esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 4 Results
### Reflectance Results  
We report on the terahertz reflectance spectra of six materials measured in the final reflection configuration. The improved setup with optimized, remeasured components, and added reflection-eliminating features provided us with a better signal-to-noise ratio and provided an opportunity to express the measured reflection as a percentage, rather than in arbitrary units as in previous preliminary research [44]. The improved setup enabled the possibility to measure samples with a low reflection coefficient and in a wider frequency range, providing at the same time experimental results with better signal quality. In Figures 7-12, we present the original results of measurements conducted at the distance of 1m of lactose, PABA, RDX, PETN, salicylic acid and sugar. Measurements were conducted in an atmosphere with about 40% RH at room temperature. This fact implied the presence of water vapor absorption features in the measured characteristics. The reflectance measurements were compared with the reference data, and the water vapor transmission spectra were added to the figures.  
Figure 5: The transmission spectra of tartaric acid measured in the 0.7–1.6 THz range in an atmosphere with 42% RH.  
Figure 6: The transmission spectra of PABA measured in the 0.7–1.5 THz and 1.4–2.2 THz range in an atmosphere with 38% RH.  
Figure 8: The reflectance spectra of PABA measured in the 0.67–1.8 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Figure 7: The reflectance spectra of lactose measured in the 1–1.8 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Figure 9: The reflectance spectra of RDX measured in the 0.7–1.7 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Figure 11: The reflectance spectra of salicylic acid measured in the 0.9–1.6 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Figure 12: The reflectance spectra of sugar measured in the 1.65–2.1 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Figure 10: The reflectance spectra of PETN measured in the 1.8–2.2 THz range in an atmosphere with 40% RH. The presented water vapor absorption spectra were measured in the transmission configuration.  
Two of the selected materials, RDX and PABA, were measured at longer distances up to 5 m. In Figures 13 and 14, results of the measurements are presented for RDX and PABA, respectively. A wider selection of transmission results can be found in [44].
## 4 Experimental Validation of STST Based on TVCM-VPF
### Method and System of STST Based on TVC-VPF
#### 4.2.3 ES and Shear Force Measurement  
The geometry of the single water tank used in STST is the same as that in the prototype structure. However, owing to the limitation of the size and bearing capacity of the shear force measurement platform used in the substructure test, four single water tanks, as shown in Figure 6, were employed in the substructure tests in this study. To recover the reaction force of the prototype TLD, the measured reaction force of the TLD specimen was multiplied by 6 and then fed back to the evaluation of the NS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(K_{df}\) & \(K_{df}\) & \(K_{df}\) & \(K_{dr}\) & \(K_{vr}\) & \(K_{ar}\) \\ \hline
0.2 & \(-0.02\) & 0.00006 & 0.2 & 0.0079 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of three-variable control method with velocity positive feedback (TVCM-VPF) in shaking table substructure testing (STST).  
Figure 6: Specimen and experimental setup.  
When conducting STST for the TLD control structure, it is necessary to measure the shear force at the bottom of the specimen to calculate the loading targets at the next step. In this study, the shear force of the TLD was obtained directly by a shear force measurement table. A schematic diagram of this measurement table is depicted in Figure 6. This table consists of four shear force sensors, upper and lower high-strength steel plates, accelerometers, and a charge amplifier. Four piezoelectric shear force sensors, model 9146 BA of Kistler Company, Winterthur, Switzerland, were arranged between these two plates at the four corners of the table. A capacitive acceleration sensor, model 8310B of Kistler Company, Switzerland, was installed on the upper plate. The shear force \(F_{\text{E}}\) generated by the TLD specimen was determined by subtracting the inertia force of the upper table from the total shear force:  
\[F_{\text{E}}=F_{\text{Total}}-m_{\text{Top}}\cdot a_{\text{Up}}, \tag{19}\]  
where \(F_{\text{Total}}\) is the total shear force obtained from the measurement table, \(m_{\text{Top}}\) is the mass of the upper steel plate, and \(a_{\text{Up}}\) is the acceleration measured by the accelerometers installed on the upper plate.
