esa-sceva/satcom-chunk-collection huggingface dataset:
# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 2 Literature Survey
However, these methods fail to address significant variations in the color, scale, orientation, aspect ratio, and shape of text instances in real-life scenes. To address the issues mentioned above, our method presents a novel convolutional architecture to predict quadrilateral bounding boxes around text segments of different shapes and orientations in natural scenes. The proposed model is based on the MobileNetmodel that uses the depthwise separable convolutional operation, resulting in more computationally efficient object detectors than the convolutional network based detectors. There exists an earlier application of MobileNet for scene text detection in [59], although it was limited in exploration and experiments. We present a novel U-shaped convolutional neural architecture for scene text detection using MobileNet incorporating text attention blocks in the feature extraction stage. The text attention in the proposed network is based on the ECA module [24], which is an efficient method for implementing channel attention in convolutional networks. In contrast to other methods, such as [32; 33; 39; 40; 50], our method uses attention blocks in the convolutional architecture to improve feature extraction on text-specific attributes. The detector is trained using a novel multitasking formulation based on focal loss, which also accounts for the skewed sample distribution in scene text problems. The predictions by the detector network are processed by a novel non-maximal suppression technique to account for text expectation in predicted quadrilateral bounding boxes.
# Combined Use of MRI, fMRIand Cognitive Data for Alzheimer's Disease: Preliminary Results
## 2 Methods and Procedure
### Magnetic Mechanism of the BOLD Signal  
The BOLD signal represents the hemodynamic response of cerebral activation [36]. Several studies demonstrated that BOLD signal alterations represent a complex interplay between changes in the metabolic rate of cerebral blood volume, blood flow, and oxygen consumption rate [1; 36]. In fact, the increased neuronal activity induces modifications in local vasculature and thus causes changes in the deoxyhemoglobin (dHB) concentration [36].  
The dHB and the oxyhemoglobin present paramagnetic and diamagnetic behavior, respectively, leading to a shift of the local magnetic susceptibility [1]. In MRI, the material magnetization is directly proportional to the strength of the magnetic field [4]; hence, different magnetic responses provoke different signals in MRI. Logothetis [3] demonstrated that the dHB induces field inhomogeneity and neural activity, resulting in BOLD signal reduction.  
The importance of the temporal preprocessing step for the BOLD signal was underlined in [37]. In our study, preprocessing steps are implemented using the CONNtoolbox. This allows specifying possible temporal confounding factors, such as movement parameters and artifacts, and BOLD signals obtained from noisy ROIs, such as White Matter (WM) and Cerebrospinal Fluid (CSF). Each temporal confounding factor \(\mathrm{c}_{n}(t)\) is then regressed from the BOLD signal, called \(BOLD(v,t)\), at each voxel as follows [37]:  
\[BOLD(v,t)=BOLD*(v,t)-\sum_{n=1}^{N}a_{n}(v)\cdot c_{n}(t)-\sum_{k=1}^{K}\sum_{ n=1}^{M_{k}}b_{kn}(v)\cdot d_{kn}(t) \tag{1}\]  
where \(BOLD*(v,t)\) is the observed bold signal at voxel \(v\) and time \(t\), while \(N\) is the number of temporal confounds, and \(\mathrm{d}_{k}(t)\) are confound s from K noise ROIs, each characterized by \(\mathrm{M}_{k}\) components of the magnetization signal from each noise ROI.  
The resulting residual BOLD signals are band-pass filtered (0.001 Hz-0.01 Hz). Mathematical details are shown in [PERSON] et al. [37].
