esa-sceva/satcom-chunk-collection huggingface dataset:
# AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
## 1 Introduction
As shown in Figure 1, low-frequency information is not always beneficial for tasks, and high-frequency information is not entirely detrimental for classification tasks. Under the two different assumptions in Figure 1, both high and low frequency information in images play a significant role in predicting image labels. Compared to the low-pass filter in HDGN, we have constructed a filter that can adaptively adjust filter properties based on different few-shot tasks. In the filtering process, this filter can utilize these adjusted filter coefficients to enhance or suppress the frequency components of the graph signal. The model can aggregate the necessary information from neighborhoods through thadaptive filtering, thereby enhancing performance on few-shot classification tasks. The main contributions of this work are summarized as follows:  
* In the process of graph construction, a BDM module is designed to explore and utilize the hidden dependency relationships within image features to obtain node representations and edge features on the graph. * After transforming graph convolution into graph signal filtering in the frequency domain through Fourier transform, we designed a filter that can adaptively adjust the filtering frequency component coefficients according to different few-shot tasks. * AFGN has excellent experimental results on the miniImageNet dataset, with improvements of 7.17% and 6.48% on the 5-way 1-shot and 5-way 5-shot tasks compared to the latest model. It also exhibits certain enhancements compared to other models on the CUB and FC100 datasets. These demonstrate the effectiveness of our model in few-shot classification tasks.
# Human Psychophysiological Activity Estimation Based on Smartphone Camera and Wearable Electronics
## 2 Related Work
### Image Recognition Techniques for Human Activity Detection  
Over the last few years, there has been a lot of research and development of methods for solving a wide range of recognition problems. Such problems include human action classification, determining position in space and time, and quality estimation of physical activity. We propose a neural network model to solve two main problems: video frames classification for pose recognition during the meditation practice as well as human behavior recognition and estimation.  
We analyze different modern approaches used for solving similar tasks. For example, OpenPose [14] is one of the most popular bottom-up approaches for multi-person human pose estimation. Based on the key points of the human body, the system determines its basic position in space. Another example is the convolutional neural network for yoga pose classification [15]. It determines the pose taken by a person during yoga, including some postures for meditation.  
The second problem is neural networks for action recognition. Several works discuss effective convolutional neural networks for action recognition [16]. Some papers include recurrent neural networks and long short-term memory [17]. The authors of the paper joined these two approaches into long-term recurrent convolutional networks [18], which is used for visual recognition and description. The authors of the paper [19] divide the network into temporal and spatial parts. In papers [20; 21], the authors discuss skeleton feature matching sequences from images, improving the accuracy of defining the action and expanding the context of the action. However, the represented approaches have been tested on video data with significant and obvious movements such as walking, swimming, etc. None of them has been tested on a motionless process such as meditation.  
Different human pose datasets exist on the Internet--for example, the MPII human pose dataset or dataset for CNN yoga pose classifier. To recognize the skeleton of the key points of a person, we use specialized datasets--for example, the COCO keypoints challenge dataset and VGG pose dataset. However, these sets should be expanded. The approach using synthetic data [22] can help with this.  
Figure 3: Blood pressure changes during meditation.  
In our work, we should be able to recognize the human behavior by video frames as well as give an estimation of his/her actions. The solution to basic actions was demonstrated by researchers in the paper [23]. However, human movements in the video are easily tracked and significant. In our case, the person is in a state of rest. This may complicate the assessment task. To determine the position of the body more accurately, researchers collect more key points of a person. For example, this approach even allowed simulating a character moving in time with a person from polygons based on key points [24]. In addition, we combine human skeleton data with three-dimensional temporal images, as presented in the paper [25].
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 7 Static Structural Analysis  
To ensure material strength, a structural analysis should be performed during the design stage of any manufacturing method, thereby minimizing design adjustments in the final product for additional manufacturing. By using physics-based simulations, the number of design-test cycles is dramatically reduced, lowering the likelihood of unexpected design errors occurring during such testing [48].  
Finite element analysis (FEA) or finite element method (FEM) is a widespread structural analysis numerical simulation technique. FEA/FEM, in essence, employs mathematics, specifically partial differential equations (PDEs), to comprehend and evaluate permissible quantities within a structure [49]. This method yields precise solutions to a given problem while also providing valuable insights into the behavior of physical quantities in the real world [50]. FEA is useful in predicting and optimizing the structural performance of components to ensure they meet design criteria and perform effectively under a variety of conditions.  
