esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These approaches represent model-driven channel estimators or MIMO detectors, which learn through traditional iterative structures, but the propagation of detection errors over the iterations may lead to divergence. Deep learning techniques, such as neural networks (NNs), have also been proposed for MIMO detection [29; 30; 31]. However, these data-driven models are highly dependent on the size and quality of the training dataset and are often treated as black boxes due to their end-to-end training approach. Inspired by the aforementioned approaches, [PERSON] et al. investigated hybrid-driven channel estimation in intelligent reflecting-surface-aided millimeter-wave communications with infinite-bit analog-to-digital converters (ADCs) [32]. [PERSON] et al. proposed a dual-driven learning-based channel estimator for massive MIMO systems with one-bit ADCs [33]. In summary, model-driven networks can learn from traditional iterative structures, which are sensitive to MIMO detection errors, while data-driven networks can operate as black boxes. This motivates us to explore a combination of model-driven and data-driven networks to reduce detection errors and ensure a good MIMO detection performance. In this article, we propose a dual-driven learning-based MIMO detector designed for use in mm-wave massive MIMO systems for UAV AG communications. We unfold the architecture of the model-driven OAMP-Net network and insert a data-driven symbol correction network. This data-driven network corrects detection errors, while in the model-driven network, the propagation of errors is reduced during each iteration due to the corrected symbols, ensuring the convergence and good performance of the OAMP-Net network. The key contributions of this article are as follows:  
1. We introduce a dual-driven learning-based network for massive MIMO symbol detection in UAV AG communications. The use of a data-driven network reduces detection errors, and the model-driven component is an OAMP-Net network. We design an iterative algorithm for the dual-driven network to enable joint parameter updates in both the data-driven and model-driven modules during each iteration. 2. We develop the structure of the symbol correction network, which contains fully connected layers with nonlinear activation functions to minimize detection errors. To sum up, the main contribution of this article is a dual-driven MIMO detector created by inserting a data-driven neural network into the model-driven OAMP-Net network. Our main hypothesis is that the proposed dual-driven MIMO detector will outperform the model-driven OAMP-Net and some other traditional MIMO detectors as it has the advantages of both the data-driven and model-driven networks. This hypothesis is verified in this article, which is organized as follows: Section 2 introduces the system model, the MIMO detection problem, and the proposed dual-driven MIMO detector. The simulation settings of the test experiments, the results of the chosen tests to verify the detector's performance, and discussions on whether the hypothesis is verified are presented in Section 3. Section 4 concludes this article.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 3 The Mechanical Properties of Diaphrags  
The response of the diaphragm to pressure is primarily based on the deformation theory of the diaphragm. The mechanical properties of both the FD and the CD, whether with or without initial stress, will be discussed in detail. As for the CD, two different kinds, the shallow corrugated diaphragm (SCD) and the single deeply corrugated diaphragm (SDCD), will be discussed in the coming sections, and the influence of the diaphragm structure on the overall performance of the sensor will be analyzed last.
## 3 Test Procedures for GBPT Performance Assessment  
As the new global navigation satellite systems and their applications are becoming available, the performance assessment of GBPT assumes considerable importance. The different approaches that are generally proposed for the performance assessment are presented hereafter. However, regardless of the chosen approach, the GBPT performance assessment flow might be represented by the main steps summarized in Figure 2. The first step is the definition of the scenario by means of trajectories and environmental conditions. Once the GBPT is installed on board a vehicle, the test can be executed and positioning data are saved. Both a reference trajectory (ground truth) as well as the desired GBPT outputs are recorded. Such data are used to compute the errors to assess the metrics that define the GBPT performance.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 5 Conclusions  
Numerous studies have proven that pig postures under different experimental conditions can reflect the health and welfare of pigs and predict abnormal events. Accurate and powerful automatic posture detection methods are the basis for posture analysis. Initially, sensors were used as data acquisition equipment, but they cannot meet the demand for contact-free, stress-free, and automatic operations. Then, with the advancement of image processing technologies, cameras were widely used in the design and development of automatic detection methods. Depth cameras can obtain depth information in addition to 2D data in each pixel area to generate more convictive detection results. However, 2D cameras are much more popular in pig posture detection for their low cost and convenience, and the lack of three-dimensional information can be solved by installing multiple cameras aimed in different directions. In terms of posture detection methods, the traditional machine vision pipeline generally consists of pig region selection, feature extraction, and final classification. What distinguishes deep learning methods from traditional methods is that the manually designed algorithms in these processes can be replaced by self-learning systems, particularly for feature extraction. This remarkable property is mainly ascribed to the application of deep neural networks, or more precisely, deep CNN. In the posture detection field, the Faster R-CNN and YOLO series models are the most popular deep learning models, which have advantages in detection accuracy and detection speed, respectively. In recent years, methods based on the anchor-free principle have successfully achieved the trade-off between accuracy and speed. Among various models proposed so far, some are powerful enough in certain aspects of the practical demand of pig farming. In conclusion, as a dominant developing trend, deep learning methods incorporated with image systems have achieved great success in research on pig posture detection. Although existing methods expose certain limitations on several aspects, they still show promising potential to be popularized in the commercial pig farming industry on a large scale.  