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 1 Introduction
The experimental results demonstrate that the inclusion of room-to-room transition time consistently improves localization accuracy across diverse settings, reinforcing its general applicability. In summary, the contributions of this study are listed below:  
* The general applicability of the room-to-room transition time feature was validated through experiments conducted on multiple datasets collected from different residential environments, demonstrating its robustness across varying spatial layouts and user movement patterns. * A diverse set of ML algorithms, including Ensemble methods and Neural Networks, was employed to systematically evaluate the impact of the proposed feature on indoor localization performance. * An ESP32-based data-collection system has been developed to automate the acquisition of Wi-Fi signal fingerprints, streamlining the data-collection process and enhancing consistency in training data. * A set of high-quality datasets, incorporating Wi-Fi fingerprints and room-to-room transition times, is introduced to support further research in fingerprinting-based indoor localization.
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 1 Introduction
A hierarchical spike-and-slab prior is explored to model the pattern of the group sparsity of multipath propagations. We develop a spike-and-slab clustered prior to model the relationship of spatial pixels. The proposed algorithm can estimate the prior parameters without supervision and successfully achieve imaging without any information on targets, such as sizes, shapes, and numbers. Notations: Vectors and matrixes are represented by boldface lower-case and upper-case letters, respectively. The probability density function (pdf) is denoted by \(p(\cdot)\). Given the random variable \(x\), the Gaussian distribution is denoted by \(\mathcal{N}(x|a,b)\) with mean \(a\) and variance \(b\), and the Bernoulli distribution is denoted by \(\text{Bern}(x|\pi)\) with the weight of \(\pi\). In addition, symbols \((\cdot)^{T}\) and \((\cdot)^{H}\) denote a transpose and Hermitian transpose, respectively, \(\mathbf{I}_{N}\) denotes the N-dimensional identity matrix, and \(\circ\) denotes the Hadamard product of the vectors or matrixes. For clarity, the major acronyms are collected and provided in Table 1.  
The remainder of the paper is organized as follows. The signal model in the multipath propagation is presented in Section 2, and the high-resolution TWRI problem is converted into the sparse reconstruction problem, and then the clustered BCS algorithm is developed in Section 3; subsequently, simulation and experimental results are provided in Section 4; finally, some conclusions are made in Section 5. \begin{table}
\begin{tabular}{l l} \hline \hline
**Full Name** & **Acronym** \\ \hline Through-the-wall radar imaging & TWRI \\ Aspect dependence & AD \\ Compressive sensing & CS \\ Group sparse & GS \\ Bayesian compressive sensing & BCS \\ Uniform linear array & ULA \\ Markov Chain Monte Carlo & MCMC \\ Normalized mean square error & NMSE \\ relative clutter peak & RCP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of major full names and acronyms.
## 3 Petrophysical Experiments  
A total of 31 pieces of SaPu sandwich cores were obtained, and the cores were processed into plunger-type core samples measuring 2.5 cm in diameter and 4 cm in length using a core sampler. For research purposes, the processed cores were divided into two parts. One group underwent X-ray diffraction and cation exchange capacity (CEC) measurements to determine the clay mineral types, the content, and the additional conductive capability of the clays. The other group underwent basic core parameter measurements, a T2 spectrum of nuclear magnetic resonance (NMR) measurement, and resistivity experiments [11], as shown in Figure 4.  
Utilizing the outcomes of the experimental measurements, Figure 5 displays the main core parameters. The experimentally measured core gasometric porosity generally ranged from 9.0% to 23.0%, with an average of 17.2%. The gas permeability was generally distributed between 0.1 and 100 mD, with an average of 0.5 mD, signifying a reservoir characterized by a medium-to-low porosity and permeability [12]. X-ray diffraction experiments conducted on the cores revealed predominant minerals, including quartz and feldspar, accompanied by calcite. The average clay content was determined to be 11.6%, and a relatively high proportion of mixed-layer illite was observed. The cation exchange capacity (CEC) values varied from 1 to 6 mmol/100 g, with 52% falling within the range of 4 to 6 mmol/100 g.  
Figure 3: Log review and preliminary interpretation of Well T1702.