Nowadays, these simulations can be executed for performance and reliability analysis using the solid model represented in a CAx tool. Structural analyses within CAx tools are widely used to assess the strength of mechanical systems [51]. These analyses, which are required prior to the production of mechanical components, aid in the identification of factors that are not readily apparent, ensuring both the efficient use of materials and mechanical robustness. This study presents a structural analysis of the proposed multi-purpose UAV during the design stage, performed using the SolidWorks Simulation CAE tool, with a particular emphasis on examining stress and displacement in the mechanical system. Stress analysis involved assessing how internal forces distribute within the material, helping identify areas prone to deformation or failure under different loads, while displacement analysis examined the movement and distortion of the structure in response to applied forces.
## 4 Experiments, Results, and Analysis
### Comparison with Other Techniques  
The performance of the proposed neural networks is compared with the following multi-output regression techniques [3]:  
1. Linear regression (LR) [34].
2. K-Neighbors regression (K-NR) [35].
3. Decision Tree regression (DTR) [36].
4. Random Forest regression (RFR) [37].
5. Gradient Boosted regression Trees (GBRT) [38].
6. Support Vector regression (SVR) [39].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Time (s)** & **FLOPs** \\ \hline DNN & 596 & 459,548 \\ S-DNN & 407 & 17,096 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training time and number of FLOPs for the DNN and S-DNN models.  
Figure 12: \(R^{2}\) score comparison for S-DNN and DNN.  
Table 6 presents \(R^{2}\) scores achieved by these techniques, along with those obtained from S-DNN and DNN. We used Bayesian hyperparameter optimization to search for optimal settings for these methods [29]. K-NR shows the lowest overall \(R^{2}\) score, indicating its relative ineffectiveness in capturing the relationships between signal parameters and the distribution parameters. Except for K-NR, the techniques demonstrated strong predictive capabilities for the \(s\) parameter, with the lowest \(R^{2}\) of 0.6994 achieved by LR.  
The shape parameters \(a\) and \(b\) proved more challenging to estimate, requiring the use of more complex models like RFR and GBRT. While ensemble methods performed comparably to the proposed neural networks for parameter \(s\), the neural networks displayed superior performance in estimating the shape parameters. This is critical, as the estimation of shape parameters significantly impacts the prediction of composite signal peak amplitudes.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 3 Methodology
### Cascaded Neural Network Implementation of Proposed Framework
* RSSI-only: In contrast, an RSSI-only neural network was trained on \(\vec{r}_{i}\) to produce the location labels \(Y\) (only the neural on the right in Figure 4, with \(X_{k}\) removed in the input vector).
###### Abstract  
This paper presents a circularly polarized (CP) microstrip antenna with dual circular polarization that is fed using a 3 dB 90\({}^{\circ}\) hybrid coupler to ensure dual-CP radiation. The proximity-coupled structure is used as a feeding technique to obtain a larger operational bandwidth that covers the standard LTE 43 of the 5G mid-band applications (3.6-3.8 GHz). The antenna was fabricated on an FR4 dielectric substrate with overall dimensions of 66.1 \(\times\) 35 \(\times\) 1.6 mm\({}^{3}\). A circular patch was formed as a radiating element, with four identical slits that were etched on the patch to guarantee the presence of CP radiation at the required mid-band of the 5G frequency range. The measured impedance bandwidth (BW) of this antenna was 10.6% (3.46-3.85 GHz), while the 3 dB axial ratio bandwidth (ARBW) was 8.2% along the range (3.50-3.80 GHz). The measured average gain was about 1.3 dBi along the antenna impedance bandwidth.  
3 4B hybrid coupler; dual circular polarization; proximity coupled; axial ratio Article
## 2 Materials and Methods
### Policy Gradient-Based Deep Reinforcement Learning
#### 2.3.1 Policy Network  
A policy function, frequently symbolized with \(\pi\), is a central concept in RL that specifies the decision-making strategy of an agent in a given environment. The agent chooses an action for a given state based on the probability distribution described by the policy. Mathematically, a policy can be expressed as a mapping function \(\pi:S\to P(A)\), where \(P(A)\) is the probability distribution over the action space \(A\), and \(S\) is the state space.  
CNNs can be used as function approximators to represent the policy of an agent by mapping the observed state to a probability distribution across actions. The policy network can be expressed as \(\pi(a|s,\theta)\), where \(a\) denotes an action, \(s\) denotes a state, \(\theta\) denotes trainable neural network parameters, and \(\sum_{a\in A}\pi(a|s,\theta)=1\).  