Conceptualization, Z.C. and H.W.; methodology, Z.C. and J.L.; investigation, Z.C.; resources, H.W.; data curation, J.L.; writing--original draft preparation, Z.C.; writing--review and editing, Z.C., J.L. and H.W.; supervision, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities of China (2662022 XXYJ009), the National key research and development program (2022 YFD1601903), the Hubei Province Science and Technology Major Project (2022 ABA002), and the HZAU-AGIS Cooperation Fund (SZYJY2022034).
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Preprocessing
#### 3.1.5 Upscaling  
The process of upscaling, also known as Super Resolution, involves enhancing the resolution of the image. Random forests can also be used to upscale images [65]. They use the standard benchmarks for super-resolution and present the training and evaluation accuracy. A deep learning method for upscaling binary document images using super-resolution is the generative adversarial network (SRGAN) [66], which improves readability and OCR performance compared to traditional interpolation methods, as per evaluation metrics. The method involves training a CNN on low-resolution and high-resolution binary document images to generate high-resolution images.  
Figure 6: A skewed document (on the left) is deskewed (on the right) to achieve better OCR results.
# A Simulated Annealing Algorithm for the Generalized Quadratic Assignment Problem
## 2 Mathematical Models
### Illustrative Example  
Consider a GQAP where five facilities (e.g., machines) are assigned to three locations on the plant floor of a production department located in a manufacturing plant. The unit cost per distance unit of transporting materials between each pair of facilities is 2 (i.e., \(c_{gkl}=2\) for all \(i\), \(j\), \(k\), and \(l\)). See Table 1 below for the other input data for the small illustrative example. Specifically, the amounts of materials transported between facilities \(i\) and \(j\) (\(f_{ij}\)), the costs of assigning or installing facility \(i\) to location \(k\) (\(a_{ik}\)), the distances between locations \(k\) and \(l\) (\(d_{kl}\)), the space requirement of each facility \(i\) (\(r_{i}\)), and the capacity of each location \(k\) (\(C_{k}\)) are given in Table 1.  
Using the MILP model presented above, which consists of Objective function (5) subject to Constraints (2)-(4) as well as Constraints (6) and (7), and the CPLEX 11.0 solver, the optimal solution is obtained for the illustrative example. Since the auxiliary variables, \(w_{gkl}\), were only used to linearize the GQAP model and does not provide any useful information, the values of these variables are not given. However, the nonzero values of the binary decision variables, \(x_{ik}\), are \(x_{11}=x_{21}=x_{32}=x_{43}=x_{53}=1\) where the total cost of the solution (\(z^{\star}\)) is 17,800, which is the sum of 6800 (i.e., total assignment cost) and 11,000 (i.e., total transportation cost). More specifically, facilities 1 and 2 are assigned to location 1, facility 3 to location 2, and facilities 4 and 5 to location 3 which give the minimum total cost of 17,800. See display of optimal solution in Figure 1 below. The optimal solution was obtained in 0.14 s. However, as will be discussed in Section 4, the computational results section of the paper, computational time increases exponentially as the problem size increases. Therefore, heuristic algorithms are presented next using the COP model formulation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(f_{ij}\) & **1** & **2** & **3** & **4** & **5** & \(a_{ik}\) & **1** & **2** & **3** & \(d_{kl}\) & **1** & **2** & **3** \\ \hline
1 & 0 & 100 & 50 & 20 & 0 & 1 & 1000 & 1800 & 1400 & 1 & 0 & 20 & 50 \\
2 & 0 & 0 & 100 & 0 & 0 & 2 & 1600 & 1000 & 1200 & 2 & 20 & 0 & 30 \\
3 & 0 & 0 & 0 & 50 & 0 & 3 & 1200 & 1800 & 2000 & 3 & 50 & 30 & 0 \\
4 & 0 & 0 & 0 & 0 & 100 & 4 & 2000 & 1200 & 1400 & \(C_{k}\) & 30 & 30 & 50 \\
5 & 0 & 0 & 0 & 0 & 0 & 5 & 1400 & 2000 & 1000 & \\ \hline \(r_{i}\) & 20 & 10 & 30 & 10 & 20 & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Input data for small illustrative example.