## 4 Numerical Analyses  
### Model Setup  
The experimental tests of debris flows performed by [PERSON] et al. [53] were simulated to characterize the transformation processes of debris flows in a flume with a length of 10 m, a height of 3 m, a width of 0.6 m and a slope of 6\({}^{\circ}\). In the experimental tests, a coating of black roof shingles with a uniform roughness of approximately 1 mm was present in the flume bed [54]. The debris flow was composed of white industrial kaolin, coal slag and red flint sand with medium-sized grains. The mixed slurries had a density of approximately 1800 kg/m\({}^{3}\). Low, medium and high concentrations of clay, corresponding to 5%, 15% and 25% in regard to weight, were considered for testing [55]. Based on rheometer tests, the behaviors of the slurries were found to be characterized by the Herschel-Bulkley model (Figure 4), which can be demonstrated in the following manner:  
\[s_{\text{u,H}}=s_{\text{u0,H}}\Big{(}1+\mu\dot{\gamma}^{n}\Big{)} \tag{10}\]  
where \(S_{\text{u,H}}\) and \(S_{\text{u0,H}}\), respectively, denote the shear stress and the critical shear stress, \(\mu\) is the coefficient of viscosity and \(n\) represents an index accounting for shear thinning. The rheometer test results of the three types of slurries were best fitted with the following coefficients:  
\[5\%\text{ Clay},s_{\text{u0,H}}=7.5\text{ Pa},\,\mu=0.025,\,n=1.73.\]  
\[15\%\text{ Clay},s_{\text{u0,H}}=21.0\text{ Pa},\,\mu=0.09,\,n=1.43.\]  
\[25\%\text{ Clay},s_{\text{u0,H}}=94.2\text{ Pa},\,\mu=1.19,\,n=0.93.\]  
The 2D numerical simulation was performed in a computational domain of 22 m \(\times\) 1 m. A uniform grid of 12.5 mm \(\times\) 12.5 mm was used for meshing purposes, and the entire domain was meshed with 149,783 quadrilaterals. The initial length and thickness of the slurry were 2 m and 0.5 m, respectively. The mesh was confirmed to be sufficient to obtain accurate results through trial calculations. In the two-dimensional numerical model, the pressure inlet boundary condition and the pressure outlet boundary condition are exerted on the left wall and right wall, respectively, and the base is set as a no-slip condition.  
Figure 4: Shear strengths of slurries with different clay percentages.
# AI-Based Smart Monitoring Framework for Livestock Farms
## 1 Introduction
In the Netherlands, sensors attached to cattle collect approximately 200 MB of data per cow annually. These data are combined with external information, such as climate change, to enable farmers to continuously monitor the movements and health of their livestock. Additionally, the Smart Dairy Farming program, implemented from 2015 to 2017, achieved smart livestock farming using sensors, indicators, and decision-making models. In various applications, there are increasing cases of maximizing productivity and improving management efficiency through the use of deep learning, machine learning, and big data; ICT convergence and intelligence are essential to achieving the ultimate unmanned livestock integrated management system pursued by livestock smart farms. Smart livestock technology development should be undertaken to realize not only feeding management but also productivity improvement and farm management convenience and efficiency through data collection and data-based control systems and monitoring system automation, reflecting the functional requirements shown in Figure 1.  
In this paper, we propose an AI-based intelligent monitoring framework for livestock farms by applying the RT-DETR model as an alternative to the commonly used CNN-based YOLO models. Using collected video footage, which is already available through CCTV systems commonly installed on most cattle farms, we were able to collect a training dataset and test dataset to detect lesions and identify abnormal behaviors in Hanwoo cattle without the need for additional equipment. To ensure high recognition accuracy, the RT-DETR model was applied to construct the AI model. The objective of this study is to apply AI technologies to focus on detecting abnormal behaviors and situational awareness related to the individual maintenance behaviors of Hanwoo cattle. This differentiates this study from existing smart livestock systems, which primarily focus on environmental control and the monitoring of livestock environments. Therefore, this research is expected to enhance the efficiency of livestock management while contributing to improved productivity in the industry. The rest of this paper is organized as follows: Section 2 reviews the research on smart farm monitoring and deep learning models. Section 3 proposes a framework for a smart livestock monitoring system using AI. Section 4 describes dataset collection and preprocessing and AI model construction. Section 5 describes the experiments and the verification of the constructed model, and the conclusions and future work are presented in Section 6.