As shown in Figure 7, a CNN-based policy extracts features from the raw input, including the status of the pending jobs and available computing resources, using a succession of  
Figure 6: Implementation of the task scheduling with a policy gradient-based DRL algorithm in the CEHIS.  
convolutional layers. Afterwards, the convolutional layers, with typically one or more fully connected layers, are employed to integrate the retrieved features and capture higher-level abstractions, resulting in a dense representation of the input.  
The final layer of the CNN-based policy is a softmax activation layer that converts the dense representation into an action space probability distribution. The softmax function guarantees that the output probabilities are non-negative and accumulate to one, allowing them to be interpreted as action probabilities. The agent then samples an action from this distribution to instruct the scheduler on how to choose a node for the waiting tasks.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 2 Methods
### Innovation 1: Underwater Light Propagation Model
#### 2.2.1 Principle of Underwater Light Propagation Model  
Light absorption in water varies with wavelength. The absorption of light can be described by the Beer-Lambert law, which models how light is absorbed as it travels through a medium [32] (in this case, water):  
\[I(\lambda,z)=I_{0}(\lambda)\cdot e^{-\alpha(\lambda)\cdot z} \tag{1}\]  
where \(I(\lambda,z)\) is the intensity of light at wavelength \(\lambda\) at depth \(z\), \(I_{0}(\lambda)\) is the initial light intensity at the surface (depth \(z=0\)), \(\alpha(\lambda)\) is the absorption coefficient at wavelength \(\lambda\), and \(z\) is the depth in the water. The absorption coefficient \((\lambda)\) increases with the wavelength, meaning longer wavelengths (red, yellow, and orange) are absorbed faster than shorter wavelengths (blue and green). This causes the characteristic blue-green  
Figure 3: Underwater light propagation process.  
tint seen in underwater images, as shorter wavelengths are less absorbed and scatter more. This models wavelength-dependent light absorption, explaining why red light (long wavelength, ~650 nm) disappears within 5 m depth while blue light (short wavelength, ~450 nm) penetrates >20 m in clear water. The absorption coefficient \(\alpha(\lambda)\) determines how quickly different colors vanish with depth.  
Light scattering in water occurs when light interacts with particles, plankton, or other suspended matter. The scattering effect spreads light in multiple directions and is governed by the scattering coefficient \(\beta(\lambda)\). The intensity of scattered light can be approximated using the Henyey-Greenstein phase function \(P(\theta)\), which models the angular distribution of scattered light:  
\[P(\theta)=\frac{1}{4\pi}\cdot\frac{1-\ g^{2}}{\left(1+\ g^{2}-2g\cdot\text{ cos}\theta\right)^{\frac{3}{2}}} \tag{2}\]  
where \(g\) is the asymmetry factor, describing the average direction of scattering (positive for forward scattering, negative for backward scattering), and \(\theta\) is the scattering angle. This phase function describes how light scatters at different angles \(\theta\) when hitting particles. The asymmetry factor g (\(-1\leq g\leq 1\)) determines direction: \(g>0\) (forward scattering in clear water), \(g<0\) (backward scattering in turbid water). The amount of scattered light is dependent on the water turbidity and the concentration of suspended particles. Higher turbidity increases scattering and decreases image contrast.
## 4 Results and Discussion
### Determination of the Design Compaction Level
#### 4.1.3 Determination of Compaction Level  
The formulas in Table 3 were used to back calculate the specimen heights between compaction cycle 1 and 160. The heights were retained until 0.1 mm. According to the definition of locking point shown in Figure 2, the locking points of the two aggregates under different moisture contents are shown in Table 4.  
According to Table 4, the design gyratory compaction number N\({}_{\text{design}}\) was approximately taken to be 105. It can be found that the locking points of Aggregate A were generally equal to those of Aggregate B, indicating that N\({}_{\text{design}}\) is applicable to different CTAMs that have different aggregate gradations and moisture contents.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type of Mixture**} & \multicolumn{2}{c}{**Aggregate A under Moisture**} & \multicolumn{2}{c}{**Aggregate B under Moisture**} \\  & \multicolumn{2}{c}{**Content of**} & \multicolumn{2}{c}{**Content of**} \\ \cline{2-6}  & **4.4\%** & **4.9\%** & **5.4\%** & **6.5\%** & **7.0\%** & **7.5\%** \\ \hline Compaction number & 103 & 104 & 107 & 105 & 103 & 106 \\ Average compaction number & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Locking points under different moisture contents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Moisture Content/\%**} & \multicolumn{2}{c}{**Fitting Formula**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \hline \multirow{3}{*}{Aggregate A} & 4.4 & \(y=-4.6339\)Ln(\(x\)) + 137.67 & 0.9993 \\  & 4.9 & \(y=-4.8936\)Ln(\(x\)) + 137.43 & 0.9998 \\  & 5.4 & \(y=-4.971\)Ln(\(x\)) + 138.61 & 0.9986 \\ \hline \multirow{3}{*}{Aggregate B} & 6.5 & \(y=-4.7127\)Ln(\(x\)) + 130.82 & 0.9973 \\  & 7.0 & \(y=-4.6543\)Ln(\(x\)) + 125.97 & 0.9981 \\ \cline{1-1}  & 7.5 & \(y=-4.7429\)Ln(\(x\)) + 135.26 & 0.9998 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting results of compaction curves.  