## 1 Introduction
### Our Approach and Contributions
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(p_{\text{FS}}^{t}\) & Total Power Received at the center of the cell \\ \(p_{\text{rx}}^{\text{BS}}\) & Power received from the \(i\)th BS at the center of the cell \\ \(p_{\text{rx}}\) & Transmitted power from all the BSs \\ \(H_{\text{r}}\) \(G_{i}\) & Channel and antenna gain from the \(i\)th BS to the MT \\ \(L_{i}\) & Path loss experienced by the transmitted signal from the \(i\)th BS to the MT \\ \(\theta\), \(\phi\) & Zenith and azimuth angles in the local coordinate system centered at the origin of the array \\ \(A_{E}\), \(A_{E,V}\), \(A_{E,H}(\theta,\phi)\) & Radiation pattern and its vertical and horizontal components respectively \\ \(\theta_{\text{3 dB}}\), \(\theta_{\text{3 dB}}\) & Vertical and horizontal beamwidths of the antennas in degrees \\ \(\min[x_{1},x_{2}\ldots]\) & Numerically smallest number of \(x_{k}\) \\ \(A_{m}\) & Antenna’s front-to-back ratio \\ \(SLAv_{N}\), \(SLA_{H}\) & Vertical and horizontal sidelobe attenuation levels \\ \(A_{A}(\theta,\phi)\) & Array pattern of the antenna \\ \(v_{m,n}\) & Phase shift due to the \([m,n]\) antenna element placement \\ \(w_{m,n}\) & Weighting factor due to the \([m,n]\) antenna element \\ \(N_{H}\), \(N_{V}\) & Number of horizontal and vertical antenna elements \\ \(\theta_{eilt}\), \(\phi_{\text{cosm}}\) & Zenith and azimuth electrical down-tilt steering angle \\ \(d_{\text{pr}}\), \(d_{H}\) & Vertical and horizontal antenna element spacing \\ \(\Gamma(b)\) & Gamma function \(\Gamma(b)=\int_{0}^{\infty}x^{b-1}e^{-x}dx\), \(\Re(b)>0\) \\ \(\mathbf{\Psi}_{\text{BS}}\), \(\lambda_{\text{BS}}\) & The Poisson point process and its density describing the BS distribution in the cell \\ \(\mathbb{E}_{k}[\cdot]\) & Expectation with respect to the random variable \(k\) \\ \(\lambda_{\text{MT}}\) & Density of the active MTs in the cell \\ \(\alpha\) & Emission probability of the BSs \\ \(MT_{0}\) & Mobile terminal at the center of the cell \\ \(r_{i}\) & the distance between \(\text{BS}_{i}\) and \(\text{MT}_{0}\) \\ \(\eta\) & Path loss exponent assumed constant in the whole cell \\ \(\varphi_{X}\), \(\Phi_{X}\) & Moment generating function and characteristic function of the random variable \(X\) \\ \(F_{X}(x)\) & CDF of the random variable \(X\) \\ \(\Gamma(a,x)\) & Upper incomplete gamma function \\ \(\gamma(a,x)\) & Lower incomplete gamma function \\ \(K\) & lid random variable describing the channel and antenna gains \\ \({}_{2}F_{2}(a_{1},a_{2},b_{1},b_{2},z)\) & Generalized hypergeometric function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of symbols used in antenna pattern and model derivation.
