esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep 3D Convolutional Neural Network for Facial Micro-Expression Analysis from Video Images
## 4 Pre-Processing
### Selection of Apex Frame Sequence  
In pre-processing, the last task was to choose the best sequence of frames with high emotion or where the facial muscle movement was at its peak. The sequence with high emotion needs to be considered for micro-expression recognition. Details about the frames with high emotional content are obtained from the data annotations by analyzing their AU levels. The CASME II and SMIC datasets also provide information about the on-set frame (where micro-expression starts), the Apex frame (where micro-expression peaks), and the off-set frame (where micro-expression ends). By taking advantage of these details, the selection of frame sequence is performed. The Apex frame sequence consists only of frames surrounding the Apex event with the highest micro-expression intensity. For instance, a fixed frame length of 36 frames is chosen for a video sample from CASME II. Then, 18 frames before and 18 frames after the Apex frame (including it) are considered in the Apex frame sequence. For SMIC, the sequence length is 26 frames.
## 4 Experimental Results and Discussion
### Model Parameter Selection  
In order to improve the ability of the model to solve problems, the link of model reference fitting is very important. The parameter settings directly affect the performance of the model. For this reason, this paper used randomized multi-parameter combinations to select the best parameter combinations after many experiments; the specific parameter settings are shown in Table 1.  
In Table 1, given the need for the same number of dilated causal convolutional layers, each LR-TCN contains two L-TCN, so the LR-TCN model is set to two layers. Usually, the dilated rate increases exponentially with the number of dilated convolutional layers, and model computation becomes larger. To achieve the effect of reducing model computation, the dilated rate of the four L-TCN is [1,2,2,2] in turn. In addition, according to the characteristics of the dataset, 24 time steps were chosen to estimate one time step.  
All algorithms were implemented in Python V3.8 using the integrated development environment PyCharm 2021.2.3 (Community Edition). The programs were executed on a Windows 11 (x64) operating system with a 12 th Gen Intel(R) Core (TM) i7-12700H CPU (Intel, Santa Clara, CA, USA), NVIDIA GTX 3060 GPU (NVIDIA, Santa Clara, CA, USA), and 16 GB of RAM, utilizing the CUDA-enabled version of PyTorch (V1.13.1) as the primary computational framework.
## 2 Modelling Approach and Governing Equation  
The establishment of the driveshaft-stator contact model in a viscous medium is presented in this section. The fundamental model of rotor lateral and torsional vibration is used to study the mechanisms of impact between a rotating driveshaft and casing (stator) which includes two unbalanced shafts connected by a Hooke's joint. The twin-rotor system consists of two identical shafts, a driveshaft which is referred to as a motor-shaft and the driven shaft connected to the first shaft by a flexible Hooke's joint as shown in Figure 1. A front view of the model for the response analysis in the coordinate systems (\(O_{1}\)\(X_{1}\), \(Y_{1}\), \(Z_{1}\)) and (\(O_{2}\)\(X_{2}\), \(Y_{2}\), \(Z_{2}\)) is indicated in Figure 2.  
In the coupled twin-rotor modelling process, the following assumptions are made: (1) The connected twin-rotor system via the Hooke's joint can be modelled as a Jeffcott rotor system in which the two rotors are simplified into an identical shaft disc with an eccentric mass attached to each disc. (2) The driveshaft system spins around the longitudinal axis \(Z\) at a constant angular velocity \(\Omega\) with an angular misalignment to the driven shaft defined by \(\beta\). (3) The diameter of a shaft is significantly small relative to its length, the inertias of the discs are not consistent and with the lower rotational motor speed, the gyroscopic effects are therefore neglected. (4) The self-aligned bearings are rigidly supported, viscous damping acts to oppose the movement of the driveshaft-disc system. (5) When rub-impact happens, conditions of viscous fluid deformations are assumed to be elastic. It is also considered that the two eccentric masses \(m_{u1}\) and \(m_{u2}\) are attached at a distance \(e_{1}\) and \(e_{2}\) of the centre of rotation of each shaft disc's masses \(M_{1}\) and \(M_{2}\), respectively. The coordinate frames used to develop the model are shown in Figure 2 where the centres of the discs are at the points, \(x_{1},y_{1}\) and \(x_{2},y_{2}\), which are the rotating coordinates attached to the respective centres of the discs.
