exhibit more resistance to motion during the excitation; in this range of frequency, the unripe fruits did not evidence resonance peaks.  
As modal analysis theory exposes, it is important to denote that mechanical impedance is an FRF that represents a degree of freedom of a structure. This means that the scaled mechanical impedances were taken only for two degrees of freedom. According to the theory, these functions can be joined spatially, and for multiple degrees of freedom, it is possible to reconstruct the modal shape.  
Moreover, between 100 and 1000 Hz (see Figure 9b,d), the scaled impedance signals showed higher amplitudes with a trend that does not evidence any relevant difference among the ripening stages, respectively. The most significant characteristic is an observed peak at 600 Hz.
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 3 Results and Discussion
### Characterization of ZnO/Ag Nanocomposites
#### 3.1.3 UV-Vis Spectroscopy Studies  
The absorption properties of ZnO/Ag nanocomposites were investigated with UV-Vis spectroscopy in the 200-650 nm range of wavelengths. Figure 4 shows the absorption spectra for Ag nanoparticles on ZnO with different atomic contents of Ag. All the samples show strong absorption in the UV range. Additionally, the broad exciton-related peak at 360-370 nm disappears, and this behavior is associated with UV absorption in zinc oxide [27]. The absorption spectrum of pure ZnO shows maximum absorption at 360 nm. Furthermore, with the Zn2 sample, the absorption edge shifted to a higher wavelength (365 nm) with respect to pure ZnO (Zn1). This shift is related to the incorporation of Ag NPs in the ZnO matrix [28].
## 3 Materials and Methods
### Logging Data Processing  
Geophysical logging responses for coal quality were collected from 1:20-scale coal seam logging curves at 0.2 m intervals. Digital logging data included apparent resistivity (\(\rho_{\text{s}}\)), lateral resistivity (LLD), bulk density (DEN), natural gamma (GR), short-spaced gamma-gamma (CGS), and acoustic transit time (AC). Data processing involved three stages: depth correction, logging data extraction, and standardization. Depth correction was applied using logging curve responses from thick sandstone, limestone, and coal seams to align depth references. Logging data extraction calculated weighted averages for \(\rho_{\text{s}}\), DEN, AC, GR, LLD, and CGS curves.  
Standardization normalized \(\rho_{\text{s}}\), DEN, AC, and GR values using Formula (1) [14], and CGS and LLD values via Formula (2) [25].  
\[N_{N}=\frac{N\ -\ N_{min}}{N_{max}\ -\ N_{min}} \tag{1}\]  
\[M_{N}=\frac{\lg M\ -\ \lg M_{min}}{\lg M_{max}\ -\lg M_{min}} \tag{2}\]  
In these formulas, \(N\) and \(M\) denote raw logging data; \(N_{n}\) and \(M_{n}\) represent normalized data; \(N_{max}/M_{max}\) and \(N_{min}/M_{min}\) correspond to the maximum and minimum values of each parameter, respectively.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Parameter Study  
In this subsection, we present a detailed parameter study using the publicly available KADID-10k [44] database to find the optimal design choices of our proposed method. Specifically, we compared the performance of three traditional metrics (PSNR, SSIM [12], and HaarPSI [20]). Furthermore, we compared the performance of three different regression algorithms, such as linear SVR, Gaussian SVR, and GPR with rational quadratic kernel function. As already mentioned, the evaluation is based on 100 random train-test splits. Moreover, mean PLCC, SROCC, and KROCC values are reported. The results of the parameter study are summarized in Figure 5. From these results, it can be seen that HaarPSI metric with Gaussian SVR provides the best results. This architecture is called _ActMapFeat_ in the further sections.  
Figure 5: Parameter study with respect to the applied regression techniques and image similarity metrics.  
**(a)** Linear SVR. **(b)** Gaussian SVR. **(c)** GPR with rational quadratic kernel function.
###### Abstract  
There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient.  
vessel trajectory prediction; AIS sensor data; support vector regression (SVR); adaptive chaos differential evolution algorithm (ACDE)
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
## 4 Experiments
### Performance Analysis against Data-Loss
Figure 10: Extracted and recovered images of our proposed scheme with the different BER: (**a**) BER = 0, PSNR = 31.28 dB; (**b**) BER = 6 \(\times\) 10\({}^{-2}\), PSNR = 12.26 dB; (**c**) BER = 6 \(\times\) 10\({}^{-3}\), PSNR=22.41 dB; (**d**) BER = 2 \(\times\) 10\({}^{-4}\), PSNR = 31.11 dB. Figure 9: Graph of the bit-error rate (BER) versus the signal-to-noise ratio (SNR) in a selected communication system. Obviously, our proposed scheme is more effective in anti-cropping characteristics than in anti-error characteristics. This is because the bit error of the most significant bit (MSB) in the quantization process can lead to large distortion of the measurement value, which seriously affects the reconstruction of the image. Absolutely, the larger the compression ratio, the better the quality of image, and the larger the size of the stego-image.
