esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Validation
### Parameterization Results
#### 3.3.1 Effect of the Beam Curvature on the Transmission Force and Deformation  
The contour plots shown in Figure 14 compare the deflection obtained by means of the thermal load (left) and the pin forces (right) for a curvature of 12 m\({}^{-1}\). The evolution of the \(k\) and \(k_{f}\) factors defined in (11) and (12) and the % ratio between the radial and tangential pin forces are plotted in Figures 15-17. Finally, in Figure 18, the axial strain transmitted to the beam over the piezoelectric free strain, \(\Lambda\), is plotted against the curvature.  
A linear behavior of the transmission \(k\) factor can be observed up to a curvature of 15 m\({}^{-1}\). The advantage of this is that the curvature effect may be handled just through a corrective factor before introducing the adhesive layer effect. As the curvature increases, the pin forces ratio also increases, making necessary larger radial forces to balance tangential components with smaller relative angles. Additionally, a similar trend can be observed for the beam axial strain over the piezoelectric free strain. Finally, \(k_{f}\) seems to not be remarkably affected by the curvature. This factor remains practically constant around a value of 0.0575 for the parameters investigated in the next sections. For this reason, it is not reported.  
Figure 13: Dynamic responses with a FRF analysis of the outer and inner sensors, experimental data (red and blue), and numerical data with expanding piezo forces (solid magenta and cyan) and pin forces (dashed magenta and cyan).  
Figure 14: Comparison between the deflection for the 12 m\({}^{-1}\) curvature, obtained with the thermal load (**left**) and pin forces (**right**).  
Figure 15: The \(k\) factor vs. the curvature.  
Figure 16: The \(k_{f}\) factor vs. the curvature.  
Figure 17: The Fr/Fit percentage vs. the curvature.
# The Machine Vision Dial Automatic Drawing System--Based on CAXA Secondary Development
## 1 Introduction  
### Research Background and Significance  
High-voltage appliances often use sulfur hexafluoride (SF6), a synthetic gas with excellent arc-stopping capabilities and chemical stability [1]. SF6 gas density is monitored using an SF6 density controller in the gas chamber of SF6 high-voltage electrical equipment. The accuracy of the meter is crucial for the safety of high-voltage electrical equipment. During the manufacturing process of SF6 density controllers, the current index value of the dial pointer may not match the current pressure due to material differences in the internal structure of the product, friction between the gears, errors during the assembly process, and other factors. To correct this deviation, a dial that matches the current product must be reprinted using the standard dial as a base. The current manufacturing process uses manual visual inspection to check the pointer value of the dial, and the CAD drawing of the standard dial is adjusted based on the deviation value is recorded. However, this process has low efficiency and a high error rate due to the inherent limitations of the human eye. Therefore, a high-performance, high-accuracy SF6 density controller dial automatic rendering system is urgently needed.  
Based on actual production conditions, enterprises have proposed the following requirements for the device:  
1. The device should be designed with the MKZB-IP SF6 density controller product and should be easy to install, have a small equipment volume, be easy to operate, have a high degree of automation, and have high safety performance.
2. The device should be easily adaptable to detect different types of products.  
Machine vision technology has become an essential tool for product detection and has been widely applied in various fields through years of development. In this paper, we have designed and built a set of SF6 density controller dial automatic drawing systems based on the requirements of enterprises. Through a thorough understanding of the product's performance and usage, we have utilized machine vision technology to construct an equipment work platform and design a dial pointer recognition algorithm and automatic drawing system. This has significantly improved the efficiency and accuracy of product testing, solving the current issue of not being able to inspect products in large quantities.
