esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Findings from the Design Experiment  
The findings obtained from the design experiment are presented (Figure 5) while developing a mathematical model to determine the output of the PWC.  
**Finding 1: Flow**  
The flow graph shown in Figure 5 demonstrates that the flow increases linearly as the number of water columns increases. Mathematically,  
\[F=F_{i}\times n \tag{7}\]  
where \(F\) is the flow of water, \(F_{i}\) is the flow obtained from one water column, and \(n\) is the number of PWCs used.  
**Finding 2: Velocity**  
The velocity of water also increases linearly as the number of water columns increases, as shown in the velocity graph (Figure 5). The mathematical relationship between the velocity and PWCs is as follows:  
\[v=v_{i}\times n \tag{8}\]  
where \(v\) is the velocity at the outlet, \(v_{i}\) is the velocity obtained from one water column, and \(n\) is the number of PWCs used.  
**Finding 3: Pressure**  
The water pressure at the nozzle inlet increases with the square of the number of water columns, which increases the water flow at the nozzle outlet. The pressure graph (Figure 5) describes the mathematical relationship between the PWC and the generated pressure. Mathematically,  
\[P=P_{i}\times\ n^{2} \tag{9}\]  
where \(P\) is the total generated pressure, \(P_{i}\) is the pressure obtained from one water column, and \(n\) is the number of PWCs used in any setup.  
Figure 5: Variations in the pressure, flow, and velocity as the number of PWCs increases.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 2 Methods
### Bpnn  
Artificial neural networks are supervised machine learning methods, which have been applied in many fields [33; 34]. In the field of machine learning, back-propagation (BP) is a classical method used to train neural networks [35; 36], which can deal with complex nonlinear system problems and is widely used in the field of fault diagnosis and prediction. A three-layer BPNN is shown in Figure 1. The first layer is the input layer of the BP neural network; assuming there are n variables, the input vector \(x\in R^{n}\), where \(x=(x_{0},x_{1},\ldots,x_{n-1})^{T}\). The second layer is the hidden layer, with a total of \(l\) neurons, and its output is \(h\in R^{l}\), \(h=(h_{0},h_{1},\ldots,h_{l-1})^{T}\). The last layer is the output layer \(y\in R^{m}\), \(y=(y_{0},y_{1},\ldots,y_{m-1})^{T}\). \(w_{ij}\) is the weight of the \(i\)-th neuron in the input layer to the \(j\)-th neuron in the hidden layer, and the threshold of the \(j\)-th neuron in the hidden layer is \(\theta_{j}\). \(u^{jk}\) is the weight of the \(j\)-th neuron in the hidden layer to the \(k\)-th neuron in the output layer, and the threshold of the \(k\)-th neuron in the output layer is \(\eta_{j}\). The mapping from the input layer to the hidden layer to the output layer can be expressed as  
\[\left\{\begin{array}{l}h_{j}=f(\sum\limits_{i}^{n-1}w_{ij}x_{i}-\theta_{j}),\,j =0,1,\ldots,l-1\\ y_{k}=f(\sum\limits_{j}^{l-1}u_{jk}z_{j}-\eta_{k}),\,k=0,1,\ldots,m-1\\ \end{array}\right. \tag{7}\]  
The key to a BP neural network is to learn the weights and thresholds of the network through samples. The learning process is composed of the forward propagation of signals and the backward propagation of errors. Forward propagation means that after passing through the input layer and the hidden layer, the input signal is output to the output layer; if the desired output signal cannot be obtained, it is transferred to the reverse propagation of the error signal. In the back propagation of the error signal, the error signal is fed back layer by layer from the output layer, and each weight is adjusted by the error feedback, and through this continuous correction, the network output is closer to the expected output.
# Loss of Vitamin E While Baking and Heating French Fries
## 1 Introduction  
Tocochromanols (vitamin E, i.e., tocopherols and toctortienols, particularly \(\alpha\)-tocopherol) are important in vivo lipophilic antioxidants that protect unsaturated fatty acid bonds in tissue lipids from radical oxidation reactions. Their action is indispensable, especially to protect bio-membanes and low-density lipoproteins [1]. Sufficient vitamin E intake can be an important factor in preventing the onset and development of cardiovascular diseases [2].  