# Motion Parallax Holograms Generated from an Existing Hologram
## 1 Introduction  
Recently, holographic displays have been attracting considerable attention because they can realize special projectors and three-dimensional (3D) displays satisfying well human depth cues [1, 2, 3] compared with stereoscopic displays. There are mainly two categories of holographic 3D displays, naked-eye displays [4, 5, 6] and head-mounted displays (HMDs) [7]. The former does not need special glasses to observe 3D scenes with wide viewing areas; however, spatial light modulators (SLMs) should have ultra-high-definition and large display area. Thus, very large spatial bandwidth products are required. Meanwhile, the latter does not require SLMs with large spatial bandwidth products because the position of the human eyes are fixed and placed near SLMs.  
The fixed eye position helps to reduce the spatial bandwidth products. However, if we observe 3D scenes from different view positions, we need to re-render the 3D scenes from new positions and then re-calculate the holograms from the re-rendered 3D scenes. The calculations of the 3D rendering and holograms are time-consuming. Therefore, it is ideal to generate holograms at different view positions directly from an existing hologram without the re-rendering 3D scenes and re-calculation of holograms.  
Hologram generation from light field data [8] requires a number of motion parallax images. It first divides a whole hologram plane into sub-holograms, and the multiple 3D scenes are rendered viewing from the center of each sub-hologram; subsequently, the sub-holograms are calculated. The light field-based hologram generation benefits from the following--complex processing [9] for hidden surface removal, lighting and shading do not need, unlike conventional hologram calculations [9, 10, 11, 12] abovementioned time-consuming problem.  
To circumvent the time-consuming process, we propose a generation strategy of motion parallax holograms from an existing hologram without re-rendering and recalculation. The proposed method is based on method demonstrated in [13] which uses trigonometric addition formulas to generate two-dimensional (2D) motion parallax images. Further, the proposed method employs a simple Fourier band-pass filtering and expands it to motion parallax hologram generation. A relighting to 3D scenes based on the wavefront recording plane method [14] without the re-rendering and recalculation of holograms has been proposed [15]. However, to our best knowledge, this is the first approach to generating motion parallax holograms from an existing hologram. The main purpose of this study is to confirm the principle of the proposed method for generating motion parallax holograms. Applications of the proposed method to holographic HMDs and light field-based hologram generation will be discussed in the future. The rest of this paper is organized as follows: Section 2 describes the proposed method, Section 3 shows the results of the proposed method, and Section 4 concludes this study.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 2 OCTA Fundamentals  
A unifying feature of all OCTA algorithms is that they visualize objects that are both moving and backscattering. Hence, we begin our review with a discussion of hemodynamics and light scattering properties of blood. Importantly, we distinguish between capillaries (<10 \(\upmu\)m in diameter), where RBCs flow in a line and hematocrits are low, and macrovascular, where RBCs flow side-by-side and hematocrits approach systemic levels, with the understanding that non-capillary microvessels (10-100 \(\upmu\)m in diameter) represent an intermediate case between the two extremes discussed here [2; 3].
## 5 Simulation and Performance Analysis
### Simulation Settings
* LR, the logistic regression method [44], involves using linear or non-linear models to associate inputs and outputs. The LR model is a classic statistical method that has advantages in practicality. In this paper, a penalty term of two norm was set for the method in simulation. * CNN, the method proposed in [24], followed the process of this method (i.e., Figure 2 and Algorithm 1 in [24]) to process the CSI data and to authenticate the terminal. * LSTM, the long short-term memory method [45], is an improved method of recurrent neural networks that utilizes a three-gate structure for the proper handling of historical information through reasonable planning. In the simulation, three hidden layers were set, including 15, 25, and 20 blocks, respectively.
## 5 Conclusions  
Although many high-order WENO schemes have been developed in the field of computational fluid dynamics, most of them are challenging to apply in engineering. Either the calculation process is too complex for practical applications, or the calculations are highly unstable. However, WENO-ZQ overcomes the drawbacks of many WENO schemes by eliminating the need for computing linear weights, thereby enhancing computational stability. It not only reduces computational costs in aerodynamic applications by using fewer grid points, but also captures more fluid physics details.  