# A Video-Based Fire Detection Using Deep Learning Models
## 2 Related Work  
### Computer Vision-Based Fire Detection  
In conventional fire detection, much research has continuously focused on finding out the salient features of fire images. [PERSON] [7] analyzed the changes of fire using an RGB and HSI color model based on the difference between consecutive frames and proposed a rule-based approach for fire decision. [PERSON] and [PERSON] [5] proposed a generic rule-based flame pixel classification using the YCbCr color model to separate chrominance components from luminance ones. In addition, [PERSON] [8] extracted the candidate fire area in an image using an HSI color model and calculated the dispersion of the flame color to determine the fire area. However, color-based fire detection methods are generally vulnerable to a variety of environmental factors such as lighting and shadow.  
[PERSON] and [PERSON] [9] adopted the Bayes classifier to detect fires based on additional features such as the area, surface, and boundary of the fire area to color. [PERSON] [10] proposed the neural network-based fire detection method using optical flow for the fire area. In the method, two optical flow models are combined to distinguish between fire and dynamically moving objects. In addition, [PERSON] [11] proposed a multi-expert system which combines the analysis results of a fire's color, shape, and motion characteristics. Although insufficient, the supplementary features to color, including texture, shape, and optical flow, can reduce the false detections.  
Nevertheless, these approaches require domain knowledge of fires in captured images essential to explore hand-crafted features and cannot reflect the information spatially and temporally involved in fire environments well. In addition, almost all methods using the conventional approach only use a still image or consecutive pairs of frames to detect fire. Therefore, they only consider the short-term dynamic behavior of fire, whereas a fire has a longer-term dynamic behavior.
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 3 Fundamentals of FANET
### Optimal FANET Design  
Many researchers are trying to establish an optimal solution for FANET, which is more adaptable to any situation and more scalable to any extend. We discuss some optimal conditions that many researchers are trying to achieve. Moreover, we discuss the advantages of using RL over conventional methods in FANET.  
As discussed earlier, FANETs have unpredictable nature owing to their high mobility and speed. The flying routes may vary from UAV to UAV in a multi-UAV system, depending on the operation requirements. More UAVs can join an ongoing operation to complete the task faster. UAVs also may fail owing to any technical problems or any environmental issues. There are so many variables in FANET environment that needs to be addressed. Thus, the optimal design should be more adaptive, super-fast, highly scalable, energy-efficient, more stable, and highly secure.  
To achieve these features, there is no alternative to RL owing to its self-learning capability and energy efficiency. The conventional methods of selecting routing paths and flying trajectories are energy inefficient and slow. Moreover, these are not self-learning methods. To make the design solutions more adaptive and scalable, UAVs should learn to make their own decision based on the current situation. To establish self-learning design solutions, researchers have started using RL. Furthermore, many other problems, such as autonomous charging, jamming protection, relaying, localization, and fault handling, can be addressed using RL.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 3 DC Motor Speed Consensus Control
### Time Varying Reference  
When a time varying reference is considered, it is necessary to include additional controls in all the agents besides the leader  
\[\dot{\xi}_{1} = k_{p}  
where  
\[u_{1} =\,k_{l}\left(\xi^{d}-\xi_{2}\right)+\dot{\xi}^{d} \tag{21a}\] \[u_{2} =\,\dot{\xi}_{3}\] (21b) \[u_{3} =\,\dot{\xi}_{1} \tag{21c}\]  
by substituting (21) in (20), we obtain  
\[\dot{\xi}_{1} =\,k_{p}\left(\xi_{2}-\xi_{1}\right)+\,k_{l}\left(\xi^{d}-\xi_{2} \right)+\dot{\xi}^{d} \tag{22a}\] \[\dot{\xi}_{2} =\,k_{p}\left(\xi_{3}-\xi_{2}\right)+\,\dot{\xi}_{3}\] (22b) \[\dot{\xi}_{3} =\,k_{p}\left(\xi_{1}-\xi_{3}\right)+\,\dot{\xi}_{1} \tag{22c}\]  
again, by adding (22a), (22b), and (22c), we obtain  
\[\dot{\xi}_{2}-\dot{\xi}^{d}=\,k_{l}\left(\xi^{d}-\xi_{2}\right) \tag{23}\]  
Equation (23) shows that \(\left(\xi_{2}-\xi^{d}\right)\to 0\) as \(t\rightarrow\infty\). From Equation (22b),  
\[\dot{\xi}_{2}-\dot{\xi}_{3}=\,k_{p}\left(\xi_{3}-\xi_{2}\right) \tag{24}\]  
then \(\left(\xi_{2}-\xi_{3}\right)\to 0\) as \(t\rightarrow\infty\). Finally, from (22c),  
\[\dot{\xi}_{3}-\dot{\xi}_{1}=\,k_{p}\left(\xi_{1}-\xi_{3}\right) \tag{25}\]  
so we can see that \(\left(\xi_{3}-\xi_{1}\right)\to 0\) as \(t\rightarrow\infty\).  