One of the most important and richest sources of vitamin E in the diet is vegetable oils [3, 4]. Large quantities of vegetable oils are consumed as part of culinary processed foods [5, 6]. Significant losses of tocochromanols can occur during this process, reaching up to 100% of the original content [1, 7, 8, 9]. In the case of frying, the rate of loss was rapid when oxygen was available during the early stages of heating and decreased with prolonged heating. Other factors influencing vitamin E loss included the oil-surface-to-air ratio and the physical mixing of oxygen into the oil during heating. Some volatilization of vitamin E can also occur during prolonged frying operations, further decreasing the antioxidant capacity of the oil. Within this context, the complete depletion of tocoperhools was describedin some vegetable oils markedly before reaching a content of polymerized triacylglycerglycerols of 12% (i.e., before the end of lifetime of these oils). This may be a reason of inadequate vitamin E intake, at least in part of the population, according to some studies. Inadequate intake is more prevalent in females than males [3; 5; 7; 10].  
The losses of vitamin E during frying have been studied extensively [9; 11; 12; 13]. However, the losses of toochromanols during other culinary food processing methods such as baking, cooking and microwave heating have not been studied to such a large extent and the results of some published studies are striking [14; 15]. Thus, the aim of this paper was to study the loss of tocopherols and tocortienols while preparing food in an oven and in a microwave oven. The model food was pre-fried frozen fries, which are usually prepared by baking rather than frying in households. The loss of toochromanols during the heating of fried fries in a microwave oven was also studied. This study is essential for public health, accurate dietary recommendations, food industry standards and consumer awareness.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 5 Task Offloading Algorithms in Our Model
### Quasi-Optimal Offloading Algorithm (Q-OPT)  
The last approach is a branch and bound based algorithm that processes the whole simulation data to produce a sub-optimal yet close to optimal objective value for benchmarking our heuristic algorithms. It analyzes the data from past simulations and functions as an oracle-type decision-maker. This processed data includes task details (e.g., arrival time, size, deadline), vehicle positions, vehicle associated AP and RSSI values, and task processor details.  
For each task, possible processor assignments are determined as lists. In order to narrow down the candidate solutions, arrival and departure times of vehicles and their connection status per step are considered to filter out the unfitting processors on each list. Two more decisions, skipping (meaning failing on purpose) and offloading to the cloud, are also added to these lists. Even the most underloaded setup with meaningful results generates a huge candidate pool from the Cartesian product of decision possibilities, which cannot be handled due to combinatorial explosion. Thus, we have developed a time-windowed quasi-optimal processing approach. The solver processes and makes decisions on the tasks in the given time interval. The algorithm starts with the time window to check if the candidate solution pool can be processed. As illustrated in Figure 3, it shrinks the window end time (window length) until the number of decision combinations is less than a predefined threshold, \(\Theta_{decision}\). The solver skips some of the combinations by bounding. After finding the optimal value for this interval, it saves the decision of the first task in this window and moves to the second time window, which is between the arrival time of thenext task and the end of the simulation. It applies the same principle until all tasks are decided one by one.
## 2 Formulation for Damage Identification
### Analysis Method for Damage Identification Results  
The results of damage identification can be analyzed using mean relative error (MRE), which is a metric commonly used to evaluate the accuracy and precision of an estimation method [39]. The MRE measures the average percentage difference between the identified value and the actual value, which can be calculated using the following equation:  
\[\text{MRE}= \frac{1}{N}\sum_{i=1}^{N}\left(\frac{\left|d_{i}-d_{i}^{*}\right|}{d_ {i}}\right) \tag{5}\]  
In Equation (5), \(N\) is the number of damaged elements, \(d_{i}\) is the \(i\)th actual corrosion depth, and \(d_{i}^{*}\) is the \(i\)th optimal value obtained by optimizing the objective function.