This paper applies the finite-volume adapted WENO-ZQ scheme to simulate internal flows in aircraft engines, incorporating characteristic lines of turbomachinery and specific flow field details using the RANS and RANS/LES coupling methods. And typical turbine aerodynamic phenomena, including turbine wakes and film cooling, are also simulated. The results show that the third-order format of this scheme reduces the average error of the efficiency characteristic line calculation of the compressor from 0.76% to 0.05% compared to WENO-JS3, and the calculated turbine guide vane pressure distribution has an error of no more than 1% compared to the experimental value. In the high-precision numerical calculations of film cooling, the error of the centerline distribution of film cooling efficiency with the experimental value does not exceed 3%. Therefore, WENO-ZQ is a rare high-precision numerical calculation method with low computational cost, good robustness, and easy implementation in engineering software applications.  
Figure 14: Centerline film cooling effectiveness of the suction surface in C3X cascade.  
Figure 15: Contour of average vorticity \(\Omega\) and average strain rate S. (**a**) Mean vorticity \(\Omega\); (**b**) mean strain rate S.  
**Author Contributions:** Conceptualization, H.W. and S.Z.; methodology, H.W. and S.Z.; software, D.Z.; validation, H.W. and D.Z.; formal analysis, H.W.; investigation, H.W.; resources, N.G.; data curation, X.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W.; visualization, X.W.; supervision, N.G.; project administration, N.G.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science and Technology, grant number Y2019-I-0018-0017.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 5 Problem Solution
### Optimization Based on Lyapunov  
An offloading decision algorithm based on Lyapunov optimization theory is proposed in the paper to jointly optimize energy consumption and queue waiting time on the premise of ensuring the stability of the task computation queue, and finally minimizes the task average total energy consumption.  
The dynamic queue length at time \(t+1\) is expressed as  
\[Q_{k}(t+1)=\max[Q_{k}(t)-f_{k},0]+b_{k}(t). \tag{13}\]  
Before further discussion on the offloading strategy, Lemma 1 related to the derivation of the decision function is provided.  
Lemma 1: _Assuming that \(X\), \(Y\), \(Z\), and \(W\) are non-negative positive real number and \(X=max[Y-Z,0]+W\), \(X^{2}\leq Y^{2}+W^{2}+Z^{2}-2Y(Z-W)\) can be obtained._  
Based on Lyapunov optimization theory, the Lyapunov function is expressed as  
\[L(t)=\frac{1}{2}\sum_{k=0}^{M}Q_{k}^{2}(t) \tag{14}\]According to Lemma 1, the following equation  
\[L(t+1)-L(t) =\frac{1}{2}\sum_{k=0}^{M}[Q_{k}^{2}(t+1)-Q_{k}^{2}(t)]\] \[=\frac{1}{2}\sum_{k=0}^{M}\left[\max[Q_{k}(t)-f_{k},0]+b_{k}(t)]^{2 }-Q_{k}^{2}(t)\right. \tag{15}\] \[\leq\frac{1}{2}\sum_{k=0}^{M}(f_{k}^{2}+b_{k}^{2}(t))+\sum_{k=0}^ {M}Q(t)\cdot[b_{k}(t)-f_{k}(t)]\]  
can be obtained from Equation (14).  
Then, the Lyapunov transfer function is expressed as  
\[\Delta(t)=E\{L(t+1)-L(t)|Q(t)\}. \tag{16}\]  
Inequality (15) is substituted for Equation (16) to obtain  
\[\Delta(t)\leq\frac{1}{2}\sum_{k=0}^{M}E\{(f_{k}^{2}+b_{k}^{2}(t)|Q(t)\}-\sum_{ k=0}^{M}E\{Q_{k}(t)f_{k}(t)|Q(t)\}+\sum_{k=0}^{M}E\{Q_{k}(t)b_{k}(t)|Q(t)\}. \tag{17}\]  
According to the above Lyapunov optimization method, it is necessary to ensure the stability of the queue while minimizing the penalty term. The Lyapunov penalty term in this paper refers to the energy consumption of the task performed, namely \(\mathcal{U}\{E(a(t))|Q(t)\}\). The Lyapunov transfer penalty term is  
\[\Delta(t)+V\mathcal{U}\{E(a(t))|Q(t)\}, \tag{18}\]  
where \(V\) is a weight control parameter, which indicates the weight of minimizing energy consumption. In other words, \(V\) can be considered as a threshold of the system state. According to Equations (17) and (18), we can deduce  
\[\Delta(t)+V\mathcal{U}\{E(a(t))|Q(t)\} \leq\frac{1}{2}\sum_{k=0}^{M}\mathcal{U}\{(f_{k}^{2}+b_{k}^{2}(t)| Q(t)\}\] \[+V\mathcal{U}\{E(a(t))|Q(t)\} -\sum_{k=0}^{M}\mathcal{U}\{Q_{k}(t)b_{k}(t)|Q(t)\}\] \[+\sum_{k=0}^{M}\mathcal{U}\{Q_{k}(t)b_{k}(t)|Q(t)\} =\frac{1}{2}\sum_{k=0}^{M}f_{k}^{2}-\sum_{k=0}^{M}Q_{k}(t)f_{k} \tag{19}\] \[+\mathcal{U}\{\frac{1}{2}\sum_{k=0}^{M}(\sum_{m=1}^{M}a_{m,k}(t) \cdot A_{m}(t))^{2}+VE(a(t))\] \[+\sum_{k=0}^{M}\sum_{m=1}^{M}Q_{m,k}(t)\cdot a_{m,k}(t)\cdot A_{m }(t)]|Q(t)\}.\]