To generalize this procedure for \(N\) motors, modify (20) as follows:  
\[\dot{\xi}_{1} =\,\,\,\,k_{p}\left(\xi_{2}-\xi_{1}\right)+u_{1} \tag{26a}\] \[\dot{\xi}_{2} =\,\,\,\,k_{p}\left(\xi_{3}-\xi_{2}\right)+u_{2}\] (26b) \[\vdots\] \[\dot{\xi}_{N} =\,\,\,\,k_{p}\left(\xi_{1}-\xi_{N}\right)+u_{N} \tag{26c}\]  
where \(u_{1}\) is the same as in (21a), \(u_{k}=\dot{\xi}_{k+1}\) for \(k=2,\ldots,N-1\) and \(u_{N}=\xi_{1}\), considering a cyclic case.
## 2 Materials and Methods
### Assessing Expression Stability of Reference Genes  
The stability of expression of the candidate reference genes were estimated and ranked using four different statistical algorithms, geNorm, NormFinder, BestKeeper, and the delta Ct method, and a web-based analysis tool, RefFinder. GeNorm evaluates the most stable reference genes and determines the optimal number of reference genes required for normalization [31]. When the expression stability value (MV) is less than 1.5, the expression of this gene is stable [40]. NormFinder ranks candidate reference genes by calculating their stability value (SV) [32]. BestKeeper calculates the stability of the candidate genes based on the standard deviation (SD) and coefficient of variation (CV): the smaller the SD and CV values, the more suitable these genes are to be used as references [30]. The comparative delta Ct method calculates the delta Ct of the candidate reference genes, and smaller \(\Delta\)Ct means more stable gene expression [29]. Finally, we comprehensively ranked the candidate reference genes based on the geomean (GM) values of the above results from the four different statistical algorithms, using the web-based analysis tool RefFinder [33].
# Mobility Prediction of Mobile Wireless Nodes
## 3 Machine Learning Classifiers
### K-Nearest Neighbors  
The k-nearest neighbors (KNN) model is a type of supervised ML model. This algorithm is characterized by simplicity and is widely used in clustering, classification, and many data mining applications. The KNN model is a non-parametric technique that gives weights to neighbors' contributions (the nearest points have a larger effect than far-away points) by calculating the distance between test nodes and training nodes, where training nodes are the neighboring points. In general, KNN offers high performance and is fast because it does not have a separate training phase [21; 22].