## 3 Methodology
### Image Preprocessing and Data Preparation
In the first five steps, we improved the quality of our fundus images to make the retinal blood vessels more discernible, especially the smaller ones, which are extremely difficult to segment. In the following two steps, we enlarge our database due to the pretty small original dataset (for example, the DRIVE dataset consists of only 20 images for our training phase). To do so, we create random patches from our images. We chose our patches to have the size of 48 \(\times\) 48 and be cropped each time from the processed fundus images randomly. It must be noted that the corresponding patches are made in the manual segmentation of blood vessels in images since we will later use them as labels for the supervised training phase. The size of the patches was selected after experimentation. Due to the smaller size, it is more efficient to work in patches rather than work on the entire photo given to our model. In fact, in the training phase, our proposed model has better results in distinguishing the background of images from FOV (Field of View) since more attention is paid to details and small blood vessels, which are difficult to segment. After experimental evaluation, we found that the number of patches with the highest performance is around 100.000. In Figure 3, example of the patches are illustrated. Figure 3: Random cropped patches. (**a**) Cropped patch from original fundus image after the preprocessing steps; (**b**) The corresponding cropped patch of the manual segmentation image. Figure 2: Main preprocessing step performed in the context of our methodology. (**a**) Original image; (**b**) Image after grayscale conversion; (**c**) Image after Erosion Morphological operation; (**d**) Image after Histogram Equalization operation. The last step in our preprocessing phase is the data augmentation technique, which is used to create artificial variations on the existing images to augment the size of our data. To be more specific, data augmentation generates new and unique images from the existing dataset using transformation techniques such as zooming or rotating the existing images. Convolutional Neural Networks (CNNs) require a significant number of images to train the model effectively. Data augmentation helps our model to outperform and reduce the chance of overfitting. In the previous step, we created 100.000 random patches from the eye fundus images, and, in this step, we increased the total number of our dataset to 200.000 patches in total, which significantly improves the metrics that we use to evaluate the performance of our model, such as accuracy under the curve, global accuracy, specificity, precision and others. The size of the patches that we use is 48 \(\times\) 48.
## 3 Results
### Optimization of the MSPE Parameters
#### 3.2.2 Optimization of the Desorption Process  
The desorption solvent is crucial and it can significantly affect the MSPE efficiency. Three different solvents were investigated as desorption solvents to investigate their effect on the extraction efficiency: acetonitrile, acetone, and n-hexane. Figure 5A shows that the extraction recoveries of the target analytes eluted by acetonitrile are better than those with the other solvents. Therefore, acetonitrile was used as the desorption solvent for the following experiments.  
The desorption time is another important factor that influences the efficiency of the MSPE process. To investigate the influence of the desorption time on the MSPE efficiency, experiments were performed with ultrasonic desorption times of 2, 5, 10, and 15 min. As shown in Figure 5B, the extraction recoveries of the analytes are satisfactory when the sample solution is ultrasonicated for 5 and 10 min. Considering the efficiency, an ultrasonic desorption time of 5 min was chosen for the subsequent experiments.  
To investigate the effect of the frequency of desorption, ultrasonic desorption of the nine OCPs was performed one to four times using 2 mL of the desorption solvent and a desorption time of 5 min.  
Figure 4: Optimization of the extraction process for 50 \(\mu\)g L\({}^{-1}\) organochlorine pesticides using zeolitic imidazolate framework based on magnetic multiwalled carbon nanotubes: (**A**) amount of sorbent and (**B**) extraction time. Desorption conditions: 2 mL of acetonitrile; ultrasonication time, 5 min; and ultrasonication performed one more time. The error bars show the standard deviation of the mean (\(n=3\)).  
The results are shown in Figure 5C. The optimum recoveries are obtained when the analytes are eluted two times. Thus, the frequency of desorption was set to two for the following experiments.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Experiments  
As mentioned, we aimed to use a time-series technique to make a prediction of travel time along each selected road segment. We conducted three experiments to model our method. First, the time-series-based prediction techniques ARIMA, SVR, LSTM, and ConvLSTM were compared to decide which one would be the baseline. Second, self-attention was used to improve the LSTM by comparison with the original LSTM. Finally, several filter methods used to pre-process the time-series data were compared.  