## 1 Introduction
The rate of change in the amount of adsorbed substance in the solid phase is also described by the difference between the adsorption rate depending on the current concentration of the adsorbate in the liquid phase and the desorption rate depending on the adsorption amount [19]. There are also known models that result from approximate analytical solutions of adsorption dynamics [20]. The [PERSON] model was developed for plug flow and the Langmuir isotherm, assuming that external and internal diffusion can be neglected [20]. The second model cited in [20] is the [PERSON] model. It was developed for the Freundlich isotherm and contains parameters correcting the lack of symmetry of the bed breakthrough curve in relation to the point corresponding to half the adsorbate concentration at the entrance to the adsorption bed. Both models cannot be used in unsteady-state conditions. Similar semi-empirical models developed for the Henry, Langmuir and rectangular isotherms are cited in [21]. These are the Zhukhowicki, Zabiezinski and Tikhonov models. The model developed by [PERSON] and [PERSON] assumes that the rate of decline in the adsorption probability is proportional to the probability of adsorption of the adsorbate and breakthrough of the adsorbate on the adsorbent [20; 22]. A model of a similar class is the Klinkenberg model, enabling the determination of the bed breakthrough curve [23]. This is a model obtained from solving the advective mass transport equation and the Whitman model for the adsorption rate. The adsorption equilibrium is described by Henry's isotherm. Analytical models make it possible to determine the breakthrough curve for an adsorption column operating in steady states, i.e., at a constant flow and constant concentration of adsorbate at the entrance to the column. Universal models, enabling simulation of the operation of an adsorption column in unsteady-state conditions, require the solution of a system of differential equations describing the advective-diffusive transport of adsorbate in a liquid and the adsorption rate on the sorbent at the appropriate adsorption isotherm. This system of equations does not have an exact analytical solution and can only be integrated numerically. Such models enable the determination of adsorbate concentrations in the adsorption reactor [15; 16; 17], inside the capillaries of the sorbent [24; 25] or inside the adsorption layer [26; 27]. This article presents a model enabling calculations in transient states for a filter bed with advective-dispersive flow through two layers: anthracite with PAC and sand. This model takes into account the kinetics of adsorption onto PAC. A computer program was written to simulate the course of adsorption in the filter bed and determine the parameters of the adsorption model. The additionally proposed criterion equations enable the prediction of the dispersion coefficient and the product of the mass transfer rate constant and the specific outer surface of sorbent for various technological solutions of the PAC filter bed.
## 2 Methodology
### Objective Function
#### 2.1.3 Temporal Objective Function \(J_{to}\)  
For each spatial grid cell, we analyze the temporal coverage by using a grid in the time dimension. These temporal grid cells are characterized by their time size and corresponding spatial grid cell.  
Figure 7 shows an example of a representation of the temporal grid cells at each spatial cell for the period of one day. Here, a signal in black is shown where star signs  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weight** & **Value [\(-\)]** \\ \hline \(W_{ob}\) & 100 \\ \(W_{ro}\) & 1 \\ \(W_{ew}\) & 1 \\ \(W_{ns}\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial objective weights.  
represent the samples taken by the spacecraft. The signal runs between time \(t_{0}\) and \(t_{day}\) and is subdivided into 16 temporal cells. Analog to the spatial cells, as long as one sample is within the limits of a temporal cell, this cell is considered to be observed in its entirety. Sixteen temporal grid cells for each day of observation are chosen to balance having a sufficiently fine temporal grid cell size that meets the sampling criteria of one temporal cell per orbital revolution with the available computational limitations. The signals of interest chosen for the verification of this methodology, described in Section 4, have a 3-hour temporal resolution. This requires us to seek a temporal resolution of at least 1.5 h, leading to 16 temporal grid cells per day.  