# High Spatial and Temporal Resolution Bistatic Wind Lidar
## 3 Validation and Characterisation
### PTB Bistatic Lidar/Sonic Anemometer (CSAT3B) in Turbulent Flow
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\overline{u}\) & \(\overline{w^{\prime}}\overline{w^{\prime}}^{1/2}\) & \(u_{*}\) \\ \hline Bias (m \(\cdot\) s\({}^{-1}\)) & \(0.003\) & \(-0.009\) & \(-0.009\) \\ Mean deviation, RMSE (m \(\cdot\) s\({}^{-1}\)) & \(0.082\) & \(0.017\) & \(0.042\) \\ Regression: intercept (m \(\cdot\) s\({}^{-1}\)) & \(0.044\) & \(-0.009\) & \(0.000\) \\ Regression: slope & \(0.989\) & \(0.989\) & \(0.973\) \\ Pearson correlation coefficient & \(0.998\) & \(0.998\) & \(0.980\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical parameters of the 2019 comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast. Figure 11: Results of the 2019 comparative measurement between the PTB bistatic lidar and a CSAT3B ultrasonic anemometer mounted on top of a 30 m mast in turbulent flow, showing orthogonal linear regressions of the average horizontal wind velocity component \(\overline{u}\), of the fluctuation of the vertical wind velocity component \(\overline{w^{\prime}}\overline{w^{\prime}}^{1/2}\) and of the shear stress velocity \(u_{*}\).
## 4 Experimental and Results
### Experimental Setup
#### 4.1.2 Training Setting  
All models were trained with a batch size of 32 for 100,000 iterations. An AdamW optimizer with \(\beta_{1}=0.8\), \(\beta_{2}=0.99\) and weight decay \(\lambda=0.01\) was adopted for training. The starting learning rate was \(2\times 10^{-4}\), and the period decay coefficient was set to 0.999\({}^{1/8}\). Following previous work, we used a window generation training method. This method generates only part of the speech waveform at a time during training, which shortens the training time and reduces memory usage. Specifically, frame-level feature fragments of length 120 were fed to the frame decoder to generate the corresponding speech. We found that setting larger segment lengths may contribute to more stable performance in prosody generation.
## 2 Endoscope Imaging and Correction Principle  
### Endoscope Imaging Principle  
The industrial endoscope generally adopts an imaging system consisting of a lens and multiple-point light sources. When the main optical axis of the lens completely coincides with the hole axis, the center of the endoscope lens is set as the origin point O (0,0,0) for the spatial coordinate. Multiple point light sources are symmetrically distributed at a distance \(\Delta\) from the center of the lens. Since \(\Delta\) is generally 1-2 mm less than both the depth of field  
Figure 1: Inner surface peep image of hole. (**a**) \(\varphi\)18 hole with grooves, (**b**) M20 thread hole, (**c**) \(\varphi\)20 hole with calibration plate.  
and aperture of the measured object, it can be approximately assumed that their spatial coordinates are also (0,0,0).  
A certain point \(Q\) on the inner surface of the hole receives light and reflects the received energy as a secondary light source. However, due to limitations in the endoscope structure, only the diffuse reflection part in the direction of the lens can enter and be imaged as point \(Q^{\prime}\) on the photosensitive plane. Eventually, this is converted into digital brightness information through photoelectric sensors [20]. The essence of defect measurement based on endoscopic images is to establish a mapping relationship between the \(Q\) in the spatial coordinate system X-Y-Z and the \(Q^{\prime}\) in the phase plane coordinate system U-V. The specific imaging principle is shown in Figure 2, where (a) represents the spatial coordinate system, and (b) represents the phase plane coordinate system.  