In our experiment, we evaluated the performance of each model using a train-test split. In this case, the ordered TTR dataset of each road was split into third sets: a training set (60%), a validation set (20%), and a test set (20%), as shown in Figure 7a. The training set was used to train the model; the validation set was used to validate the loss to the adjusted hyperparameters during learning; the test set was used to test the performance of the trained model. In every iteration of the predictions, there were 24 continuous input data points as a sliding window size denoting 24 h, and the model used these to produce a predicted output representing the next hour. In other words, we used a travel time of 24 h to predict the following-hour travel time iteratively, as depicted in Figure 7b. To evaluate the model, we used the Mean Absolute Error (MAE) [14], as shown in Equation (6), where \(N\) is the number of test data; \(i\) is the test sample index; \(y^{(i)}\) is the actual travel time of the test sample at index \(i\); and \(y^{(i)}\) is the predicted travel time of the test sample at index \(i\). Our evaluations were reported by normalizing the MAE by 100 km. For example, if the model of road number 1, whose distance is 332 km, provides an MAE of 15, it is calculated by \(15\times 100\div 332\), giving 4.52.  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|y^{(i)}-y^{(i)}\right| \tag{6}\]  
Figure 7: **(a)** The split of the time-series data into a training set and a test set, and **(b)** an example of an input with a sliding window size of 24 and one output.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Pilot-Based Estimation  
Accurate channel estimation plays a crucial role in ensuring optimal performance in LIS systems. One of the primary challenges in these systems is channel estimation, especially in complex scenarios. Conventional pilot-based methods, especially the LS algorithm, have gained widespread adoption due to their simplicity and efficiency [7]. In this approach, the base station transmits predefined pilot symbols, which the receiver uses to estimate the channel characteristics. In LIS systems, pilot signals travel through both direct and reflected paths. The intelligent surface's reflective properties help the signals travel further [8].  
Although LS-based estimation offers advantages such as an improved Signal-to-Noise Ratio (SNR), spectral efficiency, and data throughput, it faces challenges in low SNR conditions. In such cases, the accuracy of estimation diminishes, which negatively impacts metrics such as the Bit Error Rate (BER) and throughput. To achieve optimal performance in these systems, it is crucial to meticulously design pilot sequences, adjust spacing, and allocate power appropriately. Only by addressing these factors can LIS systems achieve higher reliability, improved spectral efficiency, and effectively meet the demands of next-generation networks [9].
## 3 Results
### Molecular Ordering and Crystallinity  
The molecular ordering and crystallinity of the pentacene layers on various dielectrics (GO, GO/PS, and PS) were investigated by out-of-plane XRD and 2D-GIXD analysis. Figure 4 shows the XRD patterns of the GO film and pentacene films on the various dielectrics. As can be seen from the black and blue graphs in Figure 4, the diffraction peaks of the GO film correspond to (001) and (002) planes, and an interlayer spacing (d\({}_{001}\)) of 8.3 \(\pm\) 0.1 A, and the diffraction peaks of the pentacene layer on the PS substrate correspond to (001), (002), and (003) planes with d\({}_{001}\) of 15.5 \(\pm\) 0.1 A; these results are in good agreement with literature values [23; 24]. Diffraction peaks of only GO are observed in the XRD patterns of pentacene on the GO film; on the other hand, in the XRD patterns of pentacene on the GO/PS bilayer film, thin-film-phase diffraction peaks of pentacene are observed, as seen in Figure 4. This indicates that the pentacene film is more ordered and has more aligned crystalline on the GO/PS bilayer film than on the GO film.  
Figure 3: Analysis of dielectric properties of GO and GO/PS films: (**a**) frequency dependence of capacitance of GO and GO/PS films and (**b**) current density–electric field characteristics of GO and GO/PS films. The inset shows a schematic diagram of the metal–insulator–metal structure that induces the current density–electric field characteristics.  