Figure 7: Relationship between compaction number and specimen height for (**a**) Aggregate A under a moisture content of 4.4%, 4.9% and 5.4% and (**b**) Aggregate B under a moisture content of 6.5%, 7.0% and 7.5%.
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 2 Related Work  
### Data-Driven Temperature Forecasting  
Researchers have performed data-driven temperature forecasting for a few decades, and there are two main research directions: time-series analysis based on ARIMA and machine learning algorithms based on neural network models. For the former, ARIMA is a linear statistical technique for analyzing and forecasting time series. The key components of the model are auto-regressive, integrated, and moving average. Briefly, the integration component makes data stationary through differentiating,and the auto-regressive component uses the dependency between some number of lagged data and the moving-average accounts for previous error terms. For the latter temperature prediction research direction, neural network models have been the most actively studied of the machine learning algorithms for forecasting time series. Neural network models have strong power to solve many kinds of nonlinear problems and can capture the relationships between input and target data. Furthermore, they are applicable even when the units of data are different, whereas traditional methods require the same units of data.  
Researchers have mainly studied ARIMA models for forecasting temperatures for longer time units, such as monthly and annual. For instance, one ARIMA model was used to forecast monthly temperatures for Assam in India, specifically the variant of ARIMA called seasonal autoregressive integrated moving average to reflect the seasonality [15]. In another study, an ARIMA model was used to analyze annual rainfall and maximum temperature and forecast for watershed and agricultural management [17]. Researchers also used the variant of ARIMA known as autoregressive fractionally integrated moving average to forecast monthly temperature and precipitation time series [16].  
Meanwhile, researchers have used neural networks to forecast temperatures for shorter time units such as hourly and daily, including to forecast hourly temperature for load forecasting; these authors first forecast daily temperatures to predict daily temperature trends and used these to forecast hourly temperatures [18]. In another study, researchers developed an ensemble of neural network models to forecast various weather factors such as hourly temperature wind speed, and relative humidity [9], and others combined ARIMA and neural network models to forecast hourly temperatures [4]. These authors first used ARIMA to forecast temperatures and used the forecast values as inputs in the neural network for stable and accurate forecasts.  
Most researchers to date have used the same time units for both input and target data, but neural network models are versatile and can accommodate different input and target data units. Therefore, it is possible to employ more specific input data than target data for better forecasting performance. Better forecasting requires the capacity to process abundant and specific data efficiently and effectively with proper neural network model architectures.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
### Language vs. Script  
As we work on the Arabic OCR system, getting basic information about Arabic is essential. For this purpose, the concepts of language, script, and writing styles are crucial. Thus, language refers to the communication system humans use, which includes the grammar, vocabulary, and pronunciation used to convey meaning. On the other hand, the script refers to the written representation of a language, such as an alphabet or letters used to write words and sentences. A language can be written in multiple scripts and can use a script to write various languages. For example, the English letters are written in Latin script, while the Arabic language can be written in Arabic. In Arabic, the most-used styles are Naskh and Nastaleeq. Script similarities can be used to compensate for lack of availability of large amounts of training data for deep-learning-based OCR models [6].  
In [5], the authors explain the basics of the Arabic language, i.e., Arabic is written from right to left and from top to bottom. It has 28 letters, which include three vowels, i.e.,,, and. These letters change their shapes according to their usage in different words. Upper and lower case annotation does not exist. A total of 15 letters out of 28 have a point or dot above or under the letter. Arabic letters are connected from the right or left sides or both sides. However, six letters cannot be connected to their successors in a word; those letters are,,. There is another term called Tanween, which produces sound at the end of the words; these symbols are,,. in written form. Punctuation marks have their own format in Arabic; e.g., the question mark in English is written as '?', but its shape in Arabic is '%'.