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 1 Introduction
It incorporates three modules responsible for data-dependent initialization, efficient unfolding optimization, and user-specified illumination enhancement, respectively. This algorithm demonstrates impressive outcomes in terms of preserving details and suppressing noise. D2 BGAN (2019) integrates cycle consistency, geometric consistency, and illumination consistency into the training of the network model. Additionally, it employs three discriminators to individually learn the color, texture, and edges of the images. This approach ensures the alignment of structural and content aspects between input and generated images while continually enhancing image quality. This network demonstrates strong generalization capabilities across various datasets. [PERSON] et al. established a network named LACN (2019) that incorporates a hybrid attention mechanism. By stacking and fusing parameter-free attention modules, this network achieves more efficient extraction of both global and local image information, leading to a significant reduction in the loss of image details and content. Deep learning-based low-light image enhancement algorithms offer advantages such as high flexibility and wide applicability. However, the enhancement results for fine details are not always ideal. To address the various issues in existing mainstream algorithms, this paper proposes a low-light image enhancement algorithm based on deep learning and the Retinex theory. Firstly, residual connections and dilated convolutions are employed to improve the efficiency of image decomposition and reduce the loss of details during the decomposition process, resulting in the decomposition of the low-light image into an illumination map and a reflectance map. Secondly, a set of mapping functions are iteratively applied to enhance the illumination map, improving the brightness and contrast of the image. Then, a color restoration module is used to address color distortion and noise problems that may arise during the image enhancement process. Finally, subjective perception and objective metrics are used to compare the performance of different algorithms, demonstrating the effectiveness of the proposed algorithm.
# Research on Predictive Analysis Method of Building Energy Consumption Based on TCN-BiGru-Attention
###### Abstract  
Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, building energy consumption is growing rapidly. Therefore, accurate and effective building energy consumption prediction is the basis of energy conservation. Although there are currently a large number of energy consumption research methods, each method has different applicability and advantages and disadvantages. This study proposes a Time Convolution Network model based on an attention mechanism, which combines the ability of the Time Convolution Network model to capture ultra-long time series information with the ability of the BiGRU model to integrate contextual information, improve model parallelism, and reduce the risk of overfitting. In order to tune the hyperparameters in the structure of this prediction model, such as the learning rate, the size of the convolutional kernel, and the number of recurrent units, this study chooses to use the Golden Jackal Optimization Algorithm for optimization. The study shows that this optimized model has better accuracy than models such as LSTM, SVM, and CNN.  
energy consumption prediction; temporal convolutional network (TCN); BiGRU; attention; golden jackal optimization
# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 5 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Comment** & **Mitigation Strategy** \\ \hline Generalization Ability & Models like Med-PaLM and Med-Gemini may perform well on datasets similar to those they were trained on but struggle with data from different demographics or healthcare systems. & Validation across diverse datasets using techniques like domain adaptation [81] or federated learning to enhance their adaptability \\ \hline Computational Resource Demands & LLMs require substantial computational resources, limiting their feasibility in low-resource settings or smaller healthcare institutions. & Adoption of methods like model distillation and quantization to reduce computational overhead while maintaining accuracy. \\ \hline Explainability and Black-Box Models & Many models lack transparency, making it difficult for clinicians to trust and interpret AI-generated decisions. & [PERSON] and [PERSON] examine the role of explainability in health AI tools, emphasizing that effective decisions should align with care objectives and be legally defensible. They argue that clinicians must consider factors beyond AI explainability and accuracy, such as ethical accountability and practical applicability. Through case examples, they demonstrate that ethical and legally sound decisions can be achieved even in the absence of explainability [97]. \\ \hline Bias and Fairness & AI models trained on biased datasets may propagate or amplify biases, potentially leading to skewed or unfair treatment recommendations. & Use of diverse datasets during training [58] and bias-mitigating techniques like the Differential Transformer [85]. \\ \hline Regulatory and Ethical Concerns & Regulatory scrutiny for AI in healthcare is increasing, necessitating compliance with ethical standards and legal regulations. & Development of ethical frameworks such as transparency and accountability [97] and engagement with regulatory bodies like FDA [99], MHRA, and EMA to align AI tools with established healthcare standards. \\ \hline Multilingual Challenges & Models face difficulty translating medical reports into resource-poor languages like Vietnamese and Tagalog, limiting accessibility and usability globally. & Use of multilingual training datasets and validation on resource-limited languages [84]. Fine-tuning models specifically on underrepresented languages and validating with culturally diverse benchmarks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Challenges and Mitigation Strategies for Applying LLMs in Healthcare. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
# Predictor Selection for Bacterial Vaginosis Diagnosis Using Decision Tree and Relief Algorithms
## Materials and Methods
### Feature Selection Algorithms
#### 2.2.2 Relief  
Relief is a multivariate filter-method for feature selection. It selects relevant features based on the differences of feature values between pairs of instances according to the nearest-neighbor algorithm, providing a score for each feature [13]. For a random instance (_Ri_), Relief searches the two nearest neighbors: one from the same class (nearest hit _H_) and the other from a different class (nearest miss _M_). It updates the quality estimation (\(W[A]\)) for all attributes \(A\) depending on their values for _Ri_, \(M\), and \(H\). If instances _Ri_ and \(H\) have different values of the attribute \(A\), then the attribute \(A\) separates two instances with the same class such that the quality estimation \(W[A]\) is decreased. On the other hand, if instances _Ri_ and \(M\) have different values of the attribute \(A\), then the attribute \(A\) separates two instances with different class values such that the quality estimation \(W[A]\) is increased. The whole process is repeated \(m\) times, where \(m\) is a user-defined parameter [13]. The pseudocode is shown in Algorithm 1.  