In Figure 2a, \(A\), \(B\) and \(Q\) are points on the inner surface of the hole with \(AB\) parallel to the axis of the hole, \(H\) represents the axial distance between two points of \(A\) and \(B\),\(f\) is the focal length of the endoscope, \(\alpha\) is its angle of view, and \(H_{0}\) is its minimum depth of field. In Figure 2b, \(A^{\prime}\), \(B^{\prime}\) and \(Q^{\prime}\) represent the image points corresponding to \(A\), \(B\) and \(Q\) points. \(C\) denotes the projection of the lens center in the phase plane and can also be understood as the image center. \(h\) refers to pixel ring width that corresponds to axial distance \(H\) on the inner surface in the phase plane. The blue arrows indicate circumferential direction and axial direction, respectively.
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Formations in Sarawak Region
#### 4.3.5 Hydrocarbon Generation Potential of Rock Formations in Sarawak  
For Sarawak, the Begrih, Liang, Balingian, and Bintulu formations had the highest chance of generating hydrocarbons, due to their high TOC, high hydrocarbon yield (S2), and high GP. These formations were immature, indicating that there was still a chance of generating hydrocarbons in huge quantities when subjected to high temperatures. The Belaga formation had the slightest chance of generating hydrocarbons in the Sarawak region due to the very mature nature of the hydrocarbons and poor GP. The significantly matured nature of the Belaga formation indicated that it had already generated to its total capacity; therefore, there was little chance of further generating hydrocarbons. The other formations had a moderate to low probability of generating hydrocarbons, due to their relatively satisfactory TOC and S2 and fair to poor GP. Most of the formations in the Sarawak region generate type II and type III kerogen.
## 2 Methods  
### BdiT  
The overall framework of the BDiT model is illustrated in Figure 1. Firstly, we trained the ResNet50 model on the dataset used in this study, demonstrating excellent performance in capturing local features and texture information. Subsequently, we utilized ResNet50 as the Teacher model to guide the training and testing of an improved Vision Transformer model on the same dataset. This approach allowed the Student model to inherit the Teacher model's ability to capture local features and texture information while also transferring inductive biases to enhance the feature extraction capabilities of the entire model.  
The framework of the improved Vision Transformer model is depicted in Figure 2. Initially, the input image was segmented into \(n\) image tokens (patch sequence) of size \(p\times p\). In our approach, this sequence was first fed into the spatial attention module (SAM) for processing, as explained in Section 2.2. Subsequently, each image token was linearly mapped to a serialized embedding vector through an Embedding Layer. Furthermore, a Classification Token (Cls Token, denoted as \"0\" in Figure 2) was added at the beginning of the patch sequence. This inclusion of the Cls Token introduced global information about  
Figure 1: The overall framework of the BDiT model.  
the image into the sequence. Finally, a positional embedding vector was assigned to each position to incorporate the positional information of the image tokens.  
Each embedding vector of the image tokens and its corresponding positional embedding vector was concatenated to form a sequence \(Z_{p}\in\mathbb{R}^{n\times d_{m}}\) representing the entire image. Here, \(p\) denotes the size of the image tokens, \(n\) represents the number of image tokens, and \(d_{m}\) represents the channel dimension. The sequence \(Z_{p}\) was input into a multi-layer improved Transformer encoder for feature extraction. Considering that the accuracy of the Transformer model no longer improved after reaching 12 layers and that it increased the model's parameter count and computational complexity [15], in this study, we adopted a six-layer enhanced Transformer encoder (Improved Transformer Encoder). This choice was motivated by the dataset size and computational efficiency, aiming to enhance the generalization ability of the BIiT model on small datasets and reduce the computational overhead.  
After passing through multiple layers of the improved Transformer encoder, the output of the last encoder was considered the feature representation of the input image. This feature representation was then fed into a classifier for classification prediction and loss computation, ultimately completing the model training.