## 3 Epileptic Seizure Detection  
An essential part of the diagnosis and classification of epilepsy is the analysis of EEG signals. EEG patterns reflect the electrophysiological condition of the brain at any given time. This section provides an overview of current epilepsy detection techniques based on several methods for the classification and analysis of EEG data. The most common steps followed by researchers for epilepsy detection from EEG signals are given below:  
* Data acquisition;
* Pre-processing;
* Extracting features;
* Classification;
* Evaluation.  
EEG data are most commonly employed for the diagnosis and detection of epileptic seizures. The data are acquired by placing multiple electrodes on the scalp that record voltage fluctuations in the brain. Researchers usually use EEG data that are publicly available online.  
After data acquisition, the next step is the extraction of features which involves obtaining relevant statistical attributes from the data. These are necessary for the classification of EEG signals since they contain distinct signatures and characteristics that help segregate epileptic and non-epileptic events [37]. The accuracy of epilepsy detection depends upon the quality of features extracted. Therefore, improving the quality of feature extraction is critical. According to research, the quality of features can be improved by employing various dimensionality reduction techniques.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 6 VLC Application
### Information Displaying Signboards  
LEDs are utilized as light sources on signboards set in a specific order at airfields, bus stations, and other locations where information must be broadcasted. In venues such as airports, museums, and hospitals, signs are utilized to transfer data and can also be used for instructions [134], as shown in Figure 18.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 6 Implementation and Evaluation
### Communication Overhead  
The communication overhead described here counts only the communication information related to cross-domain authentication. There are two possible cases when a device \(D_{i}\) in domain A authenticates with a device \(D_{j}\) in domain B across domains, which we will discuss separately here. In the first case, when the authentication information of device \(D_{i}\) is stored in the domain manager in domain B, only the abstract of device \(D_{i}\) certificate needs to be queried from the blockchain and the communication overhead is only the size of the abstract of device \(D_{i}\) stored in the blockchain, which is the size of the expiration time plus the size of the certificate abstract and a status flag bit, totalling 321 bits. In the second case, when the domain manager in domain B does not save the authentication information of device \(D_{i}\), it is necessary not only to query \(D_{i}\) certificate digest from the blockchain, but also to send the certificate, certificate digest, and signature of device \(D_{i}\). The size of the certificate is 520 bits, the size of the certificate digest is 256 bits, and the signature requires 72 bits, so the final communication overhead requires 1169 bits. In our proposed authentication mechanism, the certificate expiration time is generally in days. When a device is successfully authenticated, it does not need to be authenticated again, so the communication overhead in most cases is only 321 bits, and the communication overhead is 1169 bits only when a device is authenticated for the first time or when the authentication information is lost.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
###### Abstract  
This paper deals with optimal charging versus aging minimization for lithium-ion batteries. The optimal charging strategy proposed involves charging controllers whose design relies on a battery model. The model, especially designed for automotive battery management systems applications, is recalled in this paper. It provides the voltage response of a cell to an input current. It also models side reactions that produce degradation mechanisms and thus decrease battery performance. Side reaction modelling involves taking into account the temperature cell variations, which are thus also modelled. The association of the three above-mentioned sub-models leads to an electro-thermal battery aging model used to design an optimal charging strategy that simultaneously takes into account the minimization of charging time and maximization of battery lifetime. Thus, to achieve a charging controller that manages battery health, an appropriate charging trajectory was computed by solving an optimization problem minimizing aging. Then, a charge control loop was designed. The nonlinear behavior of the battery was taken into account through the linearization of the electro-thermal aging model in different operating conditions. To take into account the resulting linear model family, the CRONE design methodology was used. The principles of this methodology are recapped and the design of the charging control loop is explained. The efficiency of the resulting charge controller is illustrated by several simulations.  