Since the crystal structure of organic semiconductors affects the carrier transport properties, analysis of the crystal structures of thin-film organic semiconductors is crucial for understanding the electrical and physical properties of OFETs. Figure 5 shows 2D-GLXD scan images of the GO film and pentacene films deposited on the various dielectrics. In Figure 5a, a broad (001) plane of GO is observed in the q\({}_{x}\) direction (direction parallel to the substrate), which corresponds to the XRD result of GO in Figure 4. The 2D-GLXD patterns of pentacene in Figure 5b,c reveal that the pentacene film on the GO dielectric contains both a \"thin-film phase\" and a \"bulk phase\" in the q\({}_{x}\) and q\({}_{yy}\) directions, whereas the pentacene film on the GO/PS bilayer shows a relatively highly oriented \"thin-film phase\" in the q\({}_{x}\) and q\({}_{yy}\) directions. In the case of carrier transport in pentacene, the \(\pi\)-\(\pi\) overlap depends largely on the molecular alignment along the in-plane direction, q\({}_{yy}\), and not the out-of-plane direction, q\({}_{x}\). It has been reported that the bulk phase corresponds to a tilted molecular orientation and the poorest degree of \(\pi\)-\(\pi\) overlap [25]. Therefore, the 2D-GLXD results indicate that the pentacene grains are well-stacked with fewer mismatched and tilted crystalline domains on the GO/PS bilayer than on the GO film.
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 3 Multi-Scale Adaptive Aggregate Graph Convolutional Network
### Multi-Stream Framework  
The skeleton-based action recognition methods usually utilize the coordinate in the joint information of the input skeleton sequence. [PERSON] et al. (2015) found that the bone information connected by the joints is also very helpful for the action recognition and proposed a two-stream framework using joint and bone information. Moreover, the motion information of the joints has a potential effect on action recognition. For example, standing up and sitting down have no difference only from the joint coordinates and bone information, but it is easy to recognize by adding the motion information. In our work, the input data is mainly divided into node information, bone information and motion information.  
First, we define the original 3D coordinate set of a skeleton sequence as \(X=\left\{x\in R^{\mathbb{C}\times T\times V}\right\}\), where \(C\) represents the joint coordinates, and \(T\) and \(V\) denote the numbers of frames and joints, respectively.  
We suppose that the joint feature set is \(F=\left\{x_{t,v}\in\ R^{\mathbb{C}}|t=1,2,\ \ldots,T;v=1,2,\ \ldots,V\right\}\), where \(x_{:,t,v}\) represents the coordinates of the v-th joint in the t-th frame. Additionally, the relative coordinate set is \(S=\left\{r_{i}|i=1,2,\ldots,V\right\}\), where \(r_{i}=x_{:,i}-x_{:,i}\), and \(x_{:,v,c}\) denotes the center joint of the skeleton sequence. We concatenated these two sets into a sequence as the input of the joint information stream.  
Then we calculate the bone and the motion information based on the joint information. Additionally, these two information streams expand the matrix by filling in 0 elements to compensate for the change in dimensionality. Suppose that the bone length information set is \(L=\left\{l_{i}|i=1,2,\ldots,V\right\}\), where \(l_{i}=x_{:,i}-x_{:,i}\), denotes the neighbors of joint \(i\). The bone length angle set is \(A=\left\{a_{i}|i=1,2,\ldots,V\right\}\), and the angle \(a_{i}\) can be calculated by  
\[a_{i}=arccos\frac{x_{1}x_{2}+y_{1}y_{2}+z_{1}z_{2}}{\sqrt{\left(x_{1}^{2}+y_ {1}^{2}+z_{1}^{2}\right)\left(x_{2}^{2}+y_{2}^{2}+z_{2}^{2}\right)}} \tag{11}\]  
where \(\left\{x,y,z\right\}\) represents the joint coordinates. We concatenate these two sets into a sequence as the input of the bone information stream.  
Additionally, we suppose that the motion velocity information set \(M=\left\{m_{t}|t=1,2,\ \ldots,T\right\}\), where \(m_{t}=x_{:,t+1,:}-x_{:,t,:}\), and the motion velocity difference information set is \(D=\left\{d_{i}|t=1,2,\ \ldots,T\right\}\), where \(d_{t}=m_{t+1}-m_{t}=x_{:,t+2,:}-x_{:,t,:}\). We concatenate these two sets into a sequence as the input of the motion information stream.  
We separately input the joints, bones and motion data into our network, and we add the \(softmax\) scores of these streams to obtain the final classification result.