## 1 Introduction
### Contribution of the Paper and Related Works  
The paper proposes a solution for digital conversion of the image of a slide stained with H&E (hematoxylin and eosin) [13] to a form resembling an image obtained with the use of fluorescence. The main contributions of the paper are:  
* Possibility to use a digital bright field microscope in place of a fluorescence microscope,
* Limiting the influence of photobleaching and photo damage in the slide microstructure,
* Analysis of the possibility of using deep learning convolutional neural networks to implement this type of conversion.  
As the research material, slides with sections of human livers were used. The confirmation of the possibility of performing this type of image conversion gives us a chance to use a similar method to convert images of other biological structures.  
The work is inspired by other research in the field of microscopic image conversion. One of the active areas of research is the digital staining of slide images [14; 15; 16; 17; 18]. As the staining process is a biochemical process, it requires proper preparation of a section, the use of chemical reagents (often expensive or hazardous to health), and time and human resources. The use of deep learning neural networks is an interesting alternative to classical biochemical staining.  
Another area of inspiration for research is improving image quality. The microscopic images often have poor contrast and sharpness. This is due to many factors such as the quality of biological material, the quality of the staining process, and the quality of the image acquisition system (cameras and lenses).  
A serious problem is the influence of thickness and thickness variation of biological material during the cutting. The use of computer methods allows one to improve the visual quality of the image. Additionally, it is possible to use super resolution methods to increase image resolution [19; 20; 21; 22].
# Physical-Layer Security Analysis over M-Distributed Fading Channels
## 4 Some Special Cases
### K Distribution Channel  
Letting \(\Omega_{n}=0\) and \(\rho_{n}=0\), the M distribution in (2) reduces to the K distribution as  
\[f_{H_{n}}(h)=\frac{2\alpha_{n}^{\frac{\alpha_{n}+1}{2}}h^{\frac{\alpha_{n}-1}{2}} }{\Gamma(\alpha_{n})(\xi_{g,n})^{\frac{\alpha_{n}+1}{2}}}K_{\alpha_{n}-1}\left( 2\sqrt{\frac{h\alpha_{n}}{\xi_{g,n}}}\right),\ h\geq 0 \tag{35}\]  
In this case, the lower bound of SOP in (16) becomes  
\[P_{\text{SOP}}^{\text{L}}=1-\frac{\left(1+\gamma_{\text{th}}\right)^{\frac{ \alpha_{n}+1}{4}}}{\Gamma(\alpha_{\text{E}})\Gamma(\alpha_{\text{B}})}\left( \frac{\sigma_{\text{B}}\alpha_{\text{B}}\xi_{g,\text{E}}}{\sigma_{\text{E}} \alpha_{\text{E}}\xi_{g,\text{B}}}\right)^{\frac{\alpha_{n}+1}{2}}G_{3,3}^{3,2}\left[\frac{\sigma_{\text{B}}\alpha_{\text{B}}\xi_{g,\text{E}}\sqrt{1+ \gamma_{\text{th}}}}{\sigma_{\text{E}}\alpha_{\text{E}}\xi_{g,\text{B}}} \right]\frac{\frac{1-2\alpha_{\text{E}}-\alpha_{\text{B}}}{2}-\frac{\alpha_{ \text{B}}+1}{2}}{\frac{\alpha_{\text{B}}-1}{2}},\frac{1-\alpha_{\text{B}}+1}{ 2}}{\frac{\alpha_{\text{B}}-1}{2}},\frac{1-\alpha_{\text{B}}+1}{2}\right] \tag{36}\]  
Moreover, the probability of SPSC in (20) reduces to  
\[P_{\text{SPSC}}=1-\frac{1}{\Gamma(\alpha_{\text{E}})\Gamma(\alpha_{\text{B}}) }\left(\frac{\sigma_{\text{B}}\alpha_{\text{B}}\xi_{g,\text{E}}}{\sigma_{\text {E}}\alpha_{\text{E}}\xi_{g,\text{B}}}\right)^{\frac{\alpha_{n}+1}{2}}G_{3,3}^ {3,2}\left[\frac{\sigma_{
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
## 3 Sonication  
### Ultrasound Treatment  
Ultrasound (US) is a form of energy generated by sound waves with a frequency higher than the hearing threshold of the human ear (18 kHz-100 MHz) [18; 27; 49; 50]. Sound waves are characterized by particular frequency, length and amplitude. They can be divided into two basic groups, based on their intensity and frequency: ultrasound of low power--waves of low intensity (<1 W/cm\({}^{2}\))--and high frequency (>1 MHz) and ultrasound of high power (or power ultrasound)--waves of high intensity (>1 W/cm\({}^{2}\)) and low frequency (18-100 kHz) [18; 27; 49; 51; 52; 53].  