Each temporal cell TC in Equation (8) represents a binary representation if a specific cell has been observed, where \(T\) stands for the number of days analyzed. Through a reformulation in vector form, we can apply the Gini coefficient in Equation (9), which represents the uniformity of each spatial cell in time (\(\mathbf{G}_{TV}\)). Equation (10) uses the previously computed Gini \(\mathbf{G}_{TV}\) to optimize the average as well as the uniformity of all spatial grid cells to each other.  
\[\begin{bmatrix}TC_{1,1}&\dots&TC_{1,16\cdot T}\\ \vdots&\ddots&\vdots\\ TC_{m,1}&\dots&TC_{m,16\cdot T}\end{bmatrix}=\begin{bmatrix}\mathbf{TV}_{1} \\ \vdots\\ \mathbf{TV}_{m}\end{bmatrix} \tag{8}\]  
\[\mathbf{G}_{TV}=\frac{\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}|\mathbf{TV}_ {i}-\mathbf{TV}_{j}|}{2m\sum\limits_{i=1}^{m}\mathbf{TV}_{i}} \tag{9}\]  
\[I_{to}=\frac{\overline{\mathbf{G}_{TV}}}{2}+\frac{1}{2}\left(\frac{\sum \limits_{i=1}^{m}\sum\limits_{j=1}^{m}|\mathbf{G}_{TV_{i}}-\mathbf{G}_{TV_{j} }|}{2m\sum\limits_{i=1}^{m}\mathbf{G}_{TV_{i}}}\right) \tag{10}\]  
This defines the temporal objective function \(J_{to}\) and will allow for a uniform solution both for the whole time of analysis (29 days) as well as over shorter periods. Due to the nondominated sorting approach used in NSGA-II, there is no need to apply weights to the spatial and temporal objective functions \(J_{s}o\) and \(J_{t}o\). This concludes the description of the objective functions used in the genetic algorithm.
## 3 Feature Extraction
### Onset Strength and Logarithmic Transformations  
Several of the described features are calculated per onset, and several of the stimuli contain multiple onsets. It is possible that the relative importance of each onset to the perceived hardness of the full stimulus is proportional to the perceptual prominence of that onset. The _onset strength_ is a measure describing the prominence of an onset. This was used as the weighting factor in the calculation of a weighted-average for each per-onset feature, across all onsets within a stimulus.  
Additionally, logarithmic transformation might improve the correlation of some of the suggested features, since human perception of frequency and amplitude are roughly logarithmic. To account for this, the base-10 logarithm of each feature was calculated.
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 3 Methodology
### Augmentation Methods in Lung Nodule Detection  
Insufficient training samples can lead to premature overfitting in models, where they merely memorize labels rather than achieving true generalization. To enhance generalization capabilities, it is crucial to challenge the models to learn general features rather than allowing easy memorization. Increasing the number of training samples for medical image classification, however, is often prohibitively expensive and time-consuming. As a result, augmentation techniques have become a vital strategy to expand training datasets by applying synthetic modifications to individual samples. When implemented effectively, augmentation can significantly enlarge the dataset size beyond the model's capacity to memorize, compelling it to rely more on generalization. Nevertheless, not all augmentation techniques are equally effective, making their research a focal point in medical image classification, especially with the rise of deep neural networks. Two primary categories of augmentation techniques are geometric and photometric. Geometric augmentation manipulates the spatial attributes of images, such as rotating, flipping, or scaling, to mimic the variability in patient positioning and organ orientation that occurs naturally. On the other hand, photometric augmentation alters image appearance attributes, like brightness, contrast, and noise levels, addressing variations due to different imaging equipment or settings. The efficacy of these techniques in preventing overfitting and promoting robustness makes them indispensable in training deep learning models for medical imaging tasks. Their ongoing evaluation and optimization continue to be critical for advancing the field and improving diagnostic accuracy in clinical settings