## 3 Results and Discussion
### Key Considerations  
The COVID-19 pandemic has greatly accelerated the development and implementation of health monitoring technologies. However, this swift progress has brought to light several critical issues that must be addressed to ensure effectiveness and reliability, as well as ethical implications. This study explores the main research question regarding the latest advances in elderly healthcare: what are the recent developments in technologies focused on elderly and vulnerable people's health that have been developed and integrated? Numerous studies across various domains have been examined to answer this question. While systems like SeisMote and smart mattresses demonstrate impressive capabilities in monitoring cardiovascular function and sleep metrics, questions remain about their accuracy and reliability across diverse populations and conditions. For instance, SeisMote's effectiveness in varying physiological conditions and the smart mattress's sensitivity to different body types and sleep disorders need further validation. The robustness of fall detection systems using Doppler radar and WiFi signals is also contingent on environmental factors, which may not have been thoroughly tested across different living environments. Some research focuses on the Internet of Things (IoT) and smart cities, proposing the use of networks of sensors to monitor vital signs either within the home environment or attached to the bodies of elderly individuals. Integrating multiple sensors and IoT devices in telehealthcare systems, including Kinect, with ECG, EEG, EMG, and PPG sensors, presents considerable data privacy and security challenges. Collecting and transmitting sensitive health information through various IoT nodes introduces vulnerabilities that could be exploited if not adequately protected. Implementing robust encryption and secure data handling protocols is crucial to prevent unauthorized access and the misuse of personal health information. Additional studies have focused on the design of wearable devices for the measurement of vital signs and detection of events such as falls, as well as on mobile and web applications that issue alerts and display data through intuitive, user-friendly interfaces.  
In addition, numerous studies have explored the integration of commercial wearable devices, such as smart watches or wristbands, with custom-developed mobile or web applications that employ artificial intelligence algorithms for data analysis and pattern classification. Other research focuses on developing functional health monitoring systems specifically designed for hospital environments.  
The significance of integrated technologies, such as Arduino, is also highlighted. These platforms enable the incorporation of various low-cost hardware components, thereby facilitating the creation of intelligent systems for continuous health monitoring in home settings.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology
### DMP Modelling and Incremental Learning Updating  
#### 3.3.1 Modelling of DMP  
In this session, the \(y_{d}\) is used as an example to model the DMP. This study focuses on discrete DMP represented in the Cartesian space. The Cartesian space representation exhibits advantages over the joint space representation as it allows for trajectory planning without considering the joints and their relative positions [33]. The DMP model is represented below [4; 23; 24]:  
\[\tau^{2}\ddot{y}_{d}=\alpha_{y_{d}}\left(\beta_{y_{d}}(g-y_{d})-\tau\dot{y}_{ d}\right)+Kf(\theta)-K\theta(g-y_{d0}) \tag{10}\]  
where \(y_{d}\) represents the original demonstration; \(\dot{y}_{d}\) and \(\ddot{y}_{d}\) denote the first- and second-order derivatives of the demonstration, respectively; \(\alpha_{y_{d}}\) and \(\beta_{y_{d}}\) (\(\alpha_{y_{d}}\), \(\beta_{y_{d}}>0\)) are damping and stiffness factors (normally \(\alpha_{y_{d}}=4\beta_{y_{d}}\)); \(\tau\) is the timing parameter used to adjust the timestep of the demonstration; \(y_{d0}\) and \(g\) mark the start and target of the demonstration;is a constant that is equal to \(\alpha_{y_{d}}\); \(\theta\) is the phase variable computed by the canonical system as expressed using Equation (11). It is used to calculate the value of the force item for each timestep; \(f(\theta)\) is the force item represented in Equation (12); \(\omega_{i}\) in \(f(\theta)\) is calculated by locally weighted regression (LWR) [24].  