The US waves of low intensity and high frequency are used for diagnostic purposes, mainly in medicine and industry, including food industry. These waves do not cause physical and chemical changes in the material they go through, which allows for their application in non-invasive analytical techniques [52]. High intensity US causes irreversible changes in the structure of the material and thus is used to accelerate the heat and mass transfer processes in the food industry as well as in cleaning machines [3; 27; 50; 52; 54; 55; 56]. High intensity US causes physical changes to the material, including its damage, as well as the initiation of various chemical reactions, including oxidation processes. It can also be successfully used to isolate intracellular proteins, for food preservation and to inactivate or activate enzymes [57; 58; 59; 50; 53]. Thus, this paper focuses only on the high intensity US.  
Generally, US can be applied as a pretreatment or treatment during process (US-assisted process) by the means of various devices and methods (immersive, contact or airborne US) [50; 60]. Immersive method is most commonly used to accelerate OD both before (US as a pretreatment) [11; 12; 13] and during OD, as well as for further drying [14; 15; 16; 17].
## 3 Basics for Implementation of the Machine-Learning Algorithm
### Reduction of Input-Space Dimension  
In a model-based scenario, one might want to reduce the number of simulations to a minimum, especially if multiple damage parameters are considered, as the computational burden might hamper the feasibility of the SHM system design. However, there would normally be issues of generalization if a training set that is too small is adopted. For example, the current wisdom in the ANN field demands that there usually be 10 training patterns per weight [15], although this requirement can be relaxed if some technique for ANN regularization is used, as in this study. In a vibration-based SHM, if the entire frequency pattern in the FFT is passed as input to the ANN, this will sensibly grow the input space dimension, thus the ANN parameters to be tuned and, as a consequence, the number of required examples for training. Thus, one might be interested in reducing the input-space dimension, which can be accomplished through principal component analysis (PCA), one of the most commonly used feature-extraction techniques. It was chosen because it is easy and fast to compute, and it retains maximal information among all linear projections [16]. Due to the widespread knowledge of the method, only its application will be described hereafter.  
The PCA allows for dimensionality reduction by using the covariance matrix of the dataset to rotate the reference system and to obtain a more efficient data description. An example is presented in Figure 14, where a rotation of the reference system from \((X,Y)\) to \((X_{1},Y_{1})\) allows describing the dataset by using the new variable \(X_{1}\) only, thus reducing the dataset dimensionality from 2 to 1.  
Figure 13: MLP structure.  
In general, after calculating the covariance matrix of the input dataset, its eigenvectors are the principal components (PC), and their associated eigenvalues \(\lambda_{i}\) are an index of the amount of variance retained by any \(i\)th component. It is usual to list the PCs in descending order of eigenvalues, so that the first PC corresponds to the largest variance of any linear combination of the original variables. A useful heuristic method to reduce input space dimension is to plot singular values to see if there is a point at which the values level off, or to select the first M components so that a fraction of the total variance will be retained [16], calculated as follows:  
\[\frac{\sum_{i=1}^{\text{M}}\lambda_{i}}{\sum_{i=1}^{d}\lambda_{i}} \tag{4}\]  
where d and M are the original and the reduced-input space dimensions, respectively. In particular, the new input matrix is obtained by multiplication of the original input matrix by the matrix containing the first M eigenvectors.