```
Input: a vector of attribute values and the class value for each training instance Output: the vector \(W\) of estimations of the qualities of attributes
1. set all weights \(W[A]\): = 0.0
2.for\(i\): = 1 to m do begin
3 randomly select an instance _Ri_;
4 find nearest hit \(H\) and nearest miss \(M\);
5 for\(A\): = 1 to ado
6 W[A]: = \(W[A]\) - diff(A, Ri, H)/m + diff(A, Ri, M)/m;
7. end;
```  
**Algorithm 1** Relief pseudocode  
Function _diff(A, I, I2)_ calculates the difference between the values of the attribute \(A\) for two instances \(I\)1 and _I2_. The function _diff_ is also used for calculating the distance between instances to find the nearest neighbors and it is based on the Manhattan distance [13]. The R package _FSelector_[14] provides an implementation of the _Relief_ algorithm.
## 1 Introduction
The production of renewable energy through the co-combustion of biomass and coal (referred to as classic co-combustion), which has been pursued for many years, is technologically straightforward and economically viable, provided that sufficient quantities of low-cost (resource cost + resource transportation cost) biomass are available near the energy producer [45; 46; 47; 48]. It is estimated that, with a high conversion rate, a 100-hectare biomass plantation can produce enough dry plant matter annually to power a plant with a capacity of less than 300 kW [49; 50]. However, direct co-combustion in power boilers presents several challenges [51; 52]. Increasing the share of biomass with a moisture content of 40-60% in co-combustion with coal by 1% leads to a 0.5-1% reduction in boiler efficiency [53; 54]. Transitioning from coal to mixtures of coal and alternative fuels increases the deposition of particulate residues on heat exchanger surfaces, accelerates low- and high-temperature corrosion, and raises the emission of undesirable by-products in flue gases and ash [55; 56]. In order to remove moisture from fuel and enhance its energy density, an autothermal valorisation process is conducted through thermolysis [57]. Biomass subjected to valorisation can serve as a valuable fuel for highly efficient renewable energy production technologies. Thermolysisproduces biochar, also known as biocarbon, with physicochemical properties that allow its immediate use in power boilers. The properties of torrefied biomass are significantly determined by the fuel type and the thermal treatment conditions [58; 59; 60]. In the process of obtaining biomass from willow trees, the stems are subjected to many loads related to bending, cutting and crushing. Obtaining the lowest possible energy expenditure in these processes involves knowing the properties of the plants. Knowing them makes it possible to understand the behaviour of the material in relation to different operations. Therefore, determining the effect of humidity and sampling location on the amount of cutting energy for willow stems is extremely important [61]. This study aimed to analyse the specific energy demand for cutting annual willow stems, considering variations in plant moisture content and sliding-cutting angles. The laboratory results discussed in this article were subjected to statistical analysis.