# A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm
## 3 Complex-Valued Encoding Grey Wolf Optimization Algorithm (CGWO)
### The Complex-Valued Encoding Method
#### 3.1.1 Initializing the Complex-Valued Encoding Population  
The variable interval of the function is set to \([A_{L},\,B_{L}]\), \(L=1,\,2,\,...\,,\,\,M\). Generate \(M\) modulus and \(M\) phase angle randomly, and they form a complex-valued \(M\). The relationship of the modulus and the phase is provided as follows:  
\[\rho_{L}=\left[0,\,\frac{B_{L}-A_{L}}{2}\right],\,\,\,\theta_{L}=\left[-2\pi, \,2\pi\right],\,\,L=1,\,2,\,....\,,\,M \tag{7}\]  
\[R_{L}+il_{L}=\rho_{L}(\text{cos}\theta_{L}+i\text{sin}\theta_{L}),\,\,\,\,L=1, \,2,\,...\,,\,M \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Gene_{1}\) & \(Gene_{2}\) & \(Gene_{1}\) & \(Gene_{M}\) \\ \hline (\(R_{p1}\), \(I_{p1}\)) & (\(R_{p2}\), \(I_{p2}\)) & \(\ldots\) & (\(R_{pM}\), \(I_{pM}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grey wolf chromosome model.  
So, we obtain \(M\) real and imaginary parts. The real and imaginary parts are updated according to Section 3.1.2.
# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 4 Data Pre-Processing
### Background Subtraction  
The background subtraction function removes the unwanted background or regions of the input data by using an imaging library in Python called DIPY [38]. The DIPY library function uses the median filter smoothening of the volume data and then an automatic histogram Otsu thresholding to generate the binary mask of the tool volume from the CT volume. It takes the following parameters:  
* **input_volume**: 3D array of the volume data;
* **median_radius**: int value representing the radius (in voxels) of the median filter. The value was taken as zero here;  
Figure 9: The general workflow of the pre-processing module.  
* **numpass**: int value indicating the number of passes of the median filter. Here, the value was set to one;
* **Autocrop**: Boolean value indicating if the input volume should be cropped using a bounding box. \"False\" was set in this case;
* **Dilate**: indicates the number of iterations for binary dilation. The default value \"None\" was kept as a parameter.  
Then, the binary mask volume is used as a reference to extract only the tool data from the CT data. In this work, two different background subtractions were developed. The binary mask is overlayed over the input volume, and then the following are performed:  
* **Method 1** (outer-background subtraction): removes only the outer background to avoid removing critical ROIs from hollow object tools. The outer boundary pixels of the tool in the scanned volume are identified, and all pixels that fall outside the boundary pixels are removed. This methods is suitable for hollow tools;
* **Method 2** (full-background subtraction): removes noisy data from both the inner and outer parts of the tools. All the pixels that are not overlapping with the binary mask are removed from the input volume. This methods is suitable for dense tools with small empty spaces.  
Figure 10 shows an illustration of the two background subtraction methods. The input image has dark regions around and also inside the tool. First, the mask image is generated, then the background pixels are identified and removed. After removing those backgrounds, the top image is cleaned from both the inner and outer parts, and the lower image shows the output when only the outer noisy background is removed.
# Advances in Piezoelectric Jet and Atomization Devices
## 2 Advances in Piezoelectric Micro Jet Devices  
The working principles of the piezoelectric micro jet device can be divided into three stages: (i) the driving signal is employed as the piezoelectric vibrator; (ii) the inverse piezoelectric effect of piezoelectric ceramics is used to make the piezoelectric vibrator deform; and (iii) the pressure wave is subsequently generated and acted on the liquid to extrude the liquid from the micropore. The earliest application of piezoelectric micro jet technology is the piezoelectric inkjet printer, in which the conversion of electrical energy to mechanical energy is achieved under the action of the AC signal, leading to the piezoelectric device deformation, extruding the ink to spray it on the paper (or other media) and finally the printing of text and figure [19]. Figure 1 shows the structure diagram of the piezoelectric micro jet device [20]. According to the deformation mode of the piezoelectric vibrator, the piezoelectric micro jet devices can be roughly divided into four types, including bend mode [21], push mode [22], squeeze mode [23], and shear mode [20].  
Figure 1: Structure diagram of the piezoelectric micro jet device [24]: (**a**) Bend mode, (**b**) Push mode, (**c**) Squeeze mode, (**d1,d2,d3**) Shear mode. Reprinted with permission from Ref. [24]. Copyright 1991 IOP Publishing, Ltd.
# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
## 2 Materials and Methods  
### Samples  
The study was conducted according to the guidelines of the Declaration of Helsinki; each patient signed informed consent, and the clinical data were depersonalized. The Ethics Committee of the Novosibirsk Research Institute of Traumatology and Orthopedics n.a. Ya.L. Tsivyan approved the study protocol (Permission #004/22-1, 17 January 2022). Glioma patients (WHO Grade 2-4, \(n=14\)), patients with SCD (\(n=13\)), and healthy donors (\(n=6\)) were provided by the Novosibirsk Research Institute of Traumatology and Orthopedics n.a. Ya.L. Tsivyan. Peripheral blood samples were collected in vacutainers with EDTA (Sarstedt AG & Co. KG, Numbrecht, Germany). The plasma was separated by centrifugation at 2800\(\times\)\(g\) for 15 min at +4 \({}^{\circ}\)C. Blood plasma samples were frozen and stored at \(-\)80 \({}^{\circ}\)C.
## 1 Introduction
The main contributions are as follows:  
First, it presents a Wide-Domain Vision Convolution Network (a Wide Field View Convolutional Network), which employs larger convolution kernels to enhance the network's perception and generalization capabilities, making it better suited for the complexities of transmission line scenarios. Additionally, a large kernel CNN can achieve a larger effective receptive field with fewer layers while also providing higher shape variance, enabling better capture of shape information within images. Secondly, a multi-dimensional dynamic convolution feature fusion network is proposed. Compared with traditional static convolutions, this method significantly improves CNN accuracy by learning a linear combination of multiple convolution kernels and weighted attention related to their input, demonstrating higher parameter efficiency and feature extraction capability. Finally, the Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is adopted to focus on and utilize important information within the input data, helping the model better allocate attention to essential targets or scene segments, enabling the learning of more generalized feature representations. This contributes to enhancing the model's generalization ability and robustness, allowing for more accurate localization of target positions.
## 5 Experiments
### Results and Analysis  
As with most machine translation studies, we use BLEU [34] scores as the indicator of translation results. The translation results of different NMT models initialized by the pretrained models are shown in Table 3. According to the first three lines in Table 3, We can see that after being initialized by the same pretrained model, our CPTT performs better than XLM baseline on both zh-en and en-de translation tasks, and the LWCT with 6 layers obtains the performance close to transformer with half of the parameters. The LWCT with 12 layers even outperforms the XLM baseline by 1.46 BLEU points on en-de translation task. However, the LWCT with 12 layers requires a bigger pretrained model. We can also see clearly that our models has less parameters than transformer. Compared with the 12 layers of LWCT models, all encoder-decoder NMT models have more parameters in total, because we do not share token embedding between encoder and decoder. To compare each NMT model in detail, Figure 5 shows the results of the first 10,000 steps of the validation set on the zh-en translation task for all NMT models.  
An easily overlooked problem is that the improvements may come from changes to the better model, rather than relying on keeping models consistent between pretraining and translation. So, we set up a comparison experiment in which all the NMT models were not initialized by the pretrained models. The results are shown in Table 4.  
We can learn that transformer performs better than all other NMT models according to Table 4. Therefore, combined with the previous experimental results, we conclude that it is necessary to keep the model consistent between the pretraining and translation stages.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Pretraining Model** & **NMT Model** & **Encoder Param** & **Decoder Param** & **zh-en** & **en-de** \\ \hline \multirow{3}{*}{Transformer encoder(6-layer)} & Transformer(XLM) & 58.9M & 66.8M & 13.68 & 21.59 \\  & LWCT(6-layer) & 58.9M & - & 13.57 & 21.26 \\  & CPTT & 58.9M & 58.9M & 14.15 & 22.21 \\ \hline Transformer encoder(12-layer) & LWCT(12-layer) & 77.8M & - & 13.65 & 23.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BLEU scores of different NMT models initialized by the pretrained model in the test set.
## 1 Introduction
### Formulation of the Problem of Interest for this Investigation  
For simplicity, we consider the identification problem of multiple-input single-output output-error (MISO-OE) systems with unknown input time-delays. The main body of the MISO-OE model is the impulse transfer functions between the inputs and output. The identification challenge lies in that the system output is nonlinear in the polynomial parameters.  
For the identification of the multivariable systems with time-delays, it is needed to form an appropriate identification model first. Taking into account the unknown time-delays, a high dimensional identification model is formed with a sparse parameter vector, which contains many zeros [9; 10]. For the identification of high dimensional models, traditional identification methods, such as the least squares method and the stochastic gradient method, require many observations as well as heavy computational burden. However, there are many situations in reality where only limited observations are available such as setpoint-operated processes, online estimation and linear time-variant system identification [11]. It has proven that traditional identification is unreliable in these cases and the estimates often tend to be biased or have large variances [12]. Therefore, it is necessary to develop new identification methods and reduce the identification cost.  
The compressive sensing theory originally arose in the signal processing community, which enables the reconstruction of sparse or compressible signals under the undersampling condition [13]. In view of the sparse characteristic of the identification model, the estimation of the sparse parameter vector can be addressed as a reconstruction problem of the sparse signal.