\[\tau\dot{\vartheta}=-\alpha_{\theta}\vartheta \tag{11}\]  
\[f(\theta)=\frac{\sum_{i=1}^{N}\omega_{i}\mathrm{i}\mathrm{j}_{i}(\vartheta)} {\sum_{i=1}^{N}\psi_{i}(\vartheta)}\vartheta,\;\mathrm{\psi}_{i}(\vartheta) =\exp(-h_{i}(\vartheta-c_{i})^{2}) \tag{12}\]  
In Equations (11) and (12), \(\alpha_{\vartheta}\) is a constant, and it is always set to one; \(\tau\) is the same as that defined in Equation (10); \(N\) denotes the number of RBF kernels; \(\psi_{i}(\vartheta)\) represents the \(i\)th RBF kernel; \(c_{i}>0\) and \(h_{i}>0\) are the centre and width of the \(i\)th RBF kernel.  
For computational convenience, Equation (11) is transformed into Equation (13) to calculate \(\theta\):  
\[d\vartheta =-\tau\alpha_{\theta}\theta dt \tag{13}\] \[\vartheta =\vartheta+d\vartheta\]  
where \(dt\) is the inverse of the number of the demonstration's timestep points. When designating a new target \(g_{new}\) to replace \(g\) in Equation (10), each \(\ddot{y}_{d}\) in the generated trajectory, denoted as \(\ddot{y}_{gen_{l}}\), is calculated by using Equation (10). It is updated with the change in \(\vartheta\); \(l\) is a timestep within the generated trajectory.  
The next coordinate \(y_{gen_{l+1}}\) is calculated by using Equation (14):  
\[\dot{y}_{gen_{l+1}}\leftarrow\dot{y}_{gen_{l}}+\tau\ddot{y}_{gen_{l}}dt,\;y_{ gen_{l+1}}\gets y_{gen_{l}}+\tau\dot{y}_{gen_{l}}dt \tag{14}\]
# Autonomous Navigation of Robots: Optimization with DQN
## 3 State of Technology
### Machine Learning  
One of the latest technological advances applicable to mobile robots is autonomous learning, which is defined by several authors as the ability to teach machines to handle large amounts of information efficiently, so today many industries apply machine learning to learn from data [94].  
Different algorithms have been developed to solve data management, for which there is no single algorithm to solve a specific problem; however, the algorithm will depend on the problem to be solved, the number of variables, the model that best suits, and so on. For this article, the use of reinforcement learning with DQN will be described.  
Reinforcement learning is one of the three main paradigms of machine learning with supervised learning and unsupervised learning; this deals with how (artificial) agents should perform actions in an environment in a way that maximizes a cumulative reward. In addition, the action changes the state of the environment in some way, and the agent receives this information and its corresponding reward, that is, based on the knowledge it acquires, it decides the next action, improving its strategy in order to increase its reward [95].  
Figure 2: Turtlebot3 Burger in the plane, illustrating the robot’s kinematic configuration and coordinate system.  
This type of learning allows the mobile robot to learn from the environment while it interacts with it; the objective of applying this type of training is that it allows the agent to find the most optimal strategy through reinforcement.  
Within reinforcement learning is Q learning, which is based on a matrix of Q values that assigns the quality of the action when the environment is in a certain state; this Q value is refined through successive approximations. When the set of states and actions becomes so large, it becomes difficult to store the entire Q matrix, which is why neural networks are used to present it accurately. This is called deep Q learning [95].  
The agent's action depends on the value of epsilon \(\epsilon\), which introduces randomness to the system. If this is zero, the algorithm will never perform random trajectories and will only be based on the stored knowledge; if it is only one, it will perform random actions. Usually, this value is set very close to zero [96].  
As shown in Figure 3, the Turtlebot3 Burger agent during training takes actions \((A_{t})\) based on the current state \((S_{t})\); this gives rise to a new state called \((S_{t+1})\), and as a result of this iteration, obtains a reward \((R_{t})\). This process is repeated iteratively, looking for the accumulated rewards to increase over time.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Transformer Grouping and Fault Types
#### 3.1.3 Fault Classification  
Transformer faults were categorized into distinct classes for analysis:  
* Class A: Thermal aging
* Class B: Electrical stress
* Class C: Moisture ingress
* Class D: Contamination-related faults