lithium-ion batteries; fast charging; battery ageing +
Footnote †: journal: Applied Sciences
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
###### Abstract  
The use of large-scale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant line-of-sight environment in a massive multiple-input multiple-output scenario, we derive analytical expressions for the sum-capacity. Then, we show that convenient simplifications on the sum-capacity expressions are possible when working at low and high signal-to-noise ratio regimes. Furthermore, in the case of low and high signal-to-noise ratio regimes, it is demonstrated that the Gamma probability density function can approximate the probability density function of the instantaneous channel sum-capacity as the number of served devices and base station antennas grows, respectively. A second important demonstration presented in this work is that a Gamma probability density function can also be used to approximate the probability density function of the summation of the channel's singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of base station antennas.  
massive MIMO; channel capacity; dense networks; outage probability Article
## 4 Discussion  
The results showed that the accuracy rates of ResNeXt+YOLO v3, Inception v3+YOLO v3, and YOLO v3 were 99.2%, 98.4%, and 96.2%, respectively, while the false alarm rates were 0%, 0.5%, and 5.8%, respectively. The times used for making predictions were 12 s, 14 s, and 21 s, respectively. The results indicated that ResNeXt+YOLO v3 performed the best and used the shortest time. This was possibly due to the YOLO v3 model learning the locations and categories of images for acquiring more information about defects and being more sensitive to defects in the images, making it easier for it to send false alarms and leading to a lower accuracy rate and higher false alarm rate. The ResNeXt+YOLO v3 model detected the standard images first, thereby decreasing the number of images needing to go through YOLO v3\({}^{\prime}\)s image detection and causing the false alarm rate of YOLO v3 to be reduced. In addition, both the ResNeXt model and the YOLO v3 model could capture more features by increasing the broadband, so a higher accuracy rate could be reached. Compared to Inception v3, ResNeXt had more flexibility in the mechanism of increasing the broadband, so it could better learn the images of defects. Therefore, the testing results showed that the ResNeXt+YOLO v3 model had a higher accuracy rate.
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
## 1 Introduction
Moreover, the integration of a fuzzy PID design controller allows for adaptive correction of control parameters to fulfill the demands for tracking accuracy amidst load disturbances. A simulation model for six-axis motor synchronous control is developed to assess the feasibility of the proposed structure and algorithm. Finally, the improved deviation coupling multi-axis synchronous control algorithm is implemented into the motion controller of a six-degrees-of-freedom robotic arm to validate its effectiveness.
# Topology Optimization Based on SA-BESO
## 3 Results and Discussion
### An Example of the L-Shaped Bracket
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SA-BESO Algorithm** & **Mean Iterative Step** & **Average Compliance of Structure** \\ \hline
1 & 31 & 37.7102 \\
2 & 30 & 37.7552 \\
3 & 31 & 37.6460 \\
4 & 33 & 37.6116 \\
5 & 32 & 37.7828 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The SA-BESO algorithm for the loaded knee structure—the optimization result data when deletion rate being 2.5%. Figure 8: Topological configurations obtained by the two algorithms. (**a**) \(C=37.8189,\ t=25\). (**b**) \(C=37.605,\ t=27\). (**c**) \(C=37.9437,\ t=27\). Figure 7: Topological configurations obtained by the two algorithms. (**a**) \(C=37.6460,\ t=31\). (**b**) \(C=37.6116,\ t=33\). (**c**) \(C=37.7607,\ t=41\). In this example, two BESO algorithms with different deletion rates were selected for the topology optimization of L-shaped scaffolds, namely, the lowest structure of compliance and minimum number of iteration steps were obtained, which were, respectively, compared with the SA-BESO algorithm. Examples show that the SA-BESO algorithm can obtain topological configurations with lower structural compliance, and its computational efficiency is no less than that